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An interior-point algorithm for large-scale nonlinear optimization with inexact step computations

Curtis, Frank and Schenk, Olaf and Wächter, Andreas. (2010) An interior-point algorithm for large-scale nonlinear optimization with inexact step computations. SIAM journal on scientific computing, Vol. 32. pp. 3447-3475.

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Official URL: http://edoc.unibas.ch/dok/A5842690

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Abstract

We present a line-search algorithm for large-scale continuous optimization. Thealgorithm is matrix-free in that it does not require the factorization of derivative matrices. Instead,it uses iterative linear system solvers. Inexact step computations are supported in order to savecomputational expense during each iteration. The algorithm is an interior-point approach derivedfrom an inexact Newton method for equality constrained optimization proposed by Curtis, Nocedal,and W ̈chter [SIAM J. Optim., 20 (2009), pp. 1224–1249], with additional functionality for handlinginequality constraints. The algorithm is shown to be globally convergent under loose assumptions.Numerical results are presented for nonlinear optimization test set collections and a pair of PDE-constrained model problems.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik
UniBasel Contributors:Schenk, Olaf
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:SIAM
ISSN:1064-8275
Note:Publication type according to Uni Basel Research Database: Journal article
Last Modified:11 Oct 2012 15:31
Deposited On:11 Oct 2012 15:21

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