BacktrackingMedium
Backtracking Algorithms: When and How to Use Them
Deep dive into backtracking algorithms with examples including permutations, combinations, and constraint satisfaction problems.
November 20, 2024
14 min read
BacktrackingRecursionAlgorithms
Backtracking Algorithms: When and How to Use Them
Backtracking is a powerful technique for solving constraint satisfaction problems by exploring all possibilities.
The Backtracking Pattern
Permutations
Generate all permutations of an array:
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 resultCombinations
Generate combinations of k elements:
def combine(n, k):
result = []
def backtrack(start, current):
if len(current) == k:
result.append(current[:])
return
for i in range(start, n + 1):
current.append(i)
backtrack(i + 1, current)
current.pop() # Backtrack
backtrack(1, [])
return resultN-Queens Problem
Place n queens on n×n board without attacking:
def solve_n_queens(n):
result = []
board = [['.'] * n for _ in range(n)]
def is_safe(row, col):
# Check column
for i in range(row):
if board[i][col] == 'Q':
return False
# Check diagonals
for i, j in zip(range(row-1, -1, -1), range(col-1, -1, -1)):
if board[i][j] == 'Q':
return False
for i, j in zip(range(row-1, -1, -1), range(col+1, n)):
if board[i][j] == 'Q':
return False
return True
def backtrack(row):
if row == n:
result.append([''.join(row) for row in board])
return
for col in range(n):
if is_safe(row, col):
board[row][col] = 'Q'
backtrack(row + 1)
board[row][col] = '.' # Backtrack
backtrack(0)
return resultWhen to Use Backtracking
Key Tips
Backtracking is essential for many interview problems!
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