# Speed up Sudoku Solver

I made this Sudoku solver using depth first search, it takes less than 0.1 second to solve any simple solution(no guessing), but if the solution requires guessing (thus using the DFS) its time grows exponentially.

One puzzle ('hard') makes 10 consecutive branches in the search, thus allowing 210 (I guess) possible outcomes. It takes around 40 seconds to beat this puzzle.

HARD - 3 RUNS
Total: 114.4 seconds
Mean: 38.13433 seconds
Max: 38.67300 seconds
Min: 37.74700 seconds

get_missing_values()
Called: 475437 times per run(1426311 total)
Running for 87.362s(in 3 runs) / 29.12073s per run
create_cells()
Called: 47700 times per run(143100 total)
Running for 19.419s(in 3 runs) / 6.47302s per run
get_next_moves()
Called: 47700 times per run(143100 total)
Running for 6.117s(in 3 runs) / 2.03890s per run
depth_first_search()
Called: 1 times per run(3 total)
Running for 0.856s(in 3 runs) / 0.28532s per run
possible_moves()
Called: 47700 times per run(143100 total)
Running for 0.647s(in 3 runs) / 0.21570s per run
main()
Called: 1 times per run(1 total)
Running for 0.056s(in 3 runs) / 0.01876s per run
win()
Called: 1 times per run(3 total)
Running for 0.000s(in 3 runs) / 0.00002s per run


As you can see, the source of the most 'work' is the method get_missing_values from the class Cell, which takes 76% of all the workload. This method searches for every cell in the grid and compare it to the others to get what possible values could be placed there. I tried to squeeze it as much as I could, using multiple if-conditions, but it still takes a lot of work.

import functools
import time

DEBUGGER_TIMER = {}

def timeit(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
t_start = time.clock()
ret = func(*args, **kwargs)
t_end = time.clock() - t_start
function_name = func.__name__
if function_name in DEBUGGER_TIMER:
DEBUGGER_TIMER[function_name][0] += t_end
DEBUGGER_TIMER[function_name][1] += 1
else:
DEBUGGER_TIMER[function_name] = [t_end, 1]
return ret

return wrapper

# index:(row,column,group,value)
INDEX_TO_VALUE = {0: (0, 0, 0), 1: (0, 1, 0), 2: (0, 2, 0),
3: (0, 3, 1), 4: (0, 4, 1), 5: (0, 5, 1),
6: (0, 6, 2), 7: (0, 7, 2), 8: (0, 8, 2),
9: (1, 0, 0), 10: (1, 1, 0), 11: (1, 2, 0),
12: (1, 3, 1), 13: (1, 4, 1), 14: (1, 5, 1),
15: (1, 6, 2), 16: (1, 7, 2), 17: (1, 8, 2),
18: (2, 0, 0), 19: (2, 1, 0), 20: (2, 2, 0),
21: (2, 3, 1), 22: (2, 4, 1), 23: (2, 5, 1),
24: (2, 6, 2), 25: (2, 7, 2), 26: (2, 8, 2),
27: (3, 0, 3), 28: (3, 1, 3), 29: (3, 2, 3),
30: (3, 3, 4), 31: (3, 4, 4), 32: (3, 5, 4),
33: (3, 6, 5), 34: (3, 7, 5), 35: (3, 8, 5),
36: (4, 0, 3), 37: (4, 1, 3), 38: (4, 2, 3),
39: (4, 3, 4), 40: (4, 4, 4), 41: (4, 5, 4),
42: (4, 6, 5), 43: (4, 7, 5), 44: (4, 8, 5),
45: (5, 0, 3), 46: (5, 1, 3), 47: (5, 2, 3),
48: (5, 3, 4), 49: (5, 4, 4), 50: (5, 5, 4),
51: (5, 6, 5), 52: (5, 7, 5), 53: (5, 8, 5),
54: (6, 0, 6), 55: (6, 1, 6), 56: (6, 2, 6),
57: (6, 3, 7), 58: (6, 4, 7), 59: (6, 5, 7),
60: (6, 6, 8), 61: (6, 7, 8), 62: (6, 8, 8),
63: (7, 0, 6), 64: (7, 1, 6), 65: (7, 2, 6),
66: (7, 3, 7), 67: (7, 4, 7), 68: (7, 5, 7),
69: (7, 6, 8), 70: (7, 7, 8), 71: (7, 8, 8),
72: (8, 0, 6), 73: (8, 1, 6), 74: (8, 2, 6),
75: (8, 3, 7), 76: (8, 4, 7), 77: (8, 5, 7),
78: (8, 6, 8), 79: (8, 7, 8), 80: (8, 8, 8)}

