Randomized Search Directions in Descent Methods for Minimizing Certain Quasi-Differentiable Functions

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/2566 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/2566 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/2566 archive 351 disk0/00/00/25/21 2016-01-15 01:55:05 2021-08-27 17:12:06 2016-01-15 01:55:05 monograph show 2 Kiwiel K. Randomized Search Directions in Descent Methods for Minimizing Certain Quasi-Differentiable Functions pub iis_met prog_sds Several descent methods have recently been proposed for minimizing smooth compositions of max-type functions. The methods generate many search directions at each iteration. It is shown here that a random choice of only two search directions at each iteration suffices to retain convergence to in#-stationary points with probability 1. Use of this technique may significantly decrease the effort involved in quadratic programming and line searches, thus allowing efficient implementations of the methods. This paper is a contribution to research on non-smooth optimization currently underway in the System and Decision Sciences Program. 1984-02 published CP-84-056 CP-84-056 public collaborative_paper IIASA, Laxenburg, Austria 16 FALSE info:eu-repo/semantics/book