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An InteriorPoint Algorithm for Nonconvex Nonlinear Programming
 Robert J. Vanderbei,
 David F. Shanno
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The paper describes an interiorpoint algorithm for nonconvex nonlinear programming which is a direct extension of interiorpoint methods for linear and quadratic programming. Major modifications include a merit function and an altered search direction to ensure that a descent direction for the merit function is obtained. 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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 Title
 An InteriorPoint Algorithm for Nonconvex Nonlinear Programming
 Journal

Computational Optimization and Applications
Volume 13, Issue 13 , pp 231252
 Cover Date
 19990401
 DOI
 10.1023/A:1008677427361
 Print ISSN
 09266003
 Online ISSN
 15732894
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 nonlinear programming
 interiorpoint methods
 nonconvex optimization
 Industry Sectors
 Authors

 Robert J. Vanderbei ^{(1)}
 David F. Shanno ^{(1)}
 Author Affiliations

 1. Princeton University, USA