class Cell:
board = []
missing_values = set('123456789')

def __init__(self, row, column, group, value, index):
self.row = row
self.column = column
self.value = value
self.group = group
self.index = index
Cell.board.append(self)

@timeit
def get_missing_values(self):
values = set('.')
for cell in Cell.board:
if cell.value not in values:
if cell.row == self.row:
elif cell.column == self.column:
elif cell.group == self.group:
return Cell.missing_values.difference(values)

@timeit
def create_cells(puzzle):
for index, value in enumerate(puzzle):
row, column, group = INDEX_TO_VALUE[index]
Cell(row, column, group, value, index)

@timeit
def get_next_moves():
try:
moves = {}
for cell in Cell.board:
if cell.value == '.':
missing_values = cell.get_missing_values()
moves[cell.index] = missing_values
if len(missing_values) == 1:
return cell.index, moves[cell.index]
for index in sorted(moves, key=lambda k: len(moves[k])):
return index, moves[index]
finally:
Cell.board = []  # clean-up, just in case

@timeit
def possible_moves(puzzle):
moves = get_next_moves()
results = set()
if moves:
for move in moves[1]:
index = moves[0]
puzzle = puzzle[:index] + move + puzzle[index + 1:]
return results

@timeit
def win(puzzle):
if "".join(sorted(puzzle)) == '111111111222222222333333333444444444555555555666666666777777777888888888999999999':
return True
return False

@timeit
def depth_first_search(graph, start):
visited, stack = set(), [start]
solutions = []
while stack:
vertex = stack.pop()
if '.' not in vertex:
if win(vertex):
solutions.append(vertex)
if len(solutions) > 1:
raise Exception("More than one solution")
if vertex not in visited:
create_cells(vertex)
graph[vertex] = possible_moves(vertex)
stack.extend(graph[vertex] - visited)
if solutions:
return solutions
else:
raise Exception("No solution found")

@timeit
def main(min_test_time, min_runs, puzzle):
easy = "53..7....6..195....98....6.8...6...34..8.3..17...2...6.6....28....419..5....8..79"
hard = "8..........36......7..9.2...5...7.......457.....1...3...1....68..85...1..9....4.."

if puzzle == 'easy':
test_puzzle = easy
else:
test_puzzle = hard

times = []
n_runs = 0
while sum(times) < min_test_time or min_runs > n_runs:
n_runs += 1
graph = {test_puzzle: [None]}
start = time.time()
result = depth_first_search(graph, graph.keys()[0])
end = time.time() - start
if not debug:
return test_puzzle,result,end
if end > 0.0:
times.append(end)
return times, n_runs

def debugger((times, n_runs)):
subtracts = {'main': ['depth_first_search'],
'depth_first_search': ['create_cells','possible_moves','win'],
'possible_moves': ['get_next_moves'],
'get_next_moves': ['get_missing_values'],
'get_missing_values': [],
'create_cells': [],
'win': []}
total_time = sum(times)
print "%s - %i RUNS"%(puzzle_to_test.upper(),n_runs)
print "Total: %.1f seconds" % total_time
if n_runs > 1:
print "\tMean: %.5f seconds" % (total_time / n_runs)
print "\tMax: %.5f seconds" % max(times)
print "\tMin: %.5f seconds\n" % min(times)
for f_name in DEBUGGER_TIMER:
f_time = DEBUGGER_TIMER[f_name][0] - sum([DEBUGGER_TIMER[o_name][0] for o_name in subtracts[f_name]])
DEBUGGER_TIMER[f_name].append(f_time)

