Interior-point methods for nonconvex nonlinear programming: Filter methods and merit functions

被引：68

作者：

Benson, HY ^{[1
]}

Vanderbei, RJ

Shanno, DF

机构：

[1] Princeton Univ, Princeton, NJ 08544 USA

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

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2002年 / 23卷 / 02期

基金：

美国国家科学基金会;

关键词：

interior-point methods; nonconvex optimization; nonlinear programming; filter methods;

D O I：

10.1023/A:1020533003783

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：

Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a filter-based approach in an interior-point algorithm. Extensive numerical testing shows that such an approach is more efficient than using a merit function alone.

引用

页码：257 / 272

页数：16

共 11 条

[1]

CONN AR, CONSTRAINED UNCONSTR

[2]

Dolan ED, 2001, BENCHMARKING OPTIMIZ

[3]

Fiacco A.V., 1990, Nonlinear Programming Sequential Unconstrained Minimization Techniques

[4]

FLETCHER R, 1997, NA171 U DUND DEP MAT