Journal article
Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions
Computational optimization and applications, v 23(2), pp 257-272
01 Nov 2002
Abstract
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. [PUBLICATION ABSTRACT]
Metrics
Details
- Title
- Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions
- Creators
- Hande Benson - Princeton UniversityRobert Vanderbei - Princeton UniversityDavid Shanno - Rutgers, The State University of New Jersey
- Publication Details
- Computational optimization and applications, v 23(2), pp 257-272
- Publisher
- Springer Nature B.V
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems); Bennett S. LeBow College of Business; Drexel University
- Web of Science ID
- WOS:000178468900005
- Scopus ID
- 2-s2.0-0036854804
- Other Identifier
- 991019549554404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematics, Applied
- Operations Research & Management Science