for f_name in sorted(DEBUGGER_TIMER, key=lambda name: DEBUGGER_TIMER[name][2], reverse=True):
real_time = DEBUGGER_TIMER[f_name][2]
f_runs = DEBUGGER_TIMER[f_name][1]
print "%s()\n\t" \
"Called: %i times per run(%i total)\n\t" \
"Running for %.3fs(in %i runs) / %.5fs per run" % (
f_name, (f_runs + (n_runs - 1)) // n_runs, f_runs, real_time, n_runs, real_time / n_runs)

if __name__ == '__main__':
puzzle_to_test = 'hard'
debug = True
if debug:
min_test_time = 10  # seconds
min_runs = 3
debugger(main(min_test_time,min_runs,puzzle_to_test))
else:
puzzle, result,end_time = main(0, 1, puzzle_to_test)
print "Puzzle: "
for i, n in enumerate(puzzle,1):
if i % 9 == 0:
print n
else:
print n,
print "\nSolution"
for i,n in enumerate(result[0],1):
if i%9==0:
print n
else:
print n,
print "\nTook %.2f seconds"%end_time


Firstly, use PyPy.

$python2 p.py Total: 29.8 seconds$ pypy p.py
Total: 5.1 seconds


PyPy actually does inlining, so the timeit overhead makes up a big part of that. Removing it gives

$pypy p.py Total: 4.1 seconds  That's a factor-of-7 improvement already. Often PyPy 3 is faster, so to try that. To do you should • change the prints to functions and use from __future__ import print_function to keep Python 2 compatibility and • change debugger to use def debugger(times, n_runs): and call it with debugger(*main(...)). It averages a tiny bit faster: $ pypy3 p.py
Total: 4.1 seconds


INDEX_TO_VALUE should be a tuple, not a dictionary:

INDEX_TO_VALUE = ((0, 0, 0), (0, 1, 0), (0, 2, 0),
(0, 3, 1), (0, 4, 1), (0, 5, 1),
(0, 6, 2), (0, 7, 2), (0, 8, 2),
...)


The comment above it is bad

# index:(row,column,group,value)


The spacing is a bit cramped, but primarily I'm concerned that there are the wrong number of elements! Remove the last one.

Further, a comprehension is much cleaner:

INDEX_TO_VALUE = tuple((row, col, row//3 + col//3*3) for row in range(9) for col in range(9))


although you might want to make a function to help:

def group_at(row, col):
return row // 3 + col // 3 * 3

INDEX_TO_VALUE = tuple((row, col, group_at(row, col)) for row in range(9) for col in range(9))


Your timeit function should really avoid the globals by using lexical scoping. This strikes me as a good comromise:

debugged = set()

def timeit(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
t_start = time.clock()
ret = func(*args, **kwargs)
t_end = time.clock() - t_start

wrapper.calls += 1
wrapper.time += t_end

return ret

wrapper.calls = 0
wrapper.time = 0
return wrapper


You still have global data but you localize it to the function. To me, this seems a sensible trade. Note that debugged and the original DEBUGGER_TIMER were not constants so should not be in ALL_CAPS.

Another thing that irks me is your use of strings to represent the board. Tuples of integers makes more sense and 0 can replace your .. Strings might be neglibily faster, but it seems like a poor readability trade to me.

You don't use graph as far as I can tell. Just remove it.

You do:

   if vertex not in visited:
...
stack.extend(graph[vertex] - visited)


The - visited is redundant with the if vertex not in visited check. Remove it. Further, you don't need any of the checks anyway; the cost of doing them exceeds the benefit, at least after moving to lists of integers. You might need to change solutions to a set.

