How can I implement backtracking in my algorithm?

How can I implement backtracking in my algorithm?

Backtracking is a problem-solving technique that involves exploring all possible solutions and abandoning those that fail to satisfy the constraints. What specific problem are you trying to solve?

I'm trying to solve the N-Queens problem.

Great choice! For the N-Queens problem, you can use backtracking to place queens on the board one by one, ensuring that no two queens threaten each other. Would you like a code example?

Yes, please provide a code example in Python.

Here's a simple implementation in Python:

def is_safe(board, row, col):
    # Check this row on left side
    for i in range(col):
        if board[row][i] == 1:
            return False
    # Check upper diagonal on left side
    for i, j in zip(range(row, -1, -1), range(col, -1, -1)):
        if board[i][j] == 1:
            return False
    # Check lower diagonal on left side
    for i, j in zip(range(row, len(board)), range(col, -1, -1)):
        if board[i][j] == 1:
            return False
    return True

# Backtracking function
def solve_n_queens_util(board, col):
    if col >= len(board):
        return True
    for i in range(len(board)):
        if is_safe(board, i, col):
            board[i][col] = 1
            if solve_n_queens_util(board, col + 1):
                return True
            board[i][col] = 0  # Backtrack
    return False

# Initialize board
n = 4
board = [[0] * n for _ in range(n)]
solve_n_queens_util(board, 0)
print(board)

This code sets up the board and uses backtracking to find a solution.