Sketching Approximability of (Weak) Monarchy Predicates

TL;DR

This paper investigates the limits of sketching algorithms for Boolean CSPs with monarchy-like constraints, revealing both intractability and approximability results across various functions within a unified framework.

Contribution

It introduces a comprehensive analysis of sketching approximability for monarchy and weaker functions, including the first example of asymmetric Boolean CSPs that are sketching approximable.

Findings

Pure monarchy CSPs with ≥5 variables are hard to approximate in o(√n) space.

Certain weaker monarchy functions are approximable with O(log n) space.

First example of sketching approximable asymmetric Boolean CSPs.

Abstract

We analyze the sketching approximability of constraint satisfaction problems on Boolean domains, where the constraints are balanced linear threshold functions applied to literals. In~particular, we explore the approximability of monarchy-like functions where the value of the function is determined by a weighted combination of the vote of the first variable (the president) and the sum of the votes of all remaining variables. The pure version of this function is when the president can only be overruled by when all remaining variables agree. For every $k \geq 5$, we show that CSPs where the underlying predicate is a pure monarchy function on $k$ variables have no non-trivial sketching approximation algorithm in $o(\sqrt{n})$ space. We also show infinitely many weaker monarchy functions for which CSPs using such constraints are non-trivially approximable by $O(\log(n))$ space sketching algorithms. Moreover, we give the first example of sketching approximable asymmetric Boolean CSPs. Our results work within the framework of Chou, Golovnev, Sudan, and Velusamy (FOCS 2021) that characterizes the sketching approximability of all CSPs. Their framework can be applied naturally to get a computer-aided analysis of the approximability of any specific constraint satisfaction problem. The novelty of our work is in using their work to get an analysis that applies to infinitely many problems simultaneously.