Fine-grained Complexity Meets IP = PSPACE

TL;DR

This paper explores the fine-grained complexity of problems in P, establishing equivalences between exact and approximate solutions, and deriving new complexity barriers using interactive proof systems and reductions.

Contribution

It introduces the BP-Pair-Class of problems, showing their equivalence under near-linear reductions and connecting their complexity to NC-SETH assumptions.

Findings

Exact and approximate solutions are equivalent in the BP-Pair-Class.

Solving these problems requires quadratic time under NC-SETH.

Modest algorithm improvements imply NEXP not in non-uniform NC^1.

Abstract

In this paper we study the fine-grained complexity of finding exact and approximate solutions to problems in P. Our main contribution is showing reductions from exact to approximate solution for a host of such problems. As one (notable) example, we show that the Closest-LCS-Pair problem (Given two sets of strings $A$ and $B$, compute exactly the maximum $\textsf{LCS}(a, b)$ with $(a, b) \in A \times B$) is equivalent to its approximation version (under near-linear time reductions, and with a constant approximation factor). More generally, we identify a class of problems, which we call BP-Pair-Class, comprising both exact and approximate solutions, and show that they are all equivalent under near-linear time reductions. Exploring this class and its properties, we also show: $\bullet$ Under the NC-SETH assumption (a significantly more relaxed assumption than SETH), solving any of the problems in this class requires essentially quadratic time. $\bullet$ Modest improvements on the running time of known algorithms (shaving log factors) would imply that NEXP is not in non-uniform $\textsf{NC}^1$. $\bullet$ Finally, we leverage our techniques to show new barriers for deterministic approximation algorithms for LCS. At the heart of these new results is a deep connection between interactive proof systems for bounded-space computations and the fine-grained complexity of exact and approximate solutions to problems in P. In particular, our results build on the proof techniques from the classical IP = PSPACE result.