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