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

• Autore: Karmitsa Napsu
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (116 p.)
• Soggetto topico: Information technology industries
• Soggetto non controllato: abs-linearization; achievement scalarizing functions; asynchronous computing; B-differential; biased-randomized algorithms; DC function; DC optimization; DCA; Gauss-Newton method; global convergence; heuristics; interactive method; level decomposition; multiobjective optimization; multiple instance learning; n/a; non-smooth optimization; nonlinear complementarity problem; nonsmooth equations; nonsmooth optimization; parallel computing; soft constraints; spent nuclear fuel disposal; stochastic hydrothermal UC problem; stochastic programming; superlinear convergence; support vector machine
• Persona (resp. second.): TaheriSona; KarmitsaNapsu
• Sommario/riassunto: 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.
• Titolo autorizzato: Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557107803321
• Lo trovi qui: Univ. Federico II