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Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov
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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.
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Keywords
- abs-linearization
- achievement scalarizing functions
- asynchronous computing
- B-differential
- biased-randomized algorithms
- DC function
- DC optimization
- DCA
- Economics, finance, business & management
- Gauss–Newton method
- global convergence
- heuristics
- Industry & industrial studies
- Information technology industries
- interactive method
- level decomposition
- Media, information & communication industries
- 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
Links
DOI: 10.3390/books978-3-03943-836-5Editions