Weakening Assumptions for Deterministic Subexponential Time Non-Singular Matrix Completion

TL;DR

This paper advances deterministic algorithms for low-degree non-singular matrix completion by weakening assumptions and exploring hardness-randomness tradeoffs, with implications for circuit complexity and VP vs VNP separation.

Contribution

It introduces a weaker assumption based on determinantal complexity for deterministic subexponential time matrix completion and links explicit multilinear polynomial families to efficient generators for NSMC.

Findings

Deterministic subexponential time algorithm for low-degree NSMC under weaker assumptions.

Equivalence between explicit multilinear polynomial families with high determinantal complexity and efficient NSMC generators.

Progress towards separating VP and VNP via hardness assumptions related to the permanent.

Abstract

In (Kabanets, Impagliazzo, 2004) it is shown how to decide the circuit polynomial identity testing problem (CPIT) in deterministic subexponential time, assuming hardness of some explicit multilinear polynomial family for arithmetical circuits. In this paper, a special case of CPIT is considered, namely low-degree non-singular matrix completion (NSMC). For this subclass of problems it is shown how to obtain the same deterministic time bound, using a weaker assumption in terms of determinantal complexity. Hardness-randomness tradeoffs will also be shown in the converse direction, in an effort to make progress on Valiant's VP versus VNP problem. To separate VP and VNP, it is known to be sufficient to prove that the determinantal complexity of the m-by-m permanent is $m^{\omega(\log m)}$. In this paper it is shown, for an appropriate notion of explicitness, that the existence of an explicit multilinear polynomial family with determinantal complexity m^{\omega(\log m)}$ is equivalent to the existence of an efficiently computable generator $G_n$ for multilinear NSMC with seed length $O(n^{1/\sqrt{\log n}})$. The latter is a combinatorial object that provides an efficient deterministic black-box algorithm for NSMC. ``Multilinear NSMC'' indicates that $G_n$ only has to work for matrices $M(x)$ of $poly(n)$ size in $n$ variables, for which $det(M(x))$ is a multilinear polynomial.