An $\widetilde{\mathcal{O}}(m/\varepsilon^{3.5})$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints

TL;DR

This paper introduces a faster algorithm for solving semidefinite programs with diagonal constraints, significantly improving the runtime over previous methods and enabling efficient approximation of Max-Cut solutions.

Contribution

The paper presents a novel $ ilde{O}(m/ ext{epsilon}^{3.5})$-time algorithm for SDPs with diagonal constraints, improving the previous $ ilde{O}(m/ ext{epsilon}^{4.5})$ bound using stochastic variance reduction.

Findings

Achieved faster SDP solving with improved runtime complexity.

Applied the algorithm to Max-Cut, obtaining near-optimal cuts efficiently.

Demonstrated the effectiveness of variance reduction in SDP algorithms.

Abstract

We study semidefinite programs with diagonal constraints. This problem class appears in combinatorial optimization and has a wide range of engineering applications such as in circuit design, channel assignment in wireless networks, phase recovery, covariance matrix estimation, and low-order controller design. In this paper, we give an algorithm to solve this problem to $\varepsilon$-accuracy, with a run time of $\widetilde{\mathcal{O}}(m/\varepsilon^{3.5})$, where $m$ is the number of non-zero entries in the cost matrix. We improve upon the previous best run time of $\widetilde{\mathcal{O}}(m/\varepsilon^{4.5})$ by Arora and Kale. As a corollary of our result, given an instance of the Max-Cut problem with $n$ vertices and $m \gg n$ edges, our algorithm when applied to the standard SDP relaxation of Max-Cut returns a $(1 - \varepsilon)\alpha_{GW}$ cut in time $\widetilde{\mathcal{O}}(m/\varepsilon^{3.5})$, where $\alpha_{GW} \approx 0.878567$ is the Goemans-Williamson approximation ratio. We obtain this run time via the stochastic variance reduction framework applied to the Arora-Kale algorithm, by constructing a constant-accuracy estimator to maintain the primal iterates.