Your

list(results.add("%s%s%s" % (puzzle[:index], move, puzzle[index + 1:])) for move in moves[1])


is a bit crazy; just do

for move in moves[1]: results.add("%s%s%s" % (puzzle[:index], move, puzzle[index + 1:]))


Cell.board is a global; this is a really bad idea as it wrecks the ability to use this extensibly (say, have two boards). Speed-wise it's very problematic because you rebuild the whole grid each time; it would be better to mutate as you go and undo when backtracking. However, it is possible to improve. I tried something like

@timeit
def get_missing_values(row, column, group, board):
missing = [1, 2, 3, 4, 5, 6, 7, 8, 9]

for c_row, c_column, c_group, c_value in board:
if c_row == row or c_column == column or c_group == group:
if c_value in missing:
missing.remove(c_value)

return missing

@timeit
def create_cells(puzzle):
taken = []
untaken = []

for index, value in enumerate(puzzle):
row, column, group = INDEX_TO_VALUE[index]
if value:
taken.append((row, column, group, value))
else:
untaken.append((row, column, group))

return taken, untaken

@timeit
def get_next_moves(puzzle):
taken, untaken = create_cells(puzzle)
other_option = None
len_option = 9

for row, column, group in untaken:
missing_values = get_missing_values(row, column, group, taken)

if len(missing_values) == 1:
return 9*row+column, missing_values

elif len(missing_values) < len_option:
len_option = len(missing_values)
other_option = 9*row+column, missing_values

return other_option


This gets rid of the class for convenience (tuples are easier here). get_next_moves now generates the board and passes that to get_missing_values. This improves times:

$python3 p.py HARD - 3 RUNS Total: 19.8 seconds$ python2 p.py
HARD - 3 RUNS
Total: 12.7 seconds

$pypy3 p.py HARD - 3 RUNS Total: 2.3 seconds$ pypy p.py
HARD - 3 RUNS
Total: 2.3 seconds


But get_missing_values expectedly still takes majority time. Using a set instead of a list speeds up CPython but slows down PyPy.

This suggests effort should be solely on a more efficient representation for that. Here's one idea:

@timeit
def create_cells(puzzle):
rows = [set() for _ in range(9)]
columns = [set() for _ in range(9)]
groups = [set() for _ in range(9)]
untaken = []

for index, value in enumerate(puzzle):
row, column, group = INDEX_TO_VALUE[index]
if value:
else:
untaken.append((row, column, group))

return rows, columns, groups, untaken

@timeit
def get_next_moves(puzzle):
rows, columns, groups, untaken = create_cells(puzzle)
other_option = None
len_option = 9

for row, column, group in untaken:
missing_values = {1, 2, 3, 4, 5, 6, 7, 8, 9} - rows[row] - columns[column] - groups[group]

if len(missing_values) == 1:
return 9*row+column, missing_values

elif len(missing_values) < len_option:
len_option = len(missing_values)
other_option = 9*row+column, missing_values

return other_option


This gives, after disabling timeit,

$python3 p.py Total: 8.1 seconds$ python2 p.py
Total: 6.7 seconds

$pypy3 p.py Total: 2.0 seconds$ pypy p.py
Total: 2.0 seconds


which is a massive boost for CPython.

It so happens that get_next_moves is still taking the most time, although create_cells is catching up. One idea is to change the sets to bitmasks:

@timeit
def create_cells(puzzle):
rows = [0] * 9
columns = [0] * 9
groups = [0] * 9
untaken = []

for index, value in enumerate(puzzle):
row, column, group = INDEX_TO_VALUE[index]
if value:
rows[row] |= 1<<(value-1)
columns[column] |= 1<<(value-1)
groups[group] |= 1<<(value-1)
else:
untaken.append((row, column, group))

return rows, columns, groups, untaken

decode_bits = [tuple(i+1 for i in range(9) if 1<<i & bits) for bits in range(512)]

