Introduction to Nonsmooth Optimization

Theory, Practice and Software

Business & Finance, Management & Leadership, Operations Research, Nonfiction, Science & Nature, Mathematics, Applied
Cover of the book Introduction to Nonsmooth Optimization by Adil Bagirov, Napsu Karmitsa, Marko M. Mäkelä, Springer International Publishing
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Author: Adil Bagirov, Napsu Karmitsa, Marko M. Mäkelä ISBN: 9783319081144
Publisher: Springer International Publishing Publication: August 12, 2014
Imprint: Springer Language: English
Author: Adil Bagirov, Napsu Karmitsa, Marko M. Mäkelä
ISBN: 9783319081144
Publisher: Springer International Publishing
Publication: August 12, 2014
Imprint: Springer
Language: English

This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily differentiable optimization). Solving these kinds of problems plays a critical role in many industrial applications and real-world modeling systems, for example in the context of image denoising, optimal control, neural network training, data mining, economics and computational chemistry and physics. The book covers both the theory and the numerical methods used in NSO and provide an overview of different problems arising in the field. It is organized into three parts:

  1. convex and nonconvex analysis and the theory of NSO;

  2. test problems and practical applications;

  3. a guide to NSO software.

The book is ideal for anyone teaching or attending NSO courses. As an accessible introduction to the field, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.

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This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily differentiable optimization). Solving these kinds of problems plays a critical role in many industrial applications and real-world modeling systems, for example in the context of image denoising, optimal control, neural network training, data mining, economics and computational chemistry and physics. The book covers both the theory and the numerical methods used in NSO and provide an overview of different problems arising in the field. It is organized into three parts:

  1. convex and nonconvex analysis and the theory of NSO;

  2. test problems and practical applications;

  3. a guide to NSO software.

The book is ideal for anyone teaching or attending NSO courses. As an accessible introduction to the field, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.

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