# CSPs with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations

## TL;DR

This paper investigates the complexity of Boolean CSPs with global modular constraints, classifying when these problems are solvable or hard, and establishing connections to polynomial representations, coding theory, and algebraic complexity.

## Contribution

It provides a classification of the complexity of certain CSPs with modular constraints and links these problems to polynomial representations and coding theory, revealing new algebraic insights.

## Key findings

- Classified moduli M for polynomial-time solvability of CSPs
- Connected HORN-SAT and LIN-2 problems to polynomial covering and sparsity
- Established algebraic framework linking complexity and polynomial representations

## Abstract

We study the complexity of Boolean constraint satisfaction problems (CSPs) when the assignment must have Hamming weight in some congruence class modulo M, for various choices of the modulus M. Due to the known classification of tractable Boolean CSPs, this mainly reduces to the study of three cases: 2-SAT, HORN-SAT, and LIN-2 (linear equations mod 2). We classify the moduli M for which these respective problems are polynomial time solvable, and when they are not (assuming the ETH). Our study reveals that this modular constraint lends a surprising richness to these classic, well-studied problems, with interesting broader connections to complexity theory and coding theory. The HORN-SAT case is connected to the covering complexity of polynomials representing the NAND function mod M. The LIN-2 case is tied to the sparsity of polynomials representing the OR function mod M, which in turn has connections to modular weight distribution properties of linear codes and locally decodable codes. In both cases, the analysis of our algorithm as well as the hardness reduction rely on these polynomial representations, highlighting an interesting algebraic common ground between hard cases for our algorithms and the gadgets which show hardness. These new complexity measures of polynomial representations merit further study. The inspiration for our study comes from a recent work by N\"agele, Sudakov, and Zenklusen on submodular minimization with a global congruence constraint. Our algorithm for HORN-SAT has strong similarities to their algorithm, and in particular identical kind of set systems arise in both cases. Our connection to polynomial representations leads to a simpler analysis of such set systems, and also sheds light on (but does not resolve) the complexity of submodular minimization with a congruency requirement modulo a composite M.