An Accelerated Method for Minimizing a Convex Function of Two Variables

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/2792 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/2792 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/2792 archive 351 disk0/00/00/27/31 2016-01-15 01:56:25 2021-08-27 17:12:31 2016-01-15 01:56:25 monograph show 2 Paizerova F.A. An Accelerated Method for Minimizing a Convex Function of Two Variables pub iis_met prog_sds The author considers the problem of minimizing a convex function of two variables without computing the derivatives or (in the nondifferentiable case) the subgradients of the function, and suggests two algorithms for doing this. Such algorithms could form an integral part of new methods for minimizing a convex function of many variables based on the solution of a two-dimensional minimization problem at each step (rather than on line-searches, as in most existing algorithms). This is a contribution to research on nonsmooth optimization currently underway in System and Decision Sciences Program Core. 1985-04 published CP-85-017 CP-85-017 public collaborative_paper IIASA, Laxenburg, Austria 18 FALSE info:eu-repo/semantics/book