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Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov - Nyomtatható verzió +- HHWForum.hu (https://hhwforum.hu) +-- Fórum: Letöltések (https://hhwforum.hu/forumdisplay.php?fid=9) +--- Fórum: E-könyvek (https://hhwforum.hu/forumdisplay.php?fid=57) +---- Fórum: Külföldi könyvek (https://hhwforum.hu/forumdisplay.php?fid=64) +---- Téma: Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov (/showthread.php?tid=266347) |
RE: Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov - book24h - 2025-03-27 ![]() Free Download Napsu Karmitsa, "Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov" English | ISBN: 3039438352 | 2020 | 116 pages | PDF | 2 MB 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. Read more Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.Links are Interchangeable - Single Extraction |