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Title: A modified limited-memory BNS method for unconstrained minimization derived from the conjugate directions idea (English)
Author: Vlček, Jan
Author: Lukšan, Ladislav
Language: English
Journal: Programs and Algorithms of Numerical Mathematics
Volume: Proceedings of Seminar. Dolní Maxov, June 8-13, 2014
Issue: 2014
Year:
Pages: 237-243
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Category: math
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Summary: A modification of the limited-memory variable metric BNS method for large scale unconstrained optimization of the differentiable function $f:{\cal R}^N\to\cal R$ is considered, which consists in corrections (based on the idea of conjugate directions) of difference vectors for better satisfaction of the previous quasi-Newton conditions. In comparison with [11], more previous iterations can be utilized here. For quadratic objective functions, the improvement of convergence is the best one in some sense, all stored corrected difference vectors are conjugate and the quasi-Newton conditions with these vectors are satisfied. The algorithm is globally convergent for convex sufficiently smooth functions and our numerical experiments indicate its efficiency. (English)
Keyword: large scale unconstrained optimization
Keyword: limited-memory variable metric method
Keyword: BNS method
Keyword: quasi-Newton method
Keyword: convergence analysis
Keyword: numerical experiments
MSC: 65K05
MSC: 65Y20
MSC: 90C06
MSC: 90C53
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Date available: 2015-04-20T06:17:12Z
Last updated: 2023-06-05
Stable URL: http://hdl.handle.net/10338.dmlcz/702689
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