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Introduction to Nonsmooth Optimization [electronic resource] : Theory, Practice and Software / by Adil Bagirov, Napsu Karmitsa, Marko M. Mäkelä.

By: Contributor(s): Material type: TextTextPublication details: Cham : Springer International Publishing : Imprint: Springer, 2014.Description: XVIII, 372 p. 72 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319081144
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 658.40301 23
LOC classification:
  • HD30.23
Online resources:
Contents:
Introduction -- Nonsmooth Analysis and Optimization -- Nonsmooth Problems -- Nonsmooth Optimizational Methods.
In: Springer eBooksSummary: 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 provides 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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Introduction -- Nonsmooth Analysis and Optimization -- Nonsmooth Problems -- Nonsmooth Optimizational Methods.

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 provides 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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