An Algorithmic Blend of LPs and Ring Equations for Promise CSPs

Abstract

Promise CSPs are a relaxation of constraint satisfaction problems where the goal is to find an assignment satisfying a relaxed version of the constraints. Several well-known problems can be cast as promise CSPs including approximate graph coloring, discrepancy minimization, and interesting variants of satisfiability. Similar to CSPs, the tractability of promise CSPs can be tied to the structure of operations on the solution space called polymorphisms, though in the promise world these operations are much less constrained. Under the thesis that non-trivial polymorphisms govern tractability, promise CSPs therefore provide a fertile ground for the discovery of novel algorithms. In previous work, we classified Boolean promise CSPs when the constraint predicates are symmetric. In this work, we vastly generalize these algorithmic results. Specifically, we show that promise CSPs that admit a family of "regional-periodic" polymorphisms are in P, assuming that determining which region a point is in can be computed in polynomial time. Such polymorphisms are quite general and are obtained by gluing together several functions that are periodic in the Hamming weights in different blocks of the input. Our algorithm is based on a novel combination of linear programming and solving linear systems over rings. We also abstract a framework based on reducing a promise CSP to a CSP over an infinite domain, solving it there, and then rounding the solution to an assignment for the promise CSP instance. The rounding step is intimately tied to the family of polymorphisms and clarifies the connection between polymorphisms and algorithms in this context. As a key ingredient, we introduce the technique of finding a solution to a linear program with integer coefficients that lies in a different ring (such as $\mathbb Z[\sqrt{2}]$) to bypass ad-hoc adjustments for lying on a rounding boundary.