DYNAMIC PROGRAMMING (DP)
========================
DP solves problems by breaking them into overlapping subproblems and storing results to avoid recomputation.

Two approaches:
- Top-Down (Memoization): recursion + cache
- Bottom-Up (Tabulation): fill a table iteratively

Key properties needed:
1. Optimal substructure: optimal solution built from optimal subproblems.
2. Overlapping subproblems: same subproblem solved multiple times.

--- Fibonacci Number ---
Classic DP example. fib(n) = fib(n-1) + fib(n-2)

# Naive recursion - O(2^n)
def fib_naive(n):
    if n <= 1:
        return n
    return fib_naive(n-1) + fib_naive(n-2)

# Memoization - O(n)
def fib_memo(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fib_memo(n-1, memo) + fib_memo(n-2, memo)
    return memo[n]

# Tabulation - O(n)
def fib_dp(n):
    if n <= 1:
        return n
    dp = [0] * (n + 1)
    dp[1] = 1
    for i in range(2, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

print(fib_dp(10))   # Output: 55
print(fib_dp(20))   # Output: 6765

--- 0/1 Knapsack Problem ---
Given items with weights and values, maximize value in a bag of capacity W.
Each item can be taken once (0/1). Time: O(n*W). Space: O(n*W).

def knapsack(weights, values, capacity):
    n = len(weights)
    dp = [[0] * (capacity + 1) for _ in range(n + 1)]
    for i in range(1, n + 1):
        for w in range(capacity + 1):
            # Don't take item i
            dp[i][w] = dp[i-1][w]
            # Take item i if it fits
            if weights[i-1] <= w:
                dp[i][w] = max(dp[i][w], dp[i-1][w - weights[i-1]] + values[i-1])
    return dp[n][capacity]

weights = [1, 2, 3, 5]
values  = [1, 6, 10, 16]
capacity = 7
print(knapsack(weights, values, capacity))  # Output: 22

--- Longest Common Subsequence (LCS) ---
Find length of longest subsequence present in both strings.
Time: O(m*n). Space: O(m*n).

def lcs(s1, s2):
    m, n = len(s1), len(s2)
    dp = [[0] * (n + 1) for _ in range(m + 1)]
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if s1[i-1] == s2[j-1]:
                dp[i][j] = dp[i-1][j-1] + 1
            else:
                dp[i][j] = max(dp[i-1][j], dp[i][j-1])
    return dp[m][n]

print(lcs("ABCBDAB", "BDCABA"))  # Output: 4 (BCBA)

--- Longest Increasing Subsequence (LIS) ---
Find length of longest strictly increasing subsequence.
Time: O(n^2). Space: O(n).

def lis(arr):
    n = len(arr)
    dp = [1] * n
    for i in range(1, n):
        for j in range(i):
            if arr[j] < arr[i]:
                dp[i] = max(dp[i], dp[j] + 1)
    return max(dp)

print(lis([10, 9, 2, 5, 3, 7, 101, 18]))  # Output: 4 (2,3,7,101)

--- Coin Change Problem ---
Find minimum number of coins to make amount. Coins can be reused.
Time: O(amount * len(coins)). Space: O(amount).

def coin_change(coins, amount):
    dp = [float('inf')] * (amount + 1)
    dp[0] = 0
    for i in range(1, amount + 1):
        for coin in coins:
            if coin <= i:
                dp[i] = min(dp[i], dp[i - coin] + 1)
    return dp[amount] if dp[amount] != float('inf') else -1

print(coin_change([1, 5, 6, 9], 11))  # Output: 2 (coins 5+6)
print(coin_change([2], 3))            # Output: -1 (impossible)

--- Subset Sum Problem ---
Check if any subset of array sums to target.
Time: O(n * target). Space: O(n * target).

def subset_sum(arr, target):
    n = len(arr)
    dp = [[False] * (target + 1) for _ in range(n + 1)]
    for i in range(n + 1):
        dp[i][0] = True  # sum 0 always possible
    for i in range(1, n + 1):
        for j in range(1, target + 1):
            dp[i][j] = dp[i-1][j]
            if arr[i-1] <= j:
                dp[i][j] = dp[i][j] or dp[i-1][j - arr[i-1]]
    return dp[n][target]

print(subset_sum([3, 34, 4, 12, 5, 2], 9))  # Output: True (4+5)
print(subset_sum([3, 34, 4, 12, 5, 2], 30)) # Output: False

--- Matrix Chain Multiplication ---
Find minimum operations to multiply a chain of matrices.
Time: O(n^3). Space: O(n^2).

def matrix_chain(dims):
    n = len(dims) - 1
    dp = [[0] * n for _ in range(n)]
    for length in range(2, n + 1):
        for i in range(n - length + 1):
            j = i + length - 1
            dp[i][j] = float('inf')
            for k in range(i, j):
                cost = dp[i][k] + dp[k+1][j] + dims[i]*dims[k+1]*dims[j+1]
                dp[i][j] = min(dp[i][j], cost)
    return dp[0][n-1]

dims = [40, 20, 30, 10, 30]
print(matrix_chain(dims))  # Output: 26000

--- Edit Distance (Levenshtein) ---
Minimum operations (insert, delete, replace) to convert one string to another.
Time: O(m*n). Space: O(m*n).

def edit_distance(s1, s2):
    m, n = len(s1), len(s2)
    dp = [[0] * (n + 1) for _ in range(m + 1)]
    for i in range(m + 1):
        dp[i][0] = i
    for j in range(n + 1):
        dp[0][j] = j
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if s1[i-1] == s2[j-1]:
                dp[i][j] = dp[i-1][j-1]
            else:
                dp[i][j] = 1 + min(dp[i-1][j],    # delete
                                   dp[i][j-1],    # insert
                                   dp[i-1][j-1])  # replace
    return dp[m][n]

print(edit_distance("horse", "ros"))   # Output: 3
print(edit_distance("intention", "execution"))  # Output: 5