Introduction to Nonsmooth Optimization Theory, Practice and Software [Paperback]

This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily di erentiable 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 di erent problems arising in the
eld. 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
eld, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.Introduction.- Nonsmooth Analysis and Optimization.- Nonsmooth Problems.- Nonsmooth Optimizational Methods.

This textbook can be useful for students to gain a panoramic view of the theory of nonsmooth optimization and of several practical applications that result in nondifferentiable optimization problems. The guide on numerical methods and software description in the final part can mostly be seen as a pointer to other books or articles with details and explanations. (Claudia A. Sagastiz?bal, Mathematical Reviews, February, 2016)

A.M. Bagirov received a master degree in Applied Mathematics from Baku State University, Azerbaijan in 1983, and the Candidate of Sciences degree in Mathematical Cybernetics from the Institute of Cybernetics of Azerbaijan National Academy of Sciences in 1989 and PhD degree in Optimization from Federation University Australia (formerly the University of Ballarat), Ballarat, Australia in 2002. He worked at the Space Research Institute (Baku, Al3‚