Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov
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...
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| Định dạng: | Online |
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| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Những chủ đề: | |
| Truy cập trực tuyến: | ONIX_20210501_9783039438358_1175 |
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| _version_ | 1869529101618905088 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-69429 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-694292024-03-30T12:51:17Z Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov Karmitsa, Napsu Taheri, Sona 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries 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. 2021-05-01T15:49:26Z 2021-05-01T15:49:26Z 2020 book ONIX_20210501_9783039438358_1175 9783039438358 9783039438365 https://directory.doabooks.org/handle/20.500.12854/69429 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3231 https://mdpi.com/books/pdfview/book/3231 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-836-5 10.3390/books978-3-03943-836-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039438358 9783039438365 116 Basel, Switzerland open access |
| spellingShingle | 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov |
| title | Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov |
| title_full | Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov |
| title_fullStr | Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov |
| title_full_unstemmed | Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov |
| title_short | Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov |
| title_sort | nonsmooth optimization in honor of the 60th birthday of adil m bagirov |
| topic | 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20210501_9783039438358_1175 |