An interior-point algorithm for nonconvex nonlinear programming

被引：344

作者：

Vanderbei, RJ ^{[1
]}

Shanno, DF

机构：

[1] Princeton Univ, Princeton, NJ 08544 USA

[2] Rutgers State Univ, Piscataway, NJ 08855 USA

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 1999年 / 13卷 / 1-3期

关键词：

nonlinear programming; interior-point methods; nonconvex optimization;

D O I：

10.1023/A:1008677427361

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：

The paper describes an interior-point algorithm for nonconvex nonlinear programming which is a direct extension of interior-point methods for linear and quadratic programming. Major modifications include a merit function and an altered search direction to ensure that a descent direction for the merit function is obtained. Preliminary numerical testing indicates that the method is robust. Further, numerical comparisons with MINOS and LANCELOT show that the method is efficient, and has the promise of greatly reducing solution times on at least some classes of models.

引用

页码：231 / 252

页数：22

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