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A sequential semidefinite programming method and an application in passive reduced-order modeling

2005/03/08 by Roland W. Freund, Freund, Roland W., Florian Jarre +3
Engineering · Mathematics · #Advanced Control Systems Optimization #Advanced Optimization Algorithms Research #Control Systems and Identification #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.math/0503135

openalex publication_date 2005/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the solution of nonlinear programs with nonlinear semidefiniteness constraints. The need for an efficient exploitation of the cone of positive semidefinite matrices makes the solution of such nonlinear semidefinite programs more complicated than the solution of standard nonlinear programs. In particular, a suitable symmetrization procedure needs to be chosen for the linearization of the complementarity condition. The choice of the symmetrization procedure can be shifted in a very natural way to certain linear semidefinite subproblems, and can thus be reduced to a well-studied problem. The resulting sequential semidefinite programming (SSP) method is a generalization of the well-known SQP method for standard nonlinear programs. We present a sensitivity result for nonlinear semidefinite programs, and then based on this result, we give a self-contained proof of local quadratic convergence of the SSP method. We also describe a class of nonlinear semidefinite programs that arise in passive reduced-order modeling, and we report results of some numerical experiments with the SSP method applied to problems in that class.

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