#Algorithms for solving sudoku puzzles code#
#Algorithms for solving sudoku puzzles free#

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#Algorithms for solving sudoku puzzles free#

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A comprehensive dataset ensures that these algorithms are tested against a wide variety of instances, facilitating robust and reliable models. OCR and Sudoku solving algorithms demand diverse and complex data. Lukas is a highly motivated computing science graduate focusing mainly on data mining and machine learning problems but sporting a broader interest in all things computationally elegant.In this tutorial, you will create an automatic Sudoku puzzle solver using OpenCV, Deep Learning, and Optical Character Recognition (OCR). What do you think of this approach? I might implement this later (I might have to brush up on my Python skills). I’m hoping this would give us the best of both worlds. The complex rule thus serves as a ‘last resort’ before the searching strategy is applied. The algorithm should only switch to testing more complex rules when it gets stuck, before it attempts to ‘search’. Long execution times probably occur when ‘searching’ leads to many incorrect assumptions being made and being tested.īased on that insight, I would want to keep the original algorithm intact as much as possible. I reckon that the original algorithm is fast, because it can solve many puzzles without ‘searching’. I would propose an alternative approach to limiting the execution time of the original algorithm by switching after 0.2 seconds. Both probably result in quite some additional computational work. Switching to more complex rules would mean you either start from the beginning of the puzzle (and lose everything you’ve done so far) or re-evaluate previously eliminated numbers (to see if the more complex rule might have been applied there). The main problem with that approach would be that both algorithms check constraints only as options are eliminated. I can see some potential pitfalls, but it should be possible to switch to more complex rules only when needed. Assuming the million random puzzles I tested on accurately represent all sudoku puzzles in existence, your algorithm is simply better suited for that particular use case than my modified version. That being said, the title of my previous post was “solving EVERY sudoku puzzle FASTER”, and that is one of those use cases where I think mean execution time is more important than anything else. I fully agree that the modified version would be better for most use cases. Good to hear you consider the reduction in standard deviation worth the (slight) performance impact. This is in line with Peter’s earlier findings. This seems to indicate that most of these random puzzles are easier to solve save for a few extreme puzzles. For the same puzzles, my modified version required an average of 0.008 seconds per puzzle (124 puzzles per second) with a maximum execution time of merely 5.75 seconds.īoth these averages are far lower than those for the ninety-five difficult puzzles attempted earlier, but the maxima are orders of magnitude higher. Peter’s original algorithm required an average of 0.007 seconds per puzzle (142 puzzles per second) with a maximum execution time of 91.06 seconds. Overnight, one million random puzzles were attempted by the two competing algorithms (999.925 were actually solved). This allowed me to measure execution times more precisely.

#Algorithms for solving sudoku puzzles code#

Secondly, I modified the code so that it uses the timeit module for measuring execution times. This is especially important since Peter’s experiments showed that long execution times (difficult puzzles) are rare but have an extreme impact a few puzzles taking more than a minute to solve. Otherwise one algorithm might, simply by virtue of some serious bad luck, end up with more difficult puzzles. To make this a fair competition, I made some slight modifications.įirstly, I had to ensure that both algorithms were solving the exact same random puzzles. The results were a little unexpected, but not very surprising. So I set out to make a more rigorous analysis of the relative strengths of our approaches using the method for generating random puzzles included in the original code. I had only compared the performance of the two algorithms based on the ninety-five difficult puzzles provided by Peter, but he was not convinced.

So maybe I was jumping to conclusions a little early in the game.Īfter I posted my own modified version of Peter Norvig‘s Sudoku Solver, Peter and I had some brief discussion about my results and conclusions.