Nonsmooth Optimization In Honor Of The 60th Birthday Of Adil M Bagirov

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Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov

Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov
Author :
Publisher : MDPI
Total Pages : 116
Release :
ISBN-10 : 9783039438358
ISBN-13 : 3039438352
Rating : 4/5 (352 Downloads)

Book Synopsis Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov by : Napsu Karmitsa

Download or read book Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov written by Napsu Karmitsa and published by MDPI. This book was released on 2020-12-18 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book was to collect the most recent methods developed for NSO and its practical applications. The book contains seven papers: The first is the foreword by the Guest Editors giving a brief review of NSO and its real-life applications and acknowledging the outstanding contributions of Professor Adil Bagirov to both the theoretical and practical aspects of NSO. The second paper introduces a new and very efficient algorithm for solving uncertain unit-commitment (UC) problems. The third paper proposes a new nonsmooth version of the generalized damped Gauss–Newton method for solving nonlinear complementarity problems. In the fourth paper, the abs-linear representation of piecewise linear functions is extended to yield simultaneously their DC decomposition as well as the pair of generalized gradients. The fifth paper presents the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and nonsmooth optimization problems in many practical applications. In the sixth paper, a problem concerning the scheduling of nuclear waste disposal is modeled as a nonsmooth multiobjective mixed-integer nonlinear optimization problem, and a novel method using the two-slope parameterized achievement scalarizing functions is introduced. Finally, the last paper considers binary classification of a multiple instance learning problem and formulates the learning problem as a nonconvex nonsmooth unconstrained optimization problem with a DC objective function.


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