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

• Other Authors: Karmitsa, Napsu (Editor), Taheri, Sona (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: Information technology industries; multiple instance learning; support vector machine; DC optimization; nonsmooth optimization; achievement scalarizing functions; interactive method; multiobjective optimization; spent nuclear fuel disposal; non-smooth optimization; biased-randomized algorithms; heuristics; soft constraints; DC function; abs-linearization; DCA; Gauss-Newton method; nonsmooth equations; nonlinear complementarity problem; B-differential; superlinear convergence; global convergence; stochastic programming; stochastic hydrothermal UC problem; parallel computing; asynchronous computing; level decomposition; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.