Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems

Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Euiwoong Lee, Debmalya Panigrahi

Keywords: learning-augmented algorithm, approximation algorithm, maximization, CSPs, learning with advice

TL;DR: This paper presents learning-augmented algorithms for maximum cut and other max-CSPs.

Abstract: In recent years, there has been a surge of interest in the use of machine-learned predictions to bypass worst-case lower bounds for classical problems in combinatorial optimization. So far, the focus has mostly been on online algorithms, where information-theoretic barriers are overcome using predictions about the unknown future. In this paper, we consider the complementary question of using learned information to overcome computational barriers in the form of approximation hardness of polynomial-time algorithms for NP-hard (offline) problems. We show that noisy predictions about the optimal solution can be used to break classical hardness results for maximization problems such as the max-cut problem and more generally, maximization versions of constraint satisfaction problems (CSPs).

Primary Area: Optimization (convex and non-convex, discrete, stochastic, robust)

Submission Number: 15642