Problem Solving at the Edge of Chaos: Entropy, Puzzles and the Sudoku Freezing Transition

TL;DR

This paper explores the phase transition and critical phenomena in Sudoku puzzles using statistical mechanics, revealing a freezing transition and linking puzzle hardness to entropy and constrainedness.

Contribution

It provides the first description of a Sudoku freezing transition and connects puzzle constrainedness with informational entropy, advancing understanding of puzzle hardness and phase transitions.

Findings

Identifies a phase transition in Sudoku constrainedness.

Shows puzzle hardness peaks at critical clue densities.

Links entropy to the frozen state of variables.

Abstract

Sudoku is a widely popular $\mathcal{NP}$-Complete combinatorial puzzle whose prospects for studying human computation have recently received attention, but the algorithmic hardness of Sudoku solving is yet largely unexplored. In this paper, we study the statistical mechanical properties of random Sudoku grids, showing that puzzles of varying sizes attain a hardness peak associated with a critical behavior in the constrainedness of random instances. In doing so, we provide the first description of a Sudoku \emph{freezing} transition, showing that the fraction of backbone variables undergoes a phase transition as the density of pre-filled cells is calibrated. We also uncover a variety of critical phenomena in the applicability of Sudoku elimination strategies, providing explanations as to why puzzles become boring outside the typical range of clue densities adopted by Sudoku publishers. We further show that the constrainedness of Sudoku puzzles can be understood in terms of the informational (Shannon) entropy of their solutions, which only increases up to the critical point where variables become frozen. Our findings shed light on the nature of the $k$-coloring transition when the graph topology is fixed, and are an invitation to the study of phase transition phenomena in problems defined over \emph{alldifferent} constraints. They also suggest advantages to studying the statistical mechanics of popular $\mathcal{NP}$-Hard puzzles, which can both aid the design of hard instances and help understand the difficulty of human problem solving.