A Primal-Dual Framework for Symmetric Cone Programming

TL;DR

This paper presents a primal-dual framework for symmetric cone programming that unifies various optimization models, offers nearly linear time algorithms, and demonstrates efficiency through geometric problem applications and parallelization.

Contribution

It extends primal-dual methods to symmetric cone programs, including SOCPs and mixed SCPs, with nearly linear time complexity and parallelization capabilities.

Findings

Algorithms solve geometric problems efficiently in nearly linear time.

Parallel implementation on GPU yields substantial speedups.

Framework unifies and extends existing optimization methods.

Abstract

In this paper, we introduce a primal-dual algorithmic framework for solving Symmetric Cone Programs (SCPs), a versatile optimization model that unifies and extends Linear, Second-Order Cone (SOCP), and Semidefinite Programming (SDP). Our work generalizes the primal-dual framework for SDPs introduced by Arora and Kale, leveraging a recent extension of the Multiplicative Weights Update method (MWU) to symmetric cones. Going beyond existing works, our framework can handle SOCPs and mixed SCPs, exhibits nearly linear time complexity, and can be effectively parallelized. To illustrate the efficacy of our framework, we employ it to develop approximation algorithms for two geometric optimization problems: the Smallest Enclosing Sphere problem and the Support Vector Machine problem. Our theoretical analyses demonstrate that the two algorithms compute approximate solutions in nearly linear running time and with parallel depth scaling polylogarithmically with the input size. We compare our algorithms against CGAL as well as interior point solvers applied to these problems. Experiments show that our algorithms are highly efficient when implemented on a CPU and achieve substantial speedups when parallelized on a GPU, allowing us to solve large-scale instances of these problems.