Towards Scalable Semidefinite Programming: Optimal Metric ADMM with A Worst-case Performance Guarantee

Abstract

Despite the numerous uses of semidefinite programming (SDP) and its universal solvability via interior point methods (IPMs), it is rarely applied to practical large-scale problems. This mainly owes to the computational cost of IPMs that increases in a bad exponential way with the data size. While first-order algorithms such as ADMM can alleviate this issue, but the scalability improvement appears far not enough. In this work, we aim to achieve extra acceleration for ADMM by appealing to a non-Euclidean metric space, while maintaining everything in closed-form expressions. The efficiency gain comes from the extra degrees of freedom of a variable metric compared to a scalar step-size, which allows us to capture some additional ill-conditioning structures. On the application side, we consider the quadratically constrained quadratic program (QCQP), which naturally appears in an SDP form after a dualization procedure. This technique, known as semidefinite relaxation, has important uses across different fields, particularly in wireless communications. Numerically, we observe that the scalability property is significantly improved. Depending on the data generation process, the extra acceleration can easily surpass the scalar-parameter efficiency limit, and the advantage is rapidly increasing as the data conditioning becomes worse.