Royset J., Wets R. An Optimization Primer 2022

Textbook in PDF format

This richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finance and economics, the text explains how to formulate and justify models while accounting for real-world considerations such as data uncertainty. It goes beyond the classical topics of linear, nonlinear and convex programming and deals with nonconvex and nonsmooth problems as well as games, generalized equations and stochastic optimization.
Preface.
Acknowledgements.
Prelude.
Convex optimization.
Optimization under uncertainty.
Minimization problems.
Perturbation and duality.
Without convexity or smoothness.
Generalized equations.
Risk modeling and sample averages.
Games and minsup problems.
Decomposition.
References.
Index