@timeit
def get_next_moves(puzzle):
rows, columns, groups, untaken = create_cells(puzzle)
other_option = None
len_option = 9

for row, column, group in untaken:
missing_values = decode_bits[0b111111111 & ~rows[row] & ~columns[column] & ~groups[group]]

if len(missing_values) == 1:
return 9*row+column, missing_values

elif len(missing_values) < len_option:
len_option = len(missing_values)
other_option = 9*row+column, missing_values

return other_option


This gives a very good boost in speed to PyPy and improves CPython noticeably:

$python3 p.py HARD - 3 RUNS Total: 8.0 seconds Mean: 2.66720 seconds Max: 2.67445 seconds Min: 2.66322 seconds create_cells() Called: 47680 times per run (143040 total) Running for 6.101s (in 3 runs) / 2.03374s per run get_next_moves() Called: 47680 times per run (143040 total) Running for 1.189s (in 3 runs) / 0.39625s per run possible_moves() Called: 47680 times per run (143040 total) Running for 0.443s (in 3 runs) / 0.14770s per run depth_first_search() Called: 1 times per run (3 total) Running for 0.268s (in 3 runs) / 0.08946s per run win() Called: 1 times per run (3 total) Running for 0.000s (in 3 runs) / 0.00004s per run main() Called: 1 times per run (1 total) Running for 0.000s (in 3 runs) / 0.00003s per run  and for PyPy: $ pypy3 p.py
HARD - 4 RUNS
Total: 1.0 seconds
Mean: 0.26078 seconds
Max: 0.35315 seconds
Min: 0.21801 seconds

possible_moves()
Called: 47680 times per run (190720 total)
Running for 0.519s (in 4 runs) / 0.12972s per run
create_cells()
Called: 47680 times per run (190720 total)
Running for 0.339s (in 4 runs) / 0.08473s per run
depth_first_search()
Called: 1 times per run (4 total)
Running for 0.094s (in 4 runs) / 0.02351s per run
get_next_moves()
Called: 47680 times per run (190720 total)
Running for 0.091s (in 4 runs) / 0.02272s per run
win()
Called: 1 times per run (4 total)
Running for 0.000s (in 4 runs) / 0.00009s per run
main()
Called: 1 times per run (1 total)
Running for 0.000s (in 4 runs) / 0.00005s per run


So the next thing to tackle is create_cells or possible_moves, depending on which interpreter you care about most. Going with create_cells, I currently have:

@timeit
def create_cells(puzzle):
rows = [0] * 9
columns = [0] * 9
groups = [0] * 9
untaken = []

for index, value in enumerate(puzzle):
row, column, group = INDEX_TO_VALUE[index]
if value:
rows[row] |= 1<<(value-1)
columns[column] |= 1<<(value-1)
groups[group] |= 1<<(value-1)
else:
untaken.append((row, column, group))

return rows, columns, groups, untaken


CPython doesn't deduplicate repeated constants like PyPy does, so one should deduplicate it manually. We should also move to using zip instead of enumerate:

@timeit
def create_cells(puzzle):
rows = [0] * 9
columns = [0] * 9
groups = [0] * 9
untaken = []

for position, value in zip(INDEX_TO_VALUE, puzzle):
if value:
row, column, group = position
bit = 1<<(value-1)
rows[row] |= bit
columns[column] |= bit
groups[group] |= bit
else:
untaken.append(position)

return rows, columns, groups, untaken


CPython is noticably faster:

$python3 p.py Total: 5.9 seconds$ python2 p.py
Total: 4.1 seconds


CPython is now as fast as PyPy was at the start! I didn't get times for PyPy.

