An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Robert J. Vanderbei; David F. Shanno

doi:10.1023/A:1008677427361

An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Robert J. Vanderbei, David F. Shanno

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

379 Scopus citations

Abstract

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 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.

Original language	English (US)
Pages (from-to)	231-252
Number of pages	22
Journal	Computational Optimization and Applications
Volume	13
Issue number	1-3
DOIs	https://doi.org/10.1023/A:1008677427361
State	Published - 1999

All Science Journal Classification (ASJC) codes

Computational Mathematics
Control and Optimization
Applied Mathematics

Keywords

Interior-point methods
Nonconvex optimization
Nonlinear programming

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10.1023/A:1008677427361

Cite this

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AB - 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 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.

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KW - Nonconvex optimization

KW - Nonlinear programming

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An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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