Properties of the block BFGS update and its application to the limited-memory block BNS method for unconstrained minimization

dc.contributor.authorVlček, Jan
dc.contributor.authorLukšan, Ladislav
dc.date.accessioned2019-08-30T07:15:54Z
dc.date.available2019-08-30T07:15:54Z
dc.date.issued2019-03
dc.description.abstractA block version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) variable metric update formula and its modifications are investigated. In spite of the fact that this formula satisfies the quasi-Newton conditions with all used difference vectors and that the improvement of convergence is the best one in some sense for quadratic objective functions, for general functions, it does not guarantee that the corresponding direction vectors are descent directions. To overcome this difficulty, but at the same time utilize the advantageous properties of the block BFGS update, a block version of the limited-memory variable metric BNS method for large-scale unconstrained optimization is proposed. The global convergence of the algorithm is established for convex sufficiently smooth functions. Numerical experiments demonstrate the efficiency of the new method.cs
dc.format.extent31 strancs
dc.identifier.WebofScienceResearcherIDB-3177-2014 Vlček, Jan
dc.identifier.WebofScienceResearcherIDA-7356-2014 Lukšan, Ladislav
dc.identifier.doi10.1007/s11075-018-0513-3
dc.identifier.urihttps://dspace.tul.cz/handle/15240/153348
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs11075-018-0513-3
dc.language.isocscs
dc.relation.ispartofNUMERICAL ALGORITHMS
dc.subjectUnconstrained minimizationcs
dc.subjectBlock variable metric methodscs
dc.subjectLimited-memory methodscs
dc.subjectBFGS updatecs
dc.subjectGlobal convergencecs
dc.subjectNumerical resultscs
dc.titleProperties of the block BFGS update and its application to the limited-memory block BNS method for unconstrained minimizationcs
local.citation.epage987
local.citation.spage957
local.relation.issue3
local.relation.volume80
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