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pro vyhledávání: '"SONGQIANG QIU"'
Autor:
Songqiang Qiu
Publikováno v:
Optimization. 72:551-579
Autor:
Songqiang Qiu, Zhongwen Chen
Publikováno v:
Journal of Industrial & Management Optimization. 16:2675-2701
In this paper, we devise an adaptively regularized SQP method for equality constrained optimization problem that is resilient to constraint degeneracy, with a relatively small departure from classical SQP method. The main feature of our method is an
Autor:
Songqiang Qiu
Publikováno v:
Computational Optimization and Applications. 73:957-996
We herein present a stabilized sequential programming method for equality constrained programming. The proposed method uses the concepts of proximal point methods and primal-dual regularization. A sequence of regularized problems are approximately so
Autor:
Songqiang Qiu, Zhongwen Chen
Publikováno v:
International Journal of Computer Mathematics. 95:2471-2495
In this paper, we present an interior point method for nonlinear programming that avoids the use of penalty function or filter. We use an adaptively perturbed primal dual interior point fra...
Autor:
Zhongwen Chen, Songqiang Qiu
Publikováno v:
Acta Applicandae Mathematicae. 142:39-60
In this paper, we propose an algorithm for the solution of nonlinear constrained programming. This algorithm does not use any penalty function or a filter. Instead, it uses the idea of maximal constraint violation to guarantee global convergence. The
Autor:
Zhongwen Chen, Songqiang Qiu
Publikováno v:
Computers & Mathematics with Applications. 65(4):589-608
We present a class of trust region algorithms that do not use any penalty function or a filter for nonlinear equality constrained optimization. In each iteration, the infeasibility is controlled by a progressively decreasing upper limit and trial ste
Publikováno v:
Journal of Industrial & Management Optimization. 9:391-409
A penalty-free method is introduced for solving nonlinear programming with nonlinear equality constraints. This method does not use any penalty function, nor a filter. It uses trust region technique to compute trial steps. By comparing the measures o
Autor:
Zhongwen Chen, Songqiang Qiu
Publikováno v:
Applied Mathematics and Computation. 218:11089-11099
In this paper, we propose a new penalty-free-type method for nonlinear equality constrained problems. The new algorithm uses trust region framework and feasibility safeguarding technique. Moreover, it has no choice of penalty parameter and penalty fu
Autor:
Zhongwen Chen, Songqiang Qiu
We present an interior point method for nonlinear programming in this paper. This method follows Byrd and Omojokun’s idea of step decomposition, which splits the trial step into a normal step and a tangential step. The method employs a new idea of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e7961ec46db1009cbbf380f5e90ab1b
http://arxiv.org/abs/1509.02585
http://arxiv.org/abs/1509.02585
Autor:
Songqiang Qiu, Zhongwen Chen
Publikováno v:
Applied Mathematical Modelling. (7):3201-3216
We present a class of trust region algorithms without using a penalty function or a filter for nonlinear inequality constrained optimization and analyze their global and local convergence. In each iteration, the algorithms reduce the value of objecti