Try our new documentation site (beta).


sudoku.py


#!/usr/bin/env python3.7

# Copyright 2023, Gurobi Optimization, LLC

# Sudoku example.

# The Sudoku board is a 9x9 grid, which is further divided into a 3x3 grid
# of 3x3 grids.  Each cell in the grid must take a value from 0 to 9.
# No two grid cells in the same row, column, or 3x3 subgrid may take the
# same value.
#
# In the MIP formulation, binary variables x[i,j,v] indicate whether
# cell <i,j> takes value 'v'.  The constraints are as follows:
#   1. Each cell must take exactly one value (sum_v x[i,j,v] = 1)
#   2. Each value is used exactly once per row (sum_i x[i,j,v] = 1)
#   3. Each value is used exactly once per column (sum_j x[i,j,v] = 1)
#   4. Each value is used exactly once per 3x3 subgrid (sum_grid x[i,j,v] = 1)
#
# Input datasets for this example can be found in examples/data/sudoku*.

import sys
import math
import gurobipy as gp
from gurobipy import GRB


if len(sys.argv) < 2:
    print('Usage: sudoku.py filename')
    sys.exit(0)

f = open(sys.argv[1])

grid = f.read().split()

n = len(grid[0])
s = int(math.sqrt(n))


# Create our 3-D array of model variables

model = gp.Model('sudoku')

vars = model.addVars(n, n, n, vtype=GRB.BINARY, name='G')


# Fix variables associated with cells whose values are pre-specified

for i in range(n):
    for j in range(n):
        if grid[i][j] != '.':
            v = int(grid[i][j]) - 1
            vars[i, j, v].LB = 1

# Each cell must take one value

model.addConstrs((vars.sum(i, j, '*') == 1
                 for i in range(n)
                 for j in range(n)), name='V')

# Each value appears once per row

model.addConstrs((vars.sum(i, '*', v) == 1
                 for i in range(n)
                 for v in range(n)), name='R')

# Each value appears once per column

model.addConstrs((vars.sum('*', j, v) == 1
                 for j in range(n)
                 for v in range(n)), name='C')


# Each value appears once per subgrid

model.addConstrs((
    gp.quicksum(vars[i, j, v] for i in range(i0*s, (i0+1)*s)
                for j in range(j0*s, (j0+1)*s)) == 1
    for v in range(n)
    for i0 in range(s)
    for j0 in range(s)), name='Sub')

model.optimize()

model.write('sudoku.lp')

print('')
print('Solution:')
print('')

# Retrieve optimization result

solution = model.getAttr('X', vars)

for i in range(n):
    sol = ''
    for j in range(n):
        for v in range(n):
            if solution[i, j, v] > 0.5:
                sol += str(v+1)
    print(sol)

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search

Gurobi Optimization