It's even a bit faster if we carry the change through; make all operations act on shifted bits and unshift them at the end; eg.

bit_counts = tuple(bin(i).count("1") for i in range(512))

@timeit
def get_next_moves(puzzle):
rows, columns, groups, untaken = create_cells(puzzle)
other_option = None
len_option = 9

for row, column, group in untaken:
missing_values = 0b111111111 & ~rows[row] & ~columns[column] & ~groups[group]
set_bits = bit_counts[missing_values]

if set_bits == 1:
return 9*row+column, missing_values

elif set_bits < len_option:
len_option = set_bits
other_option = 9*row+column, missing_values

return other_option


and similar for the rest of the code.

$pypy3 p.py Min: 0.11042 seconds$ pypy p.py
Min: 0.13626 seconds

$python3 p.py Min: 1.70156 seconds$ python2 p.py
Min: 1.24454 seconds


I'm using minimum times now because PyPy gets below the minimum runtime overall. We can see that both interpreters are much faster than previously.

Personally, I would write possible_moves as a generator:

@timeit
def possible_moves(puzzle):
index_moves = get_next_moves(puzzle)

if not index_moves:
return

index, moves = index_moves
for bit in bits:
if moves & bit:
yield puzzle[:index] + (bit,) + puzzle[index + 1:]


Instead of your timing code, I would have used cProfile. It's built-in and far simpler to use. For CPython I would also sometimes use line_profiler, which gives line-by-line timings. In other words, it's the best thing evah. I would use the time utility to get a time of the whole code when fine-grained times aren't needed.

These get rid of a nontrivial portion of the code.

I would be very careful to stick to PEP8 spacing and add line breaks where they help. Your code is too dense.

I would extract the grid printing code into another function.

main shouldn't really be returning things; stick everything under if __name__ == '__main__' into main.

depth_first_search will only ever return exactly one solution, so there's no need to return a set. Further,

• Try returning early,
• Raise an exception if not win, as you would have entered an invalid state.
• Don't use the top-level Exception type; use more precise variants.
def depth_first_search(start):
stack = [start]
solution = None

while stack:
vertex = stack.pop()

if 0 not in vertex:
assert win(vertex)

if solution and vertex != solution:
raise ValueError("More than one solution")

solution = vertex

else:
stack.extend(possible_moves(vertex))

if solution is None:
raise ValueError("No solution found")

return solution


win doesn't really check if you've won; rename it to, say, validate.

INDEX_TO_VALUE should really be renamed by this point. I would go with POSITIONS.

validate can just be:

def validate(puzzle):
return Counter(puzzle) == dict.fromkeys(BITS, 9)


In my opinion, depth_first_search should yield its solutions including duplicates and the callee should be responsible for checking that the right number of solutions are present and removing duplicates.

There should be a puzzle name: puzzle dictionary that is used to search for puzzles. I would also try to format the grid more explicitly.

I would go with:

_ = 0

puzzles = {
'easy': [
5,3,_, _,7,_, _,_,_,
6,_,_, 1,9,5, _,_,_,
_,9,8, _,_,_, _,6,_,

8,_,_, _,6,_, _,_,3,
4,_,_, 8,_,3, _,_,1,
7,_,_, _,2,_, _,_,6,

_,6,_, _,_,_, 2,8,_,
_,_,_, 4,1,9, _,_,5,
_,_,_, _,8,_, _,7,9,
],

'hard': [
8,_,_, _,_,_, _,_,_,
_,_,3, 6,_,_, _,_,_,
_,7,_, _,9,_, 2,_,_,

_,5,_, _,_,7, _,_,_,
_,_,_, _,4,5, 7,_,_,
_,_,_, 1,_,_, _,3,_,

_,_,1, _,_,_, _,6,8,
_,_,8, 5,_,_, _,1,_,
_,9,_, _,_,_, 4,_,_,
]
}


Here's the full code:

# encoding: utf8

"""
A puzzle-solving masterpiece!

