A Subquadratic Approximation Scheme for Partition

TL;DR

This paper introduces a subquadratic time approximation scheme for the Partition problem, a significant breakthrough for NP-hard problems, achieved by novel reductions and combining techniques from multiple algorithmic fields.

Contribution

It presents the first subquadratic FPTAS for Partition, improving the complexity for approximating NP-hard problems and introduces new methods for reducing problem instances.

Findings

First subquadratic FPTAS for Partition problem.

Improved approximation schemes for 3SUM, (min,+)-convolution, and Tree Sparsity.

Conditional lower bounds suggest breaking the quadratic barrier is unlikely.

Abstract

The subject of this paper is the time complexity of approximating Knapsack, Subset Sum, Partition, and some other related problems. The main result is an $\widetilde{O}(n+1/\varepsilon^{5/3})$ time randomized FPTAS for Partition, which is derived from a certain relaxed form of a randomized FPTAS for Subset Sum. To the best of our knowledge, this is the first NP-hard problem that has been shown to admit a subquadratic time approximation scheme, i.e., one with time complexity of $O((n+1/\varepsilon)^{2-\delta})$ for some $\delta>0$. To put these developments in context, note that a quadratic FPTAS for \partition has been known for 40 years. Our main contribution lies in designing a mechanism that reduces an instance of Subset Sum to several simpler instances, each with some special structure, and keeps track of interactions between them. This allows us to combine techniques from approximation algorithms, pseudo-polynomial algorithms, and additive combinatorics. We also prove several related results. Notably, we improve approximation schemes for 3SUM, (min,+)-convolution, and Tree Sparsity. Finally, we argue why breaking the quadratic barrier for approximate Knapsack is unlikely by giving an $\Omega((n+1/\varepsilon)^{2-o(1)})$ conditional lower bound.