A subspace SQP method for equality constrained optimization
In this paper, we present a subspace method for solving large scale nonlinear equality constrained optimization problems. The proposed method is based on a SQP method combined with the limited-memory BFGS update formula. Each subproblem is solved in a theoretically suitable subspace. In the case of...
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| Veröffentlicht in: | Computational optimization and applications 2019-09, Vol.74 (1), p.177-194 |
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| container_title | Computational optimization and applications |
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| creator | Lee, Jae Hwa Jung, Yoon Mo Yuan, Ya-xiang Yun, Sangwoon |
| description | In this paper, we present a subspace method for solving large scale nonlinear equality constrained optimization problems. The proposed method is based on a SQP method combined with the limited-memory BFGS update formula. Each subproblem is solved in a theoretically suitable subspace. In the case of few constraints, we show that our search direction in the subspace is equivalent to that of the SQP subproblem in the full space. In the case of many constraints, we reduce the number of constraints in the subproblem and we show that the solution of the subspace subproblem is a descent direction of a particular exact penalty function. Global convergence properties of the proposed method are given for both cases. Numerical results are given to illustrate the soundness of the proposed model. |
| doi_str_mv | 10.1007/s10589-019-00109-6 |
| format | Article |
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| subjects | Constraints Convex and Discrete Geometry Management Science Mathematical models Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Penalty function Statistics Subspace methods Subspaces |
| title | A subspace SQP method for equality constrained optimization |
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