data/docs/backtracking.txt

BACKTRACKING
============
Backtracking explores all possible solutions by building candidates incrementally.
If a candidate fails, it backtracks and tries the next option.

Template:
def backtrack(state, choices):
    if is_solution(state):
        record(state)
        return
    for choice in choices:
        if is_valid(state, choice):
            make_choice(state, choice)
            backtrack(state, remaining_choices)
            undo_choice(state, choice)   # BACKTRACK

--- Generate All Subsets ---
def subsets(nums):
    result = []
    def backtrack(start, current):
        result.append(current[:])   # record every state
        for i in range(start, len(nums)):
            current.append(nums[i])
            backtrack(i + 1, current)
            current.pop()           # backtrack
    backtrack(0, [])
    return result

print(subsets([1, 2, 3]))
# [[], [1], [1,2], [1,2,3], [1,3], [2], [2,3], [3]]

--- Generate All Permutations ---
def permute(nums):
    result = []
    def backtrack(current):
        if len(current) == len(nums):
            result.append(current[:])
            return
        for num in nums:
            if num not in current:
                current.append(num)
                backtrack(current)
                current.pop()   # backtrack
    backtrack([])
    return result

print(permute([1, 2, 3]))
# [[1,2,3], [1,3,2], [2,1,3], [2,3,1], [3,1,2], [3,2,1]]

--- Combination Sum ---
Find all combinations that sum to target. Numbers can be reused.

def combination_sum(candidates, target):
    result = []
    candidates.sort()
    def backtrack(start, current, remaining):
        if remaining == 0:
            result.append(current[:])
            return
        for i in range(start, len(candidates)):
            if candidates[i] > remaining:
                break
            current.append(candidates[i])
            backtrack(i, current, remaining - candidates[i])  # i (not i+1) allows reuse
            current.pop()
    backtrack(0, [], target)
    return result

print(combination_sum([2, 3, 6, 7], 7))
# [[2, 2, 3], [7]]

--- N-Queens Problem ---
Place N queens on NxN board so no two queens attack each other.

def solve_n_queens(n):
    result = []
    cols = set()
    diag1 = set()  # row - col
    diag2 = set()  # row + col

    board = [['.' for _ in range(n)] for _ in range(n)]

    def backtrack(row):
        if row == n:
            result.append([''.join(r) for r in board])
            return
        for col in range(n):
            if col in cols or (row - col) in diag1 or (row + col) in diag2:
                continue
            cols.add(col)
            diag1.add(row - col)
            diag2.add(row + col)
            board[row][col] = 'Q'
            backtrack(row + 1)
            cols.remove(col)         # backtrack
            diag1.remove(row - col)
            diag2.remove(row + col)
            board[row][col] = '.'

    backtrack(0)
    return result

solutions = solve_n_queens(4)
print(f"Number of solutions for 4-Queens: {len(solutions)}")  # 2
for sol in solutions:
    for row in sol:
        print(row)
    print()

--- Sudoku Solver ---
Fill 9x9 grid so each row, column, and 3x3 box has digits 1-9.

def solve_sudoku(board):
    def is_valid(board, row, col, num):
        num = str(num)
        if num in board[row]:
            return False
        if num in [board[r][col] for r in range(9)]:
            return False
        box_r, box_c = 3 * (row // 3), 3 * (col // 3)
        for r in range(box_r, box_r + 3):
            for c in range(box_c, box_c + 3):
                if board[r][c] == num:
                    return False
        return True

    def backtrack():
        for r in range(9):
            for c in range(9):
                if board[r][c] == '.':
                    for num in range(1, 10):
                        if is_valid(board, r, c, num):
                            board[r][c] = str(num)
                            if backtrack():
                                return True
                            board[r][c] = '.'  # backtrack
                    return False
        return True

    backtrack()

--- Word Search in Grid ---
Find if word exists in 2D grid by moving up/down/left/right.

def word_search(board, word):
    rows, cols = len(board), len(board[0])

    def backtrack(r, c, index):
        if index == len(word):
            return True
        if r < 0 or r >= rows or c < 0 or c >= cols:
            return False
        if board[r][c] != word[index]:
            return False
        temp = board[r][c]
        board[r][c] = '#'  # mark visited
        found = (backtrack(r+1, c, index+1) or
                 backtrack(r-1, c, index+1) or
                 backtrack(r, c+1, index+1) or
                 backtrack(r, c-1, index+1))
        board[r][c] = temp  # backtrack (restore)
        return found

    for r in range(rows):
        for c in range(cols):
            if backtrack(r, c, 0):
                return True
    return False

board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]]
print(word_search(board, "ABCCED"))  # True
print(word_search(board, "SEE"))     # True
print(word_search(board, "ABCB"))    # False

--- Rat in a Maze ---
Find path from (0,0) to (n-1,n-1) in a grid (1=open, 0=blocked).

def rat_in_maze(maze):
    n = len(maze)
    solution = [[0]*n for _ in range(n)]

    def is_safe(r, c):
        return 0 <= r < n and 0 <= c < n and maze[r][c] == 1

    def solve(r, c):
        if r == n-1 and c == n-1:
            solution[r][c] = 1
            return True
        if is_safe(r, c):
            solution[r][c] = 1
            if solve(r+1, c): return True
            if solve(r, c+1): return True
            solution[r][c] = 0  # backtrack
            return False
        return False

    if solve(0, 0):
        return solution
    return []

maze = [[1,0,0,0],[1,1,0,1],[0,1,0,0],[1,1,1,1]]
path = rat_in_maze(maze)
for row in path:
    print(row)