Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions

被引：0

作者：

Hande Y. Benson

Robert J. Vanderbei

David F. Shanno

机构：

[1] Princeton University,Operations Research and Financial Engineering

[2] Rutgers University,undefined

来源：

Computational Optimization and Applications | 2002年 / 23卷

关键词：

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

D O I：

暂无

中图分类号：

学科分类号：

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：15

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