Optimal Preconditioning for Online Quadratic Cone Programming

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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.
Funding Information
  • National Aeronautics and Space Administration (NNX17AH02A)
  • Air Force Office of Scientific Research (FA9550-20-1-005)

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