A Penalty-Free Method with Trust Region for Nonlinear Semidefinite Programming

Zhongwen Chen () and Shicai Miao ()
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Zhongwen Chen: School of Mathematics Science, Soochow University, Suzhou, 215006, P. R. China
Shicai Miao: School of Mathematics Science, Soochow University, Suzhou, 215006, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2015, vol. 32, issue 01, 1-24

Abstract: In this paper, we propose a class of new penalty-free method, which does not use any penalty function or a filter, to solve nonlinear semidefinite programming (NSDP). So the choice of the penalty parameter and the storage of filter set are avoided. The new method adopts trust region framework to compute a trial step. The trial step is then either accepted or rejected based on the some acceptable criteria which depends on reductions attained in the nonlinear objective function and in the measure of constraint infeasibility. Under the suitable assumptions, we prove that the algorithm is well defined and globally convergent. Finally, the preliminary numerical results are reported.

Keywords: Nonlinear semidefinite programming; penalty-free method; measure of constraint violation; global convergence (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0217595915400060

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