Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization

A preconditioned steepest descent (SD) method for solving very large (with dimensions up to 106 ) unconstrained optimization problems is presented. The basic idea is to incorpo1 rate the preconditioning technique in the framework of the SD method. The preconditioner, which is also a scaled memoryles...

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Main Authors: Leong, Wah June, Abu Hassan, Malik
Format: Article
Language:English
Published: Taru Publications 2009
Online Access:http://psasir.upm.edu.my/id/eprint/16624/2/Scaled%20memoryless%20BFGS%20preconditioned%20steepest%20descent%20method%20for%20very%20large.pdf
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author Leong, Wah June
Abu Hassan, Malik
author_facet Leong, Wah June
Abu Hassan, Malik
author_sort Leong, Wah June
collection UPM
description A preconditioned steepest descent (SD) method for solving very large (with dimensions up to 106 ) unconstrained optimization problems is presented. The basic idea is to incorpo1 rate the preconditioning technique in the framework of the SD method. The preconditioner, which is also a scaled memoryless BFGS updating matrix is selected despite the oftenly scaling strategy on SD method. Then the scaled memoryless BFGS preconditioned SD direction can be computed without any additional storage compared with a standard scaled SD direction. In very mild conditions it is shown that, for uniformly convex functions, the method is globally and linearly convergent. Numerical results are also given to illustrate the use of such preconditioning within the SD method. Our numerical study shows that the new proposed preconditioned SD method is significantly outperformed the SD method with Oren-Luenberger scaling and the conjugate gradient method, and comparable to the limited memory BFGS method.
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spelling upm.eprints-166242015-10-05T07:23:17Z http://psasir.upm.edu.my/id/eprint/16624/ Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization Leong, Wah June Abu Hassan, Malik A preconditioned steepest descent (SD) method for solving very large (with dimensions up to 106 ) unconstrained optimization problems is presented. The basic idea is to incorpo1 rate the preconditioning technique in the framework of the SD method. The preconditioner, which is also a scaled memoryless BFGS updating matrix is selected despite the oftenly scaling strategy on SD method. Then the scaled memoryless BFGS preconditioned SD direction can be computed without any additional storage compared with a standard scaled SD direction. In very mild conditions it is shown that, for uniformly convex functions, the method is globally and linearly convergent. Numerical results are also given to illustrate the use of such preconditioning within the SD method. Our numerical study shows that the new proposed preconditioned SD method is significantly outperformed the SD method with Oren-Luenberger scaling and the conjugate gradient method, and comparable to the limited memory BFGS method. Taru Publications 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16624/2/Scaled%20memoryless%20BFGS%20preconditioned%20steepest%20descent%20method%20for%20very%20large.pdf Leong, Wah June and Abu Hassan, Malik (2009) Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization. Journal of Information and Optimization Sciences, 30 (2). pp. 387-396. ISSN 0252-2667; ESSN: 2169-0103 10.1080/02522667.2009.10699885
spellingShingle Leong, Wah June
Abu Hassan, Malik
Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization
title Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization
title_full Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization
title_fullStr Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization
title_full_unstemmed Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization
title_short Scaled memoryless BFGS preconditioned steepest descent method for very large-scale unconstrained optimization
title_sort scaled memoryless bfgs preconditioned steepest descent method for very large scale unconstrained optimization
url http://psasir.upm.edu.my/id/eprint/16624/2/Scaled%20memoryless%20BFGS%20preconditioned%20steepest%20descent%20method%20for%20very%20large.pdf
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