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Structural and Multidisciplinary Optimization
Erschienen in: Structural and Multidisciplinary Optimization 6/2014

01.12.2014 | RESEARCH PAPER

A feasible direction interior point algorithm for nonlinear semidefinite programming

verfasst von: Miguel Aroztegui, José Herskovits, Jean Rodolphe Roche, Elmer Bazán

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2014

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Abstract

We present a new algorithm for nonlinear semidefinite programming, based on the iterative solution in the primal and dual variables of Karush-Kuhn-Tucker optimality conditions, which generates a feasible decreasing sequence. At each iteration, two linear systems with the same matrix are solved to compute a feasible descent direction and then an inexact line search is performed in order to determinate the new iterate. Feasible iterates are essential in applications where feasibility is required to compute some of the involved functions. A proof of global convergence to a stationary point is given. Several numerical tests involving nonlinear programming problems with linear or nonlinear matrix inequality constraints are described. We also solve structural topology optimization problems employing a mathematical model based on semidefinite programming. The results suggest efficiency and high robustness of the proposed method.
Vorheriger Artikel Computational optimization of adhesive microstructures based on a nonlinear beam formulation
Nächster Artikel Resolving issues with member buckling in truss topology optimization using a mixed variable approach

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Metadaten
Titel
A feasible direction interior point algorithm for nonlinear semidefinite programming
verfasst von
Miguel Aroztegui
José Herskovits
Jean Rodolphe Roche
Elmer Bazán
Publikationsdatum
01.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 6/2014
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-014-1090-2

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