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Springer Introduction to Nonsmooth Optimization - Theory, Practice, Software

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Description

Introduction to Nonsmooth Optimization is the first accessible text bridging theory and practice in nonsmooth optimization (NSO), a field essential for solving non-differentiable problems across industrial and scientific domains. This comprehensive guide covers applications in image denoising, optimal control, neural network training, data mining, economics, computational chemistry and physics. Organised into three focused parts, the book explores convex and nonconvex analysis alongside practical numerical methods, providing both foundational theory and implementable solutions. Whether you are developing algorithms, tackling real-world modelling challenges or advancing computational research, this Springer publication equips you with the theoretical understanding and software tools needed to handle complex optimisation problems that standard differentiable methods cannot address.

Key Features

Introduction to Nonsmooth Optimization covers both theoretical foundations and practical numerical methods for solving non-differentiable optimisation problems across industry.

The text addresses real-world applications including image denoising, optimal control, neural network training, data mining, economics, and computational chemistry.

Springer's book is organised into three structured parts that systematically develop convex and nonconvex analysis concepts for readers at all levels.

This guide provides software implementations and practical solutions alongside theory, making NSO techniques immediately applicable to your research and development work.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
23 August 2016
Listed Since
21 July 2016

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