id stringlengths 17 53 | domain stringclasses 5
values | category stringclasses 24
values | difficulty stringclasses 3
values | language stringclasses 10
values | problem stringlengths 29 149 | thinking_1 stringlengths 236 493 | solution_1 stringlengths 166 541 | thinking_2 stringlengths 302 467 | solution_2 stringlengths 184 565 | thinking_3 stringlengths 302 463 | solution_3 stringlengths 169 1.14k | metadata dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
merge_intervals_python_5_f520fa90 | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_5_766af517 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_5_b46964a3 | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_5_a6efb41a | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_5_b0a80d14 | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_5_a8d00722 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_5_e9c74cc9 | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_5_a3dfa51f | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_5_08567819 | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_5_f1c13eb1 | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Ensure thread-safety for concurrent access. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_6_7c3b1eea | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_6_bca7e505 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_6_d6c2543e | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_6_ad7b400b | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_6_122df5c4 | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_6_db3cd486 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_6_3ddb338d | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_6_801af87b | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_6_bf51c604 | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_6_6fd3a55d | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Add comprehensive input validation. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_7_b9285819 | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_7_98471428 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_7_f0808ec7 | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_7_47d997ce | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_7_15254844 | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_7_11e4f353 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_7_760847d9 | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_7_f86302f1 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_7_ef0dadde | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_7_1fd4665e | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Optimize for cache efficiency. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_8_960ed58a | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_8_9ee33210 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_8_7e6d82fb | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_8_87afd350 | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_8_c2c667d3 | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_8_6f084e04 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_8_4098fe53 | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_8_1aadc862 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_8_73752a8e | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_8_f58c6629 | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Use zero-based indexing throughout. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_9_3de5d5bd | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_9_0b238cb2 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_9_7c1b3221 | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_9_c68b47fc | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_9_806eaebc | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_9_5566cea7 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_9_6fba882c | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_9_51bdc3f4 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_9_1af37dc8 | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_9_760715ef | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Handle duplicate elements correctly. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_10_dfa3090a | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_10_79262806 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_10_693761ee | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_10_1a7ad755 | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_10_eff45ed4 | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_10_a377da44 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_10_2c6dc65f | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_10_38861a50 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_10_382d7020 | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_10_c1a5dbaa | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Return empty result for invalid input. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_11_0288d5e3 | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_11_ba36a7e2 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_11_53a46a77 | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_11_935a76e8 | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_11_ac937fd9 | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_11_c2ec672d | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_11_3a778d9e | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_11_1914b897 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_11_c538e4c9 | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_11_13be2b09 | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Minimize memory allocations. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_12_4c18d902 | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_12_be02b961 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_12_552541ef | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_12_48befb71 | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_12_fec3be7e | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_12_5ea40a48 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_12_ef71665a | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_12_1242be55 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_12_ad8c083f | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_12_0252f662 | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Support generic types where applicable. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_13_efff4e9f | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_13_e658059b | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_13_63327bde | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_13_f932d79a | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_13_deeeaa8c | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_13_3b74de08 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_13_1baa9f49 | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_13_5188a5e4 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_13_e142bf5c | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_13_30fc1449 | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Document time and space complexity. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_python_14_08ea213e | algorithms | arrays | medium | python | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | """ Merge Intervals - Brute Force
""" Time and space complexity depends on implementation
def merge_intervals(nums):
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | """ Merge Intervals - Sorting Greedy
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Python | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | """ Merge Intervals - Heap
""" Time and space complexity depends on implementation
def merge_intervals(nums):
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_javascript_14_6bd077c4 | algorithms | arrays | medium | javascript | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums) {
const n = nums.length;
let result = 0;
for (let i = 0; i < n; i++) {
for (let j = i; j < n; j++) {
result = Math.max(result, nums[j]);
}
}
return resul... | Approach: Sorting Greedy for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement sorting_greedy solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | Approach: Heap for Merge Intervals
Language: Javascript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums) {
// TODO: Implement heap solution
let result = 0;
for (const num of nums) {
result += num;
}
return result;
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_java_14_b3ea622d | algorithms | arrays | medium | java | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
int n = nums.length;
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = Math.max(res... | Approach: Sorting Greedy for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
... | Approach: Heap for Merge Intervals
Language: Java | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public int merge_intervals(int[] nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_cpp_14_4f56be17 | algorithms | arrays | medium | cpp | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
int n = nums.size();
int result = 0;
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
result = max(... | Approach: Sorting Greedy for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gre... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement sorting_greedy solution
int result = 0;
for (int num : nums) {
result += num;
}
return r... | Approach: Heap for Merge Intervals
Language: Cpp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
class Solution {
public:
int merge_intervals(vector<int>& nums) {
// TODO: Implement heap solution
int result = 0;
for (int num : nums) {
result += num;
}
return result;
}
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_go_14_5eafa56c | algorithms | arrays | medium | go | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space.
... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gree... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Go | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-heap ... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(nums []int) int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_rust_14_f45aecf1 | algorithms | arrays | medium | rust | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Rust | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
pub fn merge_intervals(nums: Vec<i32>) -> i32 {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_typescript_14_22fe26b9 | algorithms | arrays | medium | typescript | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-p... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Typescript | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, m... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
function merge_intervals(nums: number[]): number {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_csharp_14_76fde809 | algorithms | arrays | medium | csharp | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spa... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
... | Approach: Sorting Greedy for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass ... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Csharp | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-h... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
public class Solution {
public int MergeIntervals(int[] nums) {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_ruby_14_efaa953a | algorithms | arrays | medium | ruby | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra space... | # Merge Intervals - Brute Force
# Time and space complexity depends on implementation
def merge_intervals(nums)
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result | Approach: Sorting Greedy for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass gr... | # Merge Intervals - Sorting Greedy
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result | Approach: Heap for Merge Intervals
Language: Ruby | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-hea... | # Merge Intervals - Heap
# Time and space complexity depends on implementation
def merge_intervals(nums)
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
merge_intervals_swift_14_9406f722 | algorithms | arrays | medium | swift | Given a collection of intervals, merge all overlapping intervals. Include unit tests for verification. | Approach: Brute Force for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Start with the most straightforward approach: iterate through all possible candidates.
Step 2: Check every valid combination against the problem requirements.
Step 3: This gives higher time complexity but requires minimal extra spac... | // Merge Intervals - Brute Force
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
n = len(nums)
result = 0
for i in range(n):
for j in range(i, n):
# Process subproblem
result = max(result, nums[j])
return result
} | Approach: Sorting Greedy for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Sort items by a key criteria to enable greedy processing.
Step 2: Process items in sorted order, maintaining running state.
Step 3: Merge or combine items based on overlap/relationship.
Step 4: The sorting enables a single-pass g... | // Merge Intervals - Sorting Greedy
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement sorting_greedy solution
result = 0
for num in nums:
result += num
return result
} | Approach: Heap for Merge Intervals
Language: Swift | Difficulty: medium
Step 1: Use a heap (priority queue) for efficient min/max access.
Step 2: Heaps provide O(log N) insert/extract, O(1) peek.
Step 3: Min-heap for smallest, max-heap for largest element access.
Step 4: For median: maintain max-heap for lower, min-he... | // Merge Intervals - Heap
// Time and space complexity depends on implementation
func merge_intervals(_ nums: [Int]) -> Int {
# TODO: Implement heap solution
result = 0
for num in nums:
result += num
return result
} | {
"approach_1_type": "brute_force",
"approach_2_type": "sorting_greedy",
"approach_3_type": "heap_based",
"time_complexity_1": "O(N^2)",
"time_complexity_2": "O(N log N)",
"time_complexity_3": "O(N log K)",
"tags": [
"array",
"iteration",
"search",
"difficulty-medium",
"merge_intervals... |
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