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arXiv · 2501.14191

Optimal Preconditioning for Online Quadratic Cone Programming

Abhinav G. Kamath·Purnanand Elango·Behçet Açıkmeşe
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Abstract

First-order conic optimization solvers are sensitive to problem conditioning and typically perform poorly in the face of ill-conditioned problem data. To mitigate this, we propose an approach to preconditioning--the hypersphere preconditioner--for a class of quadratic cone programs (QCPs), i.e., conic optimization problems with a quadratic objective function, wherein the objective function is strongly convex and possesses a certain structure. This approach lends itself to factorization-free, customizable, first-order conic optimization for online applications wherein the solver is called repeatedly to solve problems of the same size/structure, but with changing problem data. We demonstrate the efficacy of our approach on numerical convex and nonconvex trajectory optimization examples, using a first-order conic optimizer under the hood.

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Abhinav G. Kamath, Purnanand Elango, Behçet Açıkmeşe. 2025-01-24. Optimal Preconditioning for Online Quadratic Cone Programming. https://arxiv.org/abs/2501.14191

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