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Main Authors: Lourenço, Bruno F., Fukuda, Ellen H., Fukushima, Masao
Format: Preprint
Published: 2017
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Online Access:https://arxiv.org/abs/1701.05298
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author Lourenço, Bruno F.
Fukuda, Ellen H.
Fukushima, Masao
author_facet Lourenço, Bruno F.
Fukuda, Ellen H.
Fukushima, Masao
contents In this work we are interested in nonlinear symmetric cone problems (NSCPs), which contain as special cases nonlinear semidefinite programming, nonlinear second order cone programming and the classical nonlinear programming problems. We explore the possibility of reformulating NSCPs as common nonlinear programs (NLPs), with the aid of squared slack variables. Through this connection, we show how to obtain second order optimality conditions for NSCPs in an easy manner, thus bypassing a number of difficulties associated to the usual variational analytical approach. We then discuss several aspects of this connection. In particular, we show a "sharp" criterion for membership in a symmetric cone that also encodes rank information. Also, we discuss the possibility of importing convergence results from nonlinear programming to NSCPs, which we illustrate by discussing a simple augmented Lagrangian method for nonlinear symmetric cones. We show that, employing the slack variable approach, we can use the results for NLPs to prove convergence results, thus extending a special case (i.e., the case with strict complementarity) of an earlier result by Sun, Sun and Zhang for nonlinear semidefinite programs.
format Preprint
id arxiv_https___arxiv_org_abs_1701_05298
institution arXiv
publishDate 2017
record_format arxiv
spellingShingle Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method
Lourenço, Bruno F.
Fukuda, Ellen H.
Fukushima, Masao
Optimization and Control
90C46, 90C30
In this work we are interested in nonlinear symmetric cone problems (NSCPs), which contain as special cases nonlinear semidefinite programming, nonlinear second order cone programming and the classical nonlinear programming problems. We explore the possibility of reformulating NSCPs as common nonlinear programs (NLPs), with the aid of squared slack variables. Through this connection, we show how to obtain second order optimality conditions for NSCPs in an easy manner, thus bypassing a number of difficulties associated to the usual variational analytical approach. We then discuss several aspects of this connection. In particular, we show a "sharp" criterion for membership in a symmetric cone that also encodes rank information. Also, we discuss the possibility of importing convergence results from nonlinear programming to NSCPs, which we illustrate by discussing a simple augmented Lagrangian method for nonlinear symmetric cones. We show that, employing the slack variable approach, we can use the results for NLPs to prove convergence results, thus extending a special case (i.e., the case with strict complementarity) of an earlier result by Sun, Sun and Zhang for nonlinear semidefinite programs.
title Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method
topic Optimization and Control
90C46, 90C30
url https://arxiv.org/abs/1701.05298