A Modified Mnemonic Enhancement Optimization Method for Solving Parametric Nonlinear Programming Problems

被引：1
|
作者：
Wang, Zhiqiang [1 ]
Shao, Zhijiang [1 ]
Fang, Xueyi [1 ]
Chen, Weifeng [1 ]
Wan, Jiaona [1 ]
机构：
[1] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China
来源：
49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2010年
关键词：
INTERIOR-POINT ALGORITHM; WARM START;
D O I：
10.1109/CDC.2010.5717333
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A mnemonic enhancement optimization framework based on radial basis function (RBF-MEO), which is concerned with the application of RBF interpolation for generation of starting points in parametric nonlinear optimization, is studied in this work. Some theories of interior point algorithm support that the RBF-MEO method is very suitable for collaborating with interior point solvers, such as IPOPT. Numerical experiments illustrate that good accuracy and high rate of convergence are obtained by IPOPT with RBF-MEO.
引用
收藏
页码：2210 / 2214
页数：5
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