Interior-point methods for nonconvex nonlinear programming: regularization and warmstarts

被引：39

作者：

Benson, Hande Y. ^{[1
]}

Shanno, David F. ^{[2
]}

机构：

[1] Drexel Univ, Philadelphia, PA 19104 USA

[2] Rutgers State Univ, RUTCOR, New Brunswick, NJ 08903 USA

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2008年 / 40卷 / 02期

基金：

美国国家科学基金会;

关键词：

interior-point methods; nonlinear programming; warmstarting; penalty methods;

D O I：

10.1007/s10589-007-9089-x

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, we investigate the use of an exact primal-dual penalty approach within the framework of an interior-point method for nonconvex nonlinear programming. This approach provides regularization and relaxation, which can aid in solving ill-behaved problems and in warmstarting the algorithm. We present details of our implementation within the LOQO algorithm and provide extensive numerical results on the CUTEr test set and on warmstarting in the context of quadratic, nonlinear, mixed integer nonlinear, and goal programming.

引用

页码：143 / 189

页数：47

共 25 条

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Anitescu M., ANLMCSP7930200

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