Lehigh University’s Department of Industrial and Systems Engineering (ISE) announces the release of Practical Nonconvex Nonsmooth Optimization, a new book by ISE faculty members Frank E. Curtis and Daniel P. Robinson. Published by the Society for Industrial and Applied Mathematics (SIAM), the book provides a comprehensive yet accessible treatment of optimization problems involving continuous functions that may be nonconvex, nonsmooth, or both. The softcover volume spans xxvi + 483 pages and is available through SIAM (ISBN: 978-1-61197-858-2). Written with both students and practitioners in mind, the book emphasizes practical relevance while maintaining a strong theoretical foundation.
Practical Nonconvex Nonsmooth Optimization introduces readers to a broad and important class of mathematical optimization problems that arise in modern applications such as control systems, signal processing, and data science. The authors begin with an intuitive discussion of foundational theory, including key properties of nonconvex and nonsmooth functions and fundamental optimality conditions. They then survey some of the most effective algorithms for solving these problems, with an emphasis on methods that are both efficient and implementable in practice. The text focuses on finite-dimensional real-vector spaces, avoiding the need for background in functional analysis, and gradually builds concepts starting from nonconvex smooth optimization. A conversational writing style is used throughout, with detailed proofs placed at the end of each chapter to allow readers to first grasp the core ideas before engaging with technical details.
Frank E. Curtis is a professor at Lehigh ISE. His research centers on the design, analysis, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He has received significant recognition for his contributions, including a DOE Early Career Award, the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization, and the 2018 INFORMS Computing Society Prize. His work has been funded by NSF, DOE, ONR, and ARPA-E, and he has led teams recognized in competitions such as the ARPA-E Grid Optimization Challenge. He is also deeply engaged in the professional community, serving as Area Editor for Mathematics of Operations Research and Mathematical Programming Computation and as an Associate Editor for leading journals including Mathematical Programming and SIAM Journal on Optimization.
Daniel P. Robinson is also a professor at Lehigh ISE. His research focuses on the theory and practice of nonlinear optimization, with particular emphasis on nonconvex and nonsmooth problems and the development of efficient, reliable algorithms for large-scale applications. He has received notable recognition for his scholarly contributions, including best paper awards at optimization and computational mathematics venues, as well as multiple educational awards. His research has been supported by agencies such NSF and ONR. Robinson is also highly active in the professional optimization community, serving as an Associate Editor for reputable journals, including Computational Optimization and Applications, Mathematics of Operations Research, and Optimization Methods and Software, and as Area Coordinator for Optimization Online, the leading repository of eprints in the optimization community.