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.