Optimizing Sudoku
Advisor(s)
Ian Kropp
Confirmation
1
Document Type
Poster
Location
McIntosh Activities Room
Start Date
19-4-2024 12:00 PM
End Date
19-4-2024 12:50 PM
Abstract
Sudoku puzzles have long intrigued enthusiasts and mathematicians alike due to their unique combination of logic and pattern recognition. In this presentation, I explore a novel approach to solving Sudoku puzzles using optimization techniques implemented with the PyMOO library in Python.
I frame the Sudoku puzzle as a matrix and leverage linear algebra operations to efficiently check for solutions. My methodology focuses on optimizing the process of puzzle-solving, aiming to minimize computational complexity while ensuring accurate results. Through the utilization of PyMOO's powerful optimization capabilities, I demonstrate how our approach can efficiently tackle Sudoku puzzles of varying difficulties.
Recommended Citation
Spinner, Elijah K., "Optimizing Sudoku" (2024). ONU Student Research Colloquium. 36.
https://digitalcommons.onu.edu/student_research_colloquium/2024/Posters/36
Level of Access
Restricted to ONU Community
Restricted
Available to ONU community via local IP address and ONU login.
Optimizing Sudoku
McIntosh Activities Room
Sudoku puzzles have long intrigued enthusiasts and mathematicians alike due to their unique combination of logic and pattern recognition. In this presentation, I explore a novel approach to solving Sudoku puzzles using optimization techniques implemented with the PyMOO library in Python.
I frame the Sudoku puzzle as a matrix and leverage linear algebra operations to efficiently check for solutions. My methodology focuses on optimizing the process of puzzle-solving, aiming to minimize computational complexity while ensuring accurate results. Through the utilization of PyMOO's powerful optimization capabilities, I demonstrate how our approach can efficiently tackle Sudoku puzzles of varying difficulties.