N Queens Problem in DSA Go - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of the N Queens Problem?

To place N queens on an N×N chessboard so that no two queens attack each other. This means no two queens share the same row, column, or diagonal.

beginner

How do we check if a queen placement is safe in the N Queens Problem?

We check three conditions: no other queen is in the same column, no other queen is in the same major diagonal, and no other queen is in the same minor diagonal.

intermediate

What technique is commonly used to solve the N Queens Problem efficiently?

Backtracking is used. It tries placing queens row by row and backtracks when a conflict is found, exploring all possible safe placements.

intermediate

In Go, what data structure can represent the chessboard for the N Queens Problem?

A slice of integers where the index represents the row and the value at that index represents the column where the queen is placed.

intermediate

Why is the N Queens Problem considered a classic example of backtracking?

Because it involves exploring all possible queen placements systematically and abandoning paths that lead to conflicts, demonstrating the backtracking approach clearly.

What does the N in N Queens Problem represent?

A The size of the chessboard and number of queens

B The number of rows only

C The number of columns only

D The number of diagonals

N represents both the size of the chessboard (N×N) and the number of queens to place.

Which of these is NOT a condition to check when placing a queen?

A Same color square

B Same column

C Same diagonal

D Same row

Queens can be on any color square; color does not affect attacks.

What happens when a queen placement leads to a conflict in backtracking?

A The algorithm stops

B The board size is increased

C The queen is removed permanently

D The algorithm backtracks to try a different position

Backtracking means undoing the last move and trying a different option.

In Go, how can the position of queens be stored efficiently?

A 2D array of booleans

B Slice of integers with index as row and value as column

C Map of strings

D Linked list

A slice of integers is simple and efficient to track queen positions by row.

Which algorithmic approach best describes the N Queens solution?

A Greedy

B Divide and Conquer

C Backtracking

D Dynamic Programming

Backtracking tries all possibilities and abandons invalid ones.

Explain how backtracking helps solve the N Queens Problem step-by-step.

Think about trying and undoing queen placements.

Describe how you would represent the chessboard and queen positions in Go for the N Queens Problem.

Focus on simple data structures that map rows to columns.

Practice

1. What is the main goal of the N Queens Problem?

easy

A. Place N queens on an NxN chessboard so that no two queens attack each other

B. Count the total number of queens on a chessboard

C. Find the shortest path for a queen to move across the board

D. Arrange queens in a single row on the chessboard

Solution

1. Step 1: Understand the problem statement

   The N Queens Problem requires placing N queens on an NxN board so that no two queens threaten each other.

2. Step 2: Identify the correct goal

   This means no two queens share the same row, column, or diagonal.

3. Final Answer:

   Place N queens on an NxN chessboard so that no two queens attack each other -> Option A

4. Quick Check:

   Goal = Place queens safely [OK]

Hint: Remember: no two queens can share row, column, or diagonal [OK]

Common Mistakes:

• Thinking queens can be in the same row or column
• Confusing the problem with counting queens
• Ignoring diagonal attacks

2. Which of the following Go function signatures is correct for solving the N Queens Problem recursively?

easy

A. func solveNQueens(board []int, row int) bool

B. func solveNQueens(board []int, row string) int

C. func solveNQueens(board [][]int, col int) string

D. func solveNQueens(board [][]int, row int) bool

Solution

1. Step 1: Understand typical parameters

   Usually, the board is represented as a 1D slice of ints where index is row and value is column, and row is int.

2. Step 2: Match correct signature

   func solveNQueens(board []int, row int) bool uses a 1D slice and int row, returning bool to indicate success, which is common in backtracking.