Solves Soduko.
"""

from __future__ import print_function

import functools
import time

from collections import Counter

def group_at(row, col):
return row // 3 + col // 3 * 3

# The row, column and group number for each item in the grid
POSITIONS = tuple((row, col, group_at(row, col)) for row in range(9) for col in range(9))

# The number of bits for each value 0 <= i < 512
BIT_COUNTS = tuple(bin(i).count("1") for i in range(512))

# For looping
BITS = tuple(1<<i for i in range(9))

# Inverse of above for printing
DECODE_BIT = {1<<i: i+1 for i in range(9)}
DECODE_BIT[0] = 0

def find_taken(puzzle):
"""
Return three lists of what numbers are taken in each row, column and group and
one list of which positions (row, column, group) are untaken.
"""

rows = [0] * 9
columns = [0] * 9
groups = [0] * 9
untaken = []

for position, bit in zip(POSITIONS, puzzle):
if bit:
row, column, group = position
rows[row] |= bit
columns[column] |= bit
groups[group] |= bit
else:
untaken.append(position)

return rows, columns, groups, untaken

def get_next_moves(puzzle):
"""
Return the (index, missing_values) pair with the fewest possible moves.
index is an integer 0 <= index < 81 and missing_values is a bitset
of length 9.

Returns None if there are no candidate moves.
"""

rows, columns, groups, untaken = find_taken(puzzle)
other_option = None
len_option = 9

for row, column, group in untaken:
missing_values = 0b111111111 & ~rows[row] & ~columns[column] & ~groups[group]
set_bits = BIT_COUNTS[missing_values]

if set_bits == 1:
return 9*row+column, missing_values

elif set_bits < len_option:
len_option = set_bits
other_option = 9*row+column, missing_values

return other_option

def possible_moves(puzzle, index, moves):
"""
Yield the states of the grid for after taking the given moves at index on puzzle.

index is an integer 0 <= index < 81 and moves is a bitset of length 9.
"""

for bit in BITS:
if moves & bit:
yield puzzle[:index] + (bit,) + puzzle[index + 1:]

def validate(puzzle):
"""
Validate that the puzzle contains 9 of each number and is length 81.

This does not fully validate that the solution is valid.
"""

return Counter(puzzle) == dict.fromkeys(BITS, 9)

def depth_first_search(puzzle):
"""
Do a depth-first search of the solution space for the input Soduku puzzle.
Yields solutions. May yield duplicates.
"""

stack = [puzzle]

while stack:
vertex = stack.pop()

if 0 not in vertex:
assert validate(vertex)
yield vertex

else:
stack.extend(possible_moves(vertex, *get_next_moves(vertex)))

def print_grid(puzzle):
"""
Print a pretty representation of the input Soduku puzzle.
"""

for i, bit in enumerate(puzzle, 1):
value = DECODE_BIT[bit] or "·"

if i % 9 == 0:
print(value)
else:
print(value, end="")

if i % 9 and not i % 3:
print(" ", end="")

if i == 27 or i == 54:
print()

def main(puzzle_name):
_ = 0

puzzles = {
'easy': [
5,3,_, _,7,_, _,_,_,
6,_,_, 1,9,5, _,_,_,
_,9,8, _,_,_, _,6,_,

8,_,_, _,6,_, _,_,3,
4,_,_, 8,_,3, _,_,1,
7,_,_, _,2,_, _,_,6,

_,6,_, _,_,_, 2,8,_,
_,_,_, 4,1,9, _,_,5,
_,_,_, _,8,_, _,7,9,
],

'hard': [
8,_,_, _,_,_, _,_,_,
_,_,3, 6,_,_, _,_,_,
_,7,_, _,9,_, 2,_,_,

_,5,_, _,_,7, _,_,_,
_,_,_, _,4,5, 7,_,_,
_,_,_, 1,_,_, _,3,_,

_,_,1, _,_,_, _,6,8,
_,_,8, 5,_,_, _,1,_,
_,9,_, _,_,_, 4,_,_,
]
}

grid = tuple(i and 1<<(i-1) for i in puzzles[puzzle_name])

print("Puzzle:")
print_grid(grid)

