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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.

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

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 --- Gauss-Newton method