3. Final Answer:

   func solveNQueens(board []int, row int) bool -> Option A

4. Quick Check:

   Correct signature uses []int and int row [OK]

Hint: Use 1D slice for board and int for row in recursive function [OK]

Common Mistakes:

• Using 2D slice which is less efficient
• Using wrong parameter types like string for row
• Returning wrong type instead of bool

3. What will be the output of the following Go code snippet for N=4?

func main() {
    solutions := solveNQueens(4)
    fmt.Println(len(solutions))
}

func solveNQueens(n int) [][]string {
    var res [][]string
    board := make([]int, n)
    backtrack(0, n, board, &res)
    return res
}

func backtrack(row, n int, board []int, res *[][]string) {
    if row == n {
        *res = append(*res, generateBoard(board, n))
        return
    }
    for col := 0; col < n; col++ {
        if isValid(row, col, board) {
            board[row] = col
            backtrack(row+1, n, board, res)
        }
    }
}

func isValid(row, col int, board []int) bool {
    for i := 0; i < row; i++ {
        if board[i] == col || abs(board[i]-col) == row-i {
            return false
        }
    }
    return true
}

func generateBoard(board []int, n int) []string {
    var res []string
    for i := 0; i < n; i++ {
        row := make([]byte, n)
        for j := 0; j < n; j++ {
            if board[i] == j {
                row[j] = 'Q'
            } else {
                row[j] = '.'
            }
        }
        res = append(res, string(row))
    }
    return res
}

func abs(x int) int {
    if x < 0 {
        return -x
    }
    return x
}

medium

A. 4

B. 2

C. 1

D. 0

Solution

1. Step 1: Understand the code logic

   The code uses backtracking to find all solutions for 4 queens on a 4x4 board.

2. Step 2: Recall known result for 4 queens

   It is a classic result that there are 2 distinct solutions for 4 queens.

3. Final Answer:

   2 -> Option B

4. Quick Check:

   Number of solutions for N=4 = 2 [OK]

Hint: Remember classic N=4 has exactly 2 solutions [OK]

Common Mistakes:

• Assuming only 1 solution
• Confusing number of solutions with board size
• Ignoring diagonal checks

4. Identify the bug in this Go backtracking function for N Queens:

func backtrack(row, n int, board []int, res *[][]string) {
    if row == n {
        *res = append(*res, generateBoard(board, n))
        return
    }
    for col := 0; col < n; col++ {
        if isValid(row, col, board) {
            board[row] = col
            backtrack(row+1, n, board, res)
            board[row] = -1
        }
    }
}

medium

A. The function should return an int, not void

B. The base case condition should be row > n

C. The loop should start from col = 1 instead of 0

D. Missing resetting board[row] after recursion causes wrong results

Solution

1. Step 1: Analyze backtracking state reset

   After recursive call, board[row] must be reset to -1 to undo the choice.

2. Step 2: Check if resetting is done correctly

   Resetting board[row] = -1 is done inside the loop after recursion, which is correct to backtrack.

3. Step 3: Identify the bug

   If resetting is missing or incorrect, it causes wrong results because previous choices remain.

4. Final Answer:

   Missing resetting board[row] after recursion causes wrong results -> Option D

5. Quick Check:

   Backtracking needs state reset [OK]

Hint: Always undo your move after recursion in backtracking [OK]

Common Mistakes:

• Not resetting board state after recursion
• Wrong base case condition
• Incorrect loop start index

5. How can you optimize the N Queens solution to handle large N efficiently in Go?

hard

A. Use nested loops without recursion for all placements

B. Store the entire board as a 2D slice of strings

C. Use bitmasks to track columns and diagonals instead of arrays

D. Check only rows for conflicts, ignore columns and diagonals

Solution

1. Step 1: Understand performance bottlenecks

   Checking conflicts using arrays for columns and diagonals is slower for large N.

2. Step 2: Use bitmasks for fast conflict checks

   Bitmasks allow O(1) checks and updates for columns and diagonals, improving speed and memory.

3. Step 3: Compare options

   Use bitmasks to track columns and diagonals instead of arrays uses bitmasks, which is a known optimization. Others increase complexity or ignore constraints.

4. Final Answer:

   Use bitmasks to track columns and diagonals instead of arrays -> Option C

5. Quick Check:

   Bitmask optimization = faster large N [OK]

Hint: Use bitmasks for columns and diagonals to speed up checks [OK]

Common Mistakes:

• Using 2D board increases memory and slows down
• Ignoring diagonal checks causes wrong solutions
• Trying nested loops without pruning is inefficient