Solving SDP Faster: A Robust IPM Framework and Efficient Implementation

TL;DR

This paper presents a new robust interior point method framework that significantly accelerates solving large-scale semidefinite programs, achieving nearly-optimal running times and improving upon previous state-of-the-art solvers.

Contribution

It introduces a novel robust analysis for interior point methods applicable to SDP, enabling faster algorithms with nearly-optimal complexity for dense SDPs.

Findings

SDP can be solved in $m^{ ext{omega}}$ time for $m = ext{Omega}(n^2)$

First nearly-optimal time algorithm for tall dense SDPs

Improves upon previous SDP solvers with new analysis and techniques

Abstract

This paper introduces a new robust interior point method analysis for semidefinite programming (SDP). This new robust analysis can be combined with either logarithmic barrier or hybrid barrier. Under this new framework, we can improve the running time of semidefinite programming (SDP) with variable size $n \times n$ and $m$ constraints up to $\epsilon$ accuracy. We show that for the case $m = \Omega(n^2)$, we can solve SDPs in $m^{\omega}$ time. This suggests solving SDP is nearly as fast as solving the linear system with equal number of variables and constraints. This is the first result that tall dense SDP can be solved in the nearly-optimal running time, and it also improves the state-of-the-art SDP solver [Jiang, Kathuria, Lee, Padmanabhan and Song, FOCS 2020]. In addition to our new IPM analysis, we also propose a number of techniques that might be of further interest, such as, maintaining the inverse of a Kronecker product using lazy updates, a general amortization scheme for positive semidefinite matrices.