Original title: A Modified Limited-Memory BNS Method for Unconstrained Minimization Derived from the Conjugate Directions Idea
Authors: Vlček, Jan ; Lukšan, Ladislav
Document type: Papers
Conference/Event: Programs and Algorithms of Numerical Mathematics /17./, Dolní Maxov (CZ), 2014-06-08 / 2014-06-13
Year: 2015
Language: eng
Abstract: 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.
Keywords: BNS method; convergence; large scale unconstrained optimization; limited-memory variable metric method; numerical experiments; quasi-Newton method
Project no.: GA13-06684S (CEP)
Funding provider: GA ČR
Host item entry: Programs and algorithms of numerical mathematics 17. Proceedings of seminar, ISBN 978-80-85823-64-6
Note: Související webová stránka: http://dml.cz/handle/10338.dmlcz/702689

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0246707

Permalink: http://www.nusl.cz/ntk/nusl-187467


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