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

Hande Y. Benson; Robert J. Vanderbei; David F. Shanno

doi:10.1023/A:1020533003783

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

Hande Y. Benson
, Robert J. Vanderbei
, David F. Shanno

Research output: Contribution to journal › Article › peer-review

80 Scopus citations

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.

Original language	English (US)
Pages (from-to)	257-272
Number of pages	16
Journal	Computational Optimization and Applications
Volume	23
Issue number	2
DOIs	https://doi.org/10.1023/A:1020533003783
State	Published - Nov 2002
Externally published	Yes

All Science Journal Classification (ASJC) codes

Control and Optimization
Computational Mathematics
Applied Mathematics

Keywords

Filter methods
Interior-point methods
Nonconvex optimization
Nonlinear programming

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10.1023/A:1020533003783

Cite this

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title = "Interior-point methods for nonconvex nonlinear programming: Filter methods and merit functions",

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.",

keywords = "Filter methods, Interior-point methods, Nonconvex optimization, Nonlinear programming",

author = "Benson, \{Hande Y.\} and Vanderbei, \{Robert J.\} and Shanno, \{David F.\}",

note = "Funding Information: Research of the first and third authors supported by NSF grant DMS-9870317, ONR grant N00014-98-1-0036. Research of the second author supported by NSF grant DMS-9805495.",

year = "2002",

month = nov,

doi = "10.1023/A:1020533003783",

language = "English (US)",

volume = "23",

pages = "257--272",

journal = "Computational Optimization and Applications",

issn = "0926-6003",

publisher = "Springer Netherlands",

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TY - JOUR

T1 - Interior-point methods for nonconvex nonlinear programming

T2 - Filter methods and merit functions

AU - Benson, Hande Y.

AU - Vanderbei, Robert J.

AU - Shanno, David F.

N1 - Funding Information: Research of the first and third authors supported by NSF grant DMS-9870317, ONR grant N00014-98-1-0036. Research of the second author supported by NSF grant DMS-9805495.

PY - 2002/11

Y1 - 2002/11

N2 - 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.

AB - 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.

KW - Filter methods

KW - Interior-point methods

KW - Nonconvex optimization

KW - Nonlinear programming

UR - https://www.scopus.com/pages/publications/0036854804

UR - https://www.scopus.com/pages/publications/0036854804#tab=citedBy

U2 - 10.1023/A:1020533003783

DO - 10.1023/A:1020533003783

M3 - Article

AN - SCOPUS:0036854804

SN - 0926-6003

VL - 23

SP - 257

EP - 272

JO - Computational Optimization and Applications

JF - Computational Optimization and Applications

IS - 2

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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