A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems

A subspace adaptation of the Coleman--Li trust region and interior method is proposed for solving large-scale bound-constrained minimization problems. This method can be implemented with either sparse Cholesky factorization or conjugate gradient computation. Under reasonable conditions the convergen...

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Bibliographic Details
Published inSIAM journal on scientific computing Vol. 21; no. 1; pp. 1 - 23
Main Authors Branch, Mary Ann, Coleman, Thomas F., Li, Yuying
Format Journal Article
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 1999
Subjects
ALGORITHMS
Applied mathematics
BOUNDARY-VALUE PROBLEMS
CONVERGENCE
FACTORIZATION
GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ITERATIVE METHODS
Linear algebra
Methods
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ISSN1064-8275
1095-7197
DOI10.1137/S1064827595289108

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Summary:A subspace adaptation of the Coleman--Li trust region and interior method is proposed for solving large-scale bound-constrained minimization problems. This method can be implemented with either sparse Cholesky factorization or conjugate gradient computation. Under reasonable conditions the convergence properties of this subspace trust region method are as strong as those of its full-space version. Computational performance on various large test problems is reported; advantages of our approach are demonstrated. Our experience indicates that our proposed method represents an efficient way to solve large bound-constrained minimization problems.
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USDOE
US Department of the Navy, Office of Naval Research (ONR)
National Science Foundation (NSF)
ISSN:1064-8275
1095-7197
DOI:10.1137/S1064827595289108