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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.
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.
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