How can I implement backtracking in my code?

How can I implement backtracking in my code?

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 with backtracking?

I'm trying to solve the N-Queens problem. Can you help me with that?

Sure! The N-Queens problem involves placing N queens on an N×N chessboard so that no two queens threaten each other. You can use backtracking to place queens one by one in different columns and check for conflicts.

What would the basic structure of the code look like?

The basic structure involves a recursive function that tries to place a queen in a column, checks for conflicts, and then calls itself to place the next queen. If placing a queen leads to a solution, you return true; otherwise, you backtrack.

Can you provide a sample code snippet?

Certainly! Here's a simple Python example:

def solve_n_queens(n):
    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, n, 1), range(col, -1, -1)):
            if board[i][j] == 1:
                return False
        return True

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

    board = [[0] * n for _ in range(n)]
    if not solve_util(board, 0):
        return "No solution exists"
    return board

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