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
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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.
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页码：257 / 272
页数：15
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