A globally convergent trust-region SQP method without a penalty function for nonlinearly constrained optimization

Hiroshi Yamashita (hymsi.co.jp)
Hiroshi Yabe (yabers.kagu.tus.ac.jp)

Abstract: In this paper, we propose a new trust-region SQP method, which uses no penalty function, for solving nonlinearly constrained optimization problem. Our method consists of alternate two phases. Specifically, we alternately proceed the feasibility restoration phase and the objective function minimization phase. The global convergence property of the proposed method is shown.

Keywords: nonlinear optimization, trust-region SQP method without a penalty function, global convergence

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Cooperative Research Report 168 "OPTIMIZATION: Modeling and Algorithms 17", The Institute of Statistical Mathematics, Tokyo, Japan, February 2004

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Entry Submitted: 06/18/2007
Entry Accepted: 06/18/2007
Entry Last Modified: 07/25/2007

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