Methods of Nonsmooth Optimization in Stochastic Programming: From Conceptual Algorithms to Real-World Applications

by Wim Stefanus van Ackooij, Welington Luis de Oliveira

Overview

This book presents a comprehensive series of methods in nonsmooth optimization, with a particular focus on their application in shastic programming and dedicated algorithms for decision-making under uncertainty. Each method is accompanied by rigorous mathematical analysis, ensuring a deep understanding of the underlying principles. The theoretical discussions included are essential for comprehending the mechanics of various algorithms and the nature of the solutions they provide—whether they are global, local, stationary, or critical. The book begins by introducing fundamental tools from set-valued analysis, optimization, and probability theory. It then transitions from deterministic to shastic optimization, starting with a thorough discussion of modeling, understanding uncertainty, and incorporating it into optimization problems. Following this foundation, the book explores numerical algorithms for nonsmooth optimization, covering well-known decomposition techniques and algorithms for convex optimization, mixed-integer convex programming, and nonconvex optimization. Additionally, it introduces numerical algorithms specifically for shastic programming, focusing on shastic programming with recourse, chance-constrained optimization, and detailed algorithms for both risk-neutral and risk-averse multistage shastic programs.

The book guides readers through the entire process, from defining optimization models for practical problems to presenting implementable algorithms that can be applied in practice. It is intended for students, practitioners, and scholars who may be unfamiliar with shastic programming and nonsmooth optimization. The analyses provided are also valuable for practitioners who may not be interested in convergence proofs but wish to understand the nature of the solutions obtained.

Product Details

ISBN-13:	9783031848360
Publisher:	Springer Nature Switzerland
Publication date:	05/06/2025
Series:	International Series in Operations Research & Management Science , #363
Pages:	570
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Wim van Ackooij holds a PhD degree from École Centrale de Paris and a Habilitation from Université Paris 1 Panthéon-Sorbonne, France, both in Applied Mathematics. He is Associate Editor of Optimization and Mathematical Programming Computation. Wim has published nearly 70 papers in refereed journals and has extensive experience in shastic optimization, specifically probabilistically constrained programming, as well as unit commitment and bundle methods. He has also worked on practical applications of optimization in the energy industry for over 20 years.

Welington de Oliveira is an Associate Professor at the Centre de Mathématiques Appliquées, Mines Paris - PSL, France. He obtained his PhD in systems engineering and computer science from the Federal University of Rio de Janeiro, Brazil, and has a Habilitation in applied mathematics from Université Paris 1 Panthéon Sorbonne, France. Welington has extensive experience in nonsmooth optimization and shastic programming, having published numerous research articles and served as an associate editor for several reputable journals in the field.

Table of Contents

Introduction.- Primer of convex analysis.- Variational analysis.- Linear and nonlinear optimization problems.- Probability and Statistics.- Fundamental modeling questions in shastic programming.- Adjusting to uncertainty: modeling recourse.- Probability constraints.- Proximal point algorithms for problems with structure.- Cutting-plane algorithms for nonsmooth convex optimization over simple domains.- Bundle methods for nonsmooth convex optimization over simple domains.- Methods for nonlinearly constrained nonsmooth optimization problems.- Methods for nonsmooth optimization with mixed-integer variables.- Methods for nonsmooth nonconvex optimization.- Two-stage shastic programs.- Progressive decoupling in multistage shastic programming.- Scenario decomposition with alternating projections.- Methods for multistage shastic linear programs.- Methods for handling probability.