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Numerical Nonsmooth Optimization - State of the Art Algorithms (Paperback, 1st ed. 2020): Adil M. Bagirov, Manlio Gaudioso,... Numerical Nonsmooth Optimization - State of the Art Algorithms (Paperback, 1st ed. 2020)
Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Makela, Sona Taheri
R5,381 Discovery Miles 53 810 Ships in 10 - 15 working days

Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem's special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization.

Partitional Clustering via Nonsmooth Optimization - Clustering via Optimization (Paperback, 1st ed. 2020): Adil M. Bagirov,... Partitional Clustering via Nonsmooth Optimization - Clustering via Optimization (Paperback, 1st ed. 2020)
Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
R2,976 Discovery Miles 29 760 Ships in 10 - 15 working days

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.

Numerical Nonsmooth Optimization - State of the Art Algorithms (Hardcover, 1st ed. 2020): Adil M. Bagirov, Manlio Gaudioso,... Numerical Nonsmooth Optimization - State of the Art Algorithms (Hardcover, 1st ed. 2020)
Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Makela, Sona Taheri
R5,414 Discovery Miles 54 140 Ships in 10 - 15 working days

Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem's special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization.

Partitional Clustering via Nonsmooth Optimization - Clustering via Optimization (Hardcover, 1st ed. 2020): Adil M. Bagirov,... Partitional Clustering via Nonsmooth Optimization - Clustering via Optimization (Hardcover, 1st ed. 2020)
Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
R3,009 Discovery Miles 30 090 Ships in 10 - 15 working days

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.

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