# I have been brute forcing sudokus wrong (And so might you)

Ever since I created my first sudoku solver, the topic has been stuck in the back of my head. It’s a very interesting problem to optimize, with a lot of logic that isn’t too hard to grasp.

A common theme in different sudoku solver tutorials is that they first write a trivial backtracking solver, and either call it mission accomplished because technically you are getting the result at some point, or declare the backtracker a last resort because it’s too slow to be useful, and go on to implement much more complicated logical strategies.

Granted, this might be mostly because these tutorials are often more about learning recursion, backtracking, maybe python list comprehensions and the like. But I think it does the backtracking solver a disservice. Because just because you are checking every possible solution, doesn’t mean we aren’t allowed to think about the problem and improve our application of brute force.

Typically, code in these tutorials ends up looking something like this:

```
def solve(board):
free_field = first_free_field(board)
if not find:
return True # No more empty fields -> Done
row, col = free_field
for i in possible_values(board, row, col):
board[row][col] = i
if solve(board):
return True
board[row][col] = 0
return False # We tried all possible values and none worked
def first_free_field(board):
for row in range(9):
for col in range(9):
if board[i][j] == 0:
return (i, j)
return None
def possible_values(board):
# Omitted, not important for us
```

They iterate left to right, top to bottom. While watching a youtube video about such a solver I got an idea for something that might possibly speed up the bruteforce solver a bit: Maybe the order in which to solve the fields mattered.

I tried it out and was stunned. It suddenly rivaled all logical solvers I’ve written.

A traditional bruteforce algorithm | The optimized bruteforce algorithm |
---|---|

(video size 22.3MiB, 15:14 min) | (video size 0.8MiB, 0:18 min) |

Iterations: 54843 | Iterations: 1067 |

The optimization is quite simple: Solve the field with the least remaining possible numbers first. Now, you might have one of two reactions: “Duh.” or

# Aren’t you just swapping factors around?

That was the reaction I got when I made that suggestion under said video. Basically, how do I know if this solution is actually faster, and doesn’t just happen to pick a better path in my tests by sheer luck? But it’s not, because the order in which you solve a sudoku influences the values that are possible in all other fields.

A basic optimization you will often find is to first fill in fields that only have one possible value. This speeds up solving sudokus considerably, and it just comes for free with this change of order. Fields that only have one possible value are solved first, and since they only have one possibility, there is nothing to backtrack. Except it happens with every guess of the backtracker, not just once at the beginning.

Fields in Sudoku are not independent of one another. Since every field solved reduces the number of possibilities in the fields after it, fields with a lot of possible values early on, which are the bane of backtracking, just never happen. So we are not just swapping factors.

In the example shown above, the primitive solver starts with solving the cell in column 1, row 1. It has 5 possible values, so each guess for that field has a chance of 20%. Meanwhile, the improved solver first picks col 5 row 2, with two possible values. It then, across the entire solving process, never encounters a single field with more than two possible values. All guesses are at worst 50/50, and the chance that our first two fields are correct is already higher than that the first guess of the primitive solver is correct.

There is another way to definitively prove that they are different. There are puzzles out there that are designed to be difficult to brute force. The basic concept is to design the puzzle in a way that the correct solution is the one a solver will pick last. We can simulate such a puzzle by just removing the success condition from our solver. This way we have created the absolute worst case for the solver. And if the solvers were just iterating over the same tree of possibilities just restructured a bit, we would expect the number of iterations to be similar.

Using the same puzzle as before, we count the number the solve function is called:

- The traditional version calls
`solve`

84015 times. - The optimized version calls
`solve`

1429 times.

Quite the difference.

# Just look at it

It’s also just interesting to look at the visual difference between the two solvers. The standard solver slowly and steadily claws its way forward, and occasionally looses huge chunks and starts over again.

The guesses of the optimized solver tend to trigger long chains of other fields only being able to have one value that then suddenly collapse as it finds a contradiction, so it gets quite deep into solving before it notices any issues. It’s also not quite so all over the place as one might expect. If you fill in a field, you are often reducing possible values in other fields in the same column/row/block, so it often fills out one of these neighborhoods at a time before moving on.

# Going even further

This is not where the backtracking has to stop. Some solving algorithms in sudoku don’t directly reveal the value of a field, but rather reduce the possible values of fields. So if you don’t compute the possible values for each field every time like I have implied in the example above, but keep track of them separately, you can add smart solvers into the backtracking algorithm, instead of using one or the other.

# TLDR

When your algorithm is slow, it might be worth considering if there are ways to improve it, rather than just calling it a lost cause and doing something completely different.

# A terrible anecdote to end on

The examples in this post where made with my program ku as a basis, and it implements essentially the algorithm we have discussed here. But I don’t know how to render videos from rust, or even just rasterize images. I’m sure there is a crate that would have worked just fine, but what I did instead was to export tens of thousands of SVG files, since those are plain text and I know how to export those from rust.

Then I’ve converted all of these SVGs into PNGs using Inkscape’s command line features, and then stitched them all together with ffmpeg.