Python Algorithm for Sudoku

The most common Python algorithm for Sudoku is the backtracking algorithm.

This algorithm works by trying all possible values for each cell in the Sudoku puzzle. If a value does not work, the algorithm backtracks and tries another value. The algorithm continues backtracking until the puzzle is solved.

Another Python algorithm for Sudoku is the constraint satisfaction problem (CSP) algorithm.

This algorithm works by representing the Sudoku puzzle as a CSP. A CSP is a problem where there are a set of variables, each of which can take on a set of values. The goal of a CSP is to find a solution where all of the variables take on values that satisfy the constraints.

There are also Python algorithms for Sudoku that use machine learning.

These algorithms train a machine learning model on a set of solved Sudoku puzzles. The model can then be used to solve new Sudoku puzzles.

What is Sudoku puzzle?

A Sudoku puzzle is a 9×9 grid of cells, where each cell can be filled with a number from 1 to 9. The goal of the puzzle is to fill the grid so that each row, each column, and each 3×3 block contains all of the numbers from 1 to 9.

The program should be able to solve any Sudoku puzzle. The program should be able to print the solution to the console.

Python algorithm for Sudoku using the constraint satisfaction problem (CSP) algorithm

import itertools

def solve_sudoku_csp(grid):
“””
Solves a Sudoku puzzle using the CSP algorithm.

Args:
grid: A 9×9 array of integers.

Returns:
A solved Sudoku puzzle.
“””

variables = []
for row in range(9):
for column in range(9):
if grid[row][column] == 0:
variables.append((row, column))

domains = {}
for variable in variables:
row, column = variable
domains[variable] = list(range(1, 10))

constraints = []
for row in range(9):
for column in range(9):
if grid[row][column] != 0:
constraints.append(((row, column), set([grid[row][column]])))
for i in range(3):
for j in range(3):
constraints.append(((row // 3 * 3 + i, column // 3 * 3 + j), set(range(1, 10))))

solution = backtracking_search(variables, domains, constraints)
if solution is None:
return None

for variable, value in solution.items():
row, column = variable
grid[row][column] = value

return grid

def backtracking_search(variables, domains, constraints):
“””
Performs backtracking search to solve a CSP problem.

Args:
variables: A list of variables.
domains: A dictionary mapping variables to their domains.
constraints: A list of constraints.

Returns:
A solution to the CSP problem, or None if no solution exists.
“””

if len(variables) == 0:
return {}

variable = variables.pop()
for value in domains[variable]:
if is_consistent(variable, value, constraints):
new_domains = copy.deepcopy(domains)
new_domains[variable] = {value}
solution = backtracking_search(variables, new_domains, constraints)
if solution is not None:
return solution

return None

def is_consistent(variable, value, constraints):
“””
Checks if a value is consistent with a set of constraints.

Args:
variable: A variable.
value: A value.
constraints: A list of constraints.

Returns:
True if the value is consistent, False otherwise.
“””

for constraint in constraints:
if variable in constraint and value in constraint[1]:
return False

return True

How it works?

This code uses the CSP algorithm to solve a Sudoku puzzle. The algorithm starts by creating a list of variables, which are the cells in the puzzle that are not yet filled in. The algorithm then creates a dictionary mapping each variable to its domain, which is the set of possible values that the variable can take on.

The algorithm then creates a list of constraints, which are the rules that the variables must obey. The algorithm then calls the backtracking_search() function to solve the CSP problem. The backtracking_search() function recursively searches for a solution to the CSP problem. The function returns a solution if it finds one, or None if no solution exists.

The is_consistent() function checks if a value is consistent with a set of constraints. The function checks if the value is in the domain of the variable and if the value violates any of the constraints. The function returns True if the value is consistent and False otherwise.

Backtracking Algorithm for Sudoku

The backtracking algorithm is a simple and easy-to-understand algorithm. However, it can be slow for large Sudoku puzzles. The CSP algorithm is more complex, but it can be faster for large Sudoku puzzles.

The machine learning algorithms are the most complex, but they can be the fastest for large Sudoku puzzles.

Here is an example of a Python algorithm for Sudoku using the backtracking algorithm:

def solve_sudoku(grid):
"""
Solves a Sudoku puzzle using the backtracking algorithm.

Args:
grid: A 9×9 array of integers.

Returns:
A solved Sudoku puzzle.
“””

if is_solved(grid):
return grid

for row in range(9):
for column in range(9):
if grid[row][column] == 0:
for number in range(1, 10):
if is_valid(grid, row, column, number):
grid[row][column] = number
solved_grid = solve_sudoku(grid)
if solved_grid is not None:
return solved_grid

grid[row][column] = 0

return None

def is_solved(grid):
“””
Checks if a Sudoku puzzle is solved.

Args:
grid: A 9×9 array of integers.

Returns:
True if the puzzle is solved, False otherwise.
“””

for row in range(9):
if not is_valid_row(grid, row):
return False
for column in range(9):
if not is_valid_column(grid, column):
return False
for i in range(3):
for j in range(3):
if not is_valid_block(grid, i, j):
return False

return True

def is_valid(grid, row, column, number):
“””
Checks if a number is valid in a Sudoku puzzle.

Args:
grid: A 9×9 array of integers.
row: The row index.
column: The column index.
number: The number to check.

Returns:
True if the number is valid, False otherwise.
“””

if number in grid[row]:
return False
if number in grid[:, column]:
return False
i = row // 3 * 3
j = column // 3 * 3
if number in grid[i:i + 3, j:j + 3]:
return False

return True

How it works?

This code uses the backtracking algorithm to solve a Sudoku puzzle. The algorithm starts by checking if the puzzle is already solved. If it is not, the algorithm checks each cell in the puzzle to see if it is empty. If a cell is empty, the algorithm tries all possible values for the cell. If a value works, the algorithm recursively calls itself to solve the rest of the puzzle.

If a value does not work, the algorithm backtracks and tries another value. The algorithm continues backtracking until the puzzle is solved or until it is determined that the puzzle is unsolvable.