I can't improve the performance of the following Sudoku Solver code. I know there are 3 loops here and they probably cause slow performance but I can't find a better/more efficient way. "board" is mutated with every iteration of recursion - if there are no zeros left, I just need to exit the recursion.
I tried to isolate "board" from mutation but it hasn't changed the performance. I also tried to use list comprehension for the top 2 "for" loops (ie only loop through rows and columns with zeros), tried to find coordinates of all zeros, and then use a single loop to go through them - hasn't helped.
I think I'm doing something fundamentally wrong here with recursion - any advice or recommendation on how to make the solution faster?
def box(board,row,column):
start_col = column - (column % 3)
finish_col = start_col + 3
start_row = row - (row % 3)
finish_row = start_row + 3
return [y for x in board[start_row:finish_row] for y in x[start_col:finish_col]]
def possible_values(board,row,column):
values = {1,2,3,4,5,6,7,8,9}
col_values = [v[column] for v in board]
row_values = board[row]
box_values = box(board, row, column)
return (values - set(row_values + col_values + box_values))
def solve(board, i_row = 0, i_col = 0):
for rn in range(i_row,len(board)):
if rn != i_row: i_col = 0
for cn in range(i_col,len(board)):
if board[rn][cn] == 0:
options = possible_values(board, rn, cn)
for board[rn][cn] in options:
if solve(board, rn, cn):
return board
board[rn][cn] = 0
#if no options left for the cell, go to previous cell and try next option
return False
#if no zeros left on the board, problem is solved
return True
problem = [
[9, 0, 0, 0, 8, 0, 0, 0, 1],
[0, 0, 0, 4, 0, 6, 0, 0, 0],
[0, 0, 5, 0, 7, 0, 3, 0, 0],
[0, 6, 0, 0, 0, 0, 0, 4, 0],
[4, 0, 1, 0, 6, 0, 5, 0, 8],
[0, 9, 0, 0, 0, 0, 0, 2, 0],
[0, 0, 7, 0, 3, 0, 2, 0, 0],
[0, 0, 0, 7, 0, 5, 0, 0, 0],
[1, 0, 0, 0, 4, 0, 0, 0, 7]
]
solve(problem)
Three things you can do to speed this up:
possible_values = row_candidates[row] & col_candidates[col] & box_candidates[box]
. This is a constant factors thing that will change very little in your approach.A result that worked at the end thanks to 53x15 and kosciej16. Not ideal or most optimal but passes the required performance test:
def solve(board, i_row = 0, i_col = 0):
cells_to_solve = [((rn, cn), possible_values(board,rn,cn)) for rn in range(len(board)) for cn in range(len(board)) if board[rn][cn] == 0]
if not cells_to_solve: return True
min_n_of_values = min([len(x[1]) for x in cells_to_solve])
if min_n_of_values == 0: return False
best_cells_to_try = [((rn,cn),options) for ((rn,cn),options) in cells_to_solve if len(options) == min_n_of_values]
for ((rn,cn),options) in best_cells_to_try:
for board[rn][cn] in options:
if solve(board, rn, cn):
return board
board[rn][cn] = 0
return False
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