Calculating Radius of Robust Feasibility of Uncertain Linear Conic Programs via Semidefinite Programs

TL;DR

This paper develops methods to compute the maximum uncertainty radius for which uncertain linear conic programs remain feasible, using semidefinite programming techniques, thereby advancing robust optimization analysis.

Contribution

It introduces computable bounds and exact formulas for the radius of robust feasibility in uncertain linear conic programs via semidefinite programs, including specific classes like SDPs and SVMs.

Findings

Provided bounds for the radius of robust feasibility.

Derived exact formulas for linear programs.

Extended methods to second-order cone programs and SVMs.

Abstract

The radius of robust feasibility provides a numerical value for the largest possible uncertainty set that guarantees robust feasibility of an uncertain linear conic program. This determines when the robust feasible set is non-empty. Otherwise the robust counterpart of an uncertain program is not well-defined as a robust optimization problem. In this paper, we address a key fundamental question of robust optimization: How to compute the radius of robust feasibility of uncertain linear conic programs, including linear programs? We first provide computable lower and upper bounds for the radius of robust feasibility for general uncertain linear conic programs under the commonly used ball uncertainty set. We then provide important classes of linear conic programs where the bounds are calculated by finding the optimal values of related semidefinite linear programs (SDPs), among them uncertain SDPs, uncertain second-order cone programs and uncertain support vector machine problems. In the case of an uncertain linear program, the exact formula allows us to calculate the radius by finding the optimal value of an associated second-order cone program.