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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/5553
Title: A Subspace, Interior, and Conjugate Gradient Method for Large-scale Bound-constrained Minimization Problems
Authors: Branch, Mary Ann
Coleman, Thomas F.
Li, Yuying
Keywords: theory center
Interior method
trust region method
negative curvature direction
inexact Newton step
conjugate gradients
bound-constrained problem
box-constraints
Issue Date: Jul-1995
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.tc/95-217
Abstract: A subspace adaption 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-scale test problems are reported; advantages of our approach are demonstrated. Our experience indicates our proposed method represents an efficient way to solve large-scalebound-constrained minimization problems.
URI: http://hdl.handle.net/1813/5553
Appears in Collections:Cornell Theory Center Technical Reports

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