anaugmentedlagrangiantrustregionmethodwithabiobjectstrategy
文献类型：期刊论文
| 作者 | Kou Caixia4; Chen Zhongwen1; Dai Yuhong3 ; Han Haifei2
|
| 刊名 | journalofcomputationalmathematics
 |
| 出版日期 | 2018 |
| 卷号 | 36期号:3页码:331 |
| ISSN号 | 0254-9409 |
| 英文摘要 | An augmented Lagrangian trust region method with a bi-object strategy is proposed for solving nonlinear equality constrained optimization, which falls in between penalty-type methods and penalty-free ones. At each iteration, a trial step is computed by minimizing a quadratic approximation model to the augmented Lagrangian function within a trust region. The model is a standard trust region subproblem for unconstrained optimization and hence can efficiently be solved by many existing methods. To choose the penalty parameter, an auxiliary trust region subproblem is introduced related to the constraint violation. It turns out that the penalty parameter need not be monotonically increasing and will not tend to infinity. A bi-object strategy, which is related to the objective function and the measure of constraint violation, is utilized to decide whether the trial step will be accepted or not. Global convergence of the method is established under mild assumptions. Numerical experiments are made, which illustrate the efficiency of the algorithm on various difficult situations. |
| 资助项目 | [Chinese NSF] ; [National 973 Program of China] |
| 语种 | 英语 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/42321]  |
| 专题 | 计算数学与科学工程计算研究所 |
| 作者单位 | 1.School of Mathematical Sciences,Soochow University 2.苏州大学 3.中国科学院数学与系统科学研究院 4.北京邮电大学 |
| 推荐引用方式 GB/T 7714 | Kou Caixia,Chen Zhongwen,Dai Yuhong,et al. anaugmentedlagrangiantrustregionmethodwithabiobjectstrategy[J]. journalofcomputationalmathematics,2018,36(3):331. |
| APA | Kou Caixia,Chen Zhongwen,Dai Yuhong,&Han Haifei.(2018).anaugmentedlagrangiantrustregionmethodwithabiobjectstrategy.journalofcomputationalmathematics,36(3),331. |
| MLA | Kou Caixia,et al."anaugmentedlagrangiantrustregionmethodwithabiobjectstrategy".journalofcomputationalmathematics 36.3(2018):331. |
入库方式： OAI收割
来源：数学与系统科学研究院
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