Approximation algorithms for satisfiable and nearly satisfiable ordering CSPs

2026-03-31

AI Summary

This work addresses the long-standing lack of a systematic understanding of approximation algorithms for ordering constraint satisfaction problems (ordering CSPs) in both satisfiable and nearly satisfiable regimes. We propose a unified framework that relaxes the original problem into an auxiliary ordering CSP and then applies a randomized transformation to produce a feasible solution, thereby reducing algorithm design to optimizing such transformations. We show that the performance of this framework is fully characterized by a class of “strong IDU transformations,” and that optimizing them reduces to an explicit low-dimensional problem whose dimension depends only on the maximum arity $k$ of the predicates and the desired accuracy $\delta$. Consequently, for any finite ordering constraint language, we can compute—in time depending solely on $k$ and $\delta$—a strong IDU transformation achieving an approximation ratio within $\delta$ of the framework’s optimum, yielding the first nontrivial approximation guarantees for a broad class of ordering predicates.

Abstract

We study approximation algorithms for satisfiable and nearly satisfiable instances of ordering constraint satisfaction problems (ordering CSPs). Ordering CSPs arise naturally in ranking and scheduling, yet their approximability remains poorly understood beyond a few isolated cases. We introduce a general framework for designing approximation algorithms for ordering CSPs. The framework relaxes an input instance to an auxiliary ordering CSP, solves the relaxation, and then applies a randomized transformation to obtain an ordering for the original instance. This reduces the search for approximation algorithms to an optimization problem over randomized transformations. Our main technical contribution is to show that the power of this framework is captured by a structured class of transformations, which we call strong IDU transformations: every transformation used in the framework can be replaced by a strong IDU transformation without weakening the resulting approximation guarantee. We then classify strong IDU transformations and show that optimizing over them reduces to an explicit optimization problem whose dimension depends only on the maximum predicate arity $k$ and the desired precision $δ> 0$. As a consequence, for any finite ordering constraint language, we can compute a strong IDU transformation whose guarantee is within $δ$ of the best guarantee achievable by the framework, in time depending only on $k$ and $δ$. The framework applies broadly and yields nontrivial approximation guarantees for a wide class of ordering predicates.

Problem

• ordering CSPs
• approximation algorithms
• satisfiable instances
• nearly satisfiable instances
• constraint satisfaction problems

Innovation

• ordering CSPs
• approximation algorithms
• strong IDU transformations
• randomized rounding
• constraint satisfaction problems