Necessary and sufficient conditions for rank-one generated cones

TL;DR

This paper establishes necessary and sufficient conditions for certain convex cones to be rank-one generated, linking these conditions to the exactness of SDP relaxations in quadratic programming, with broad applications.

Contribution

It provides a unified framework for characterizing ROG cones, extending known results, and deriving new inhomogeneous SDP exactness conditions for quadratic programs.

Findings

Identified sufficient conditions for cones to be ROG.

Proved necessity of conditions for two linear matrix inequalities.

Applied results to convex hull descriptions and reformulations.

Abstract

A closed convex conic subset $\mathcal{S}$ of the positive semidefinite (PSD) cone is rank-one generated (ROG) if all of its extreme rays are generated by rank-one matrices. The ROG property of $\mathcal{S}$ is closely related to the exactness of SDP relaxations of nonconvex quadratically constrained quadratic programs (QCQPs) related to $\mathcal{S}$. We consider the case where $\mathcal{S}$ is obtained as the intersection of the PSD cone with finitely many homogeneous linear matrix inequalities and conic constraints and identify sufficient conditions that guarantee that $\mathcal{S}$ is ROG. Our general framework allows us to recover a number of well-known results from the literature. In the case of two linear matrix inequalities, we also establish the necessity of our sufficient conditions. This extends one of the few settings from the literature -- the case of one linear matrix inequality and the S-lemma -- where an explicit characterization for the ROG property exists. Finally, we show how our ROG results on cones can be translated into inhomogeneous SDP exactness results and convex hull descriptions in the original space of a QCQP. We close with a few applications of these results; specifically, we recover the well-known perspective reformulation of a simple mixed-binary set via the ROG toolkit.