Algorithmic barriers from phase transitions

TL;DR

This paper investigates the fundamental reasons behind the algorithmic barriers in solving random constraint satisfaction problems, revealing a phase transition in the structure of solutions that aligns with the limits of polynomial-time algorithms.

Contribution

It introduces a rigorous technique to analyze the geometry of solution sets, connecting phase transitions to algorithmic hardness in random CSPs.

Findings

Identifies a phase transition in the structure of $k$-colorings of random graphs.

Shows the transition point coincides with the failure of all known polynomial algorithms.

Develops a method to rigorously prove aspects of the 1-step Replica-Symmetry-Breaking hypothesis.

Abstract

For many random Constraint Satisfaction Problems, by now, we have asymptotically tight estimates of the largest constraint density for which they have solutions. At the same time, all known polynomial-time algorithms for many of these problems already completely fail to find solutions at much smaller densities. For example, it is well-known that it is easy to color a random graph using twice as many colors as its chromatic number. Indeed, some of the simplest possible coloring algorithms already achieve this goal. Given the simplicity of those algorithms, one would expect there is a lot of room for improvement. Yet, to date, no algorithm is known that uses $(2-\epsilon) \chi$ colors, in spite of efforts by numerous researchers over the years. In view of the remarkable resilience of this factor of 2 against every algorithm hurled at it, we believe it is natural to inquire into its origin. We do so by analyzing the evolution of the set of $k$-colorings of a random graph, viewed as a subset of $\{1,...,k\}^{n}$, as edges are added. We prove that the factor of 2 corresponds in a precise mathematical sense to a phase transition in the geometry of this set. Roughly, the set of $k$-colorings looks like a giant ball for $k \ge 2 \chi$, but like an error-correcting code for $k \le (2-\epsilon) \chi$. We prove that a completely analogous phase transition also occurs both in random $k$-SAT and in random hypergraph 2-coloring. And that for each problem, its location corresponds precisely with the point were all known polynomial-time algorithms fail. To prove our results we develop a general technique that allows us to prove rigorously much of the celebrated 1-step Replica-Symmetry-Breaking hypothesis of statistical physics for random CSPs.