[result] = set(depth_first_search(grid))

print()
print("Result:")
print_grid(result)

if __name__ == '__main__':
main('hard')

• Man, I'm in awe. Amazing performance you got. Thank you so much, I will study this more deeply to understand what I was getting wrong. I've heard that PyPy was slightly faster, but that's absolute different from what I was expecting. If you feel like doing the style review I would be so much glad. – f.rodrigues Dec 26 '14 at 14:35
• You are the man. I've configured my PyCharm to inspect pep8 and all but somehow it always forgets stuffs, I'm not very familiar with all the pep8 guides so I've missed many of them. If could give two upvotes I would! Thank you again! – f.rodrigues Dec 27 '14 at 5:25

I think it would be fairly faster to store which numbers have already been used in two int[9] arrays and in one int[3][3] matrix. Then using bitmaps to represent which number have been used in each column, row and group.

For example, when you put 3 in the cell (3,4), you would:

row[3] |= 1<<3. column[4] |= 1<<3, group[1][2] |= 1<<3 and then you could check if a number is in a row, col or group with if row[rowNumber] & 1<<number: etc.

You get the point. That would be fairly faster in my opinion. Although I don't have any benchmark. I think this because then, instead of checking every cell for each new value, you check only 3 (once per # you want to discard, so 27 total).

### How it works?

row is an array of 9 positions. Each int stores the info for which number has been used in the specified row. As you know, an int is stored as a 32-bit binary number. When you do row[3] |= 1<<3 what you are actually doing is setting the 4th least significant bit to one. So if the number was (I'm only going to use 10 bits for clarity) 0000000000 it becomes 0000001000. That way, when you have a row that for example has the 1, 3, 9 used. You would have the number marked as 1000001010 (The last 0 is never marked, this to use base 1 for clarity of code). Now, how can you know which bit is marked? Well, using bitwise and &. So you create the number which only has the bit you want to ask for marked 1<<number, and then use & to extract a number that only might have that number marked (as every other bit will be compared to 0 and always yield 0). So you do row[3] & 1<<9 for example to ask if, in the row number 3, the number 9 has been used. In the bottom, you would be doing (keeping with the example of the row with 1, 3, 9):

1000001010 &
1000000000
----------
1000000000


This will yield a positive (i.e. non-zero) number, which you can "if" to see it's truth value.

If it was false (for example, asking for 8):

1000001010 &
0100000000
----------
0000000000


This would yield 0, so the if would be false.

## Update: Further modifications

In order to not recreate over and over the entire state of the board (using store so many times). You could keep one state at the beginning of ROWS, COLUMNS and GROUPS. And then, each time you store something in the stack, put there also what is the move you made (row, column, group and value). So, each time you pop something from the stack, you store the move at that point (only one thing to be stored, much more efficient). And at the end of the cycle you can unstore it by simply doing (for example) ROWS[row] &= ~(1<<value). This is how I understand your code, it might not be right, but you can analyze this and tell me if it might work.

• Sorry I don't understand those operators, it's bitwise right? I'm not familar with them. Could you explain this. – f.rodrigues Dec 24 '14 at 7:12
• Yes. That's right. I will edit the answer to explain. – Santiago Dec 24 '14 at 16:19
• That's pretty neat. I'll try to implement this. Where should the row/column/group matrix be stored? in the cell instance or in a separated class? I'm not sure that Python has an int array thou, It does have list, but they take much more space, does it matter? – f.rodrigues Dec 24 '14 at 18:26
• They should be stored apart from a cell instance. I would just put them as global variables for simplicity. Also, lists are fine, they're pretty much the equivalent and I'm not sure that would impact the performance significantly. – Santiago Dec 24 '14 at 20:25
• updated the question with the results. Thanks for the input, it didn't improved the time, but at last I learned a bit about bitwise operations. – f.rodrigues Dec 25 '14 at 0:45