This book provides a clear and accessible introduction to an important class of problems in mathematical optimization: those involving continuous functions that may be nonconvex, nonsmooth, or both. The authors begin with an intuitive treatment of theoretical foundations, including properties of nonconvex and nonsmooth functions and conditions for optimality. They then offer a broad overview of the most effective and efficient algorithms for solving such problems, with a focus on practical applications in areas such as control systems, signal processing, and data science.
@book{CurtRobi25,
author = {Frank E. Curtis and Daniel P. Robinson},
title = {Practical Nonconvex Nonsmooth Optimization},
publisher = {Society for Industrial and Applied Mathematics},
address = {Philadelphia, PA, USA},
series = {MOS-SIAM Series on Optimization},
year = {2025},
url = {https://doi.org/10.1137/1.9781611978599}
}