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A Sparsity Preserving Convexification Procedure for Indefinite Quadratic Programs Arising in Direct Optimal Control

Robin Verschueren (robin.verschueren***at***gmail.com)
Mario Zanon (zmario***at***chalmers.se)
Rien Quirynen (rien.quirynen***at***esat.kuleuven.be)
Moritz Diehl (moritz.diehl***at***imtek.uni-freiburg.de)

Abstract: Quadratic programs (QP) with an indefinite Hessian matrix arise naturally in some direct optimal control methods, e.g. as subproblems in a sequential quadratic programming (SQP) scheme. Typically, the Hessian is approximated with a positive definite matrix to ensure having a unique solution; such a procedure is called \emph{regularization}. We present a novel regularization method tailored for QPs with optimal control structure. Our approach exhibits three main advantages. First, when the QP satisfies a second order sufficient condition (SOSC) for optimality, the primal solution of the original and the regularized problem are equal. In addition, the algorithm recovers the dual solution in a convenient way. Secondly, and more importantly, the regularized Hessian bears the same sparsity structure as the original one. This allows for the use of efficient structure-exploiting QP solvers. As a third advantage, the regularization can be performed with a computational complexity that scales linearly in the length of the control horizon. We showcase the properties of our regularization algorithm on a numerical example for nonlinear optimal control. The results are compared to other sparsity preserving regularization methods.

Keywords: optimal control, SQP, regularization

Category 1: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

Citation: SIAM Journal of Optimization, accepted for publication

Download: [PDF]

Entry Submitted: 06/24/2016
Entry Accepted: 06/24/2016
Entry Last Modified: 04/28/2017

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