This book investigates the theoretical underpinnings and practical applications of methods for solving optimization problems where the objective function is not differentiable. It focuses on algorithms that can handle functions with non-smooth properties, which are common in many real-world applications. The work explores the convergence properties and computational efficiency of various descent techniques, providing a rigorous mathematical framework for their analysis.

As a volume in the Lecture Notes in Mathematics series, this book is understood to be a rigorous academic text aimed at researchers and advanced students in optimization and applied mathematics. Its focus on theoretical aspects suggests it is likely valued for its detailed mathematical treatment and comprehensive coverage of specialized algorithms. The content is expected to appeal to those seeking a deep understanding of the mathematical machinery behind solving complex optimization problems.