A Warm Restart Strategy for Solving Sudoku by Sparse Optimization Methods

TL;DR

This paper introduces a warm restart strategy combined with sparse optimization techniques to improve Sudoku puzzle solving, significantly increasing recovery accuracy from 84% to over 99%.

Contribution

It presents a novel warm restart approach integrated with sparse optimization for Sudoku, enhancing solution accuracy and defining a new difficulty level.

Findings

Recovery rate improved from 84% to over 99%.

Effective for a dataset of Sudoku puzzles.

Demonstrates the advantage of sparse optimization methods.

Abstract

This paper is concerned with the popular Sudoku problem. We proposed a warm restart strategy for solving Sudoku puzzles, based on the sparse optimization technique. Furthermore, we defined a new difficulty level for Sudoku puzzles. The efficiency of the proposed method is tested using a dataset of Sudoku puzzles, and the numerical results show that the accurate recovery rate can be enhanced from 84%+ to 99%+ using the L1 sparse optimization method.