An Optimization Primer

This book provides a balanced treatment of theory, models, and algorithms for optimization, with examples from statistical learning, operations research, engineering, finance, and economics. It covers nonconvex and nonsmooth problems, games, generalized equations, and stochastic optimization and teaches theoretical aspects in the context of concrete problems.

Format: Hardback
Length: 676 pages
Publication date: 29 March 2022
Publisher: Springer Nature Switzerland AG

This comprehensive and visually appealing book serves as an introduction to the field of optimization for a wide range of readers, presenting a balanced treatment of theory, models, and algorithms. Through numerous real-world examples drawn from statistical learning, operations research, engineering, finance, and economics, the text elucidates the process of formulating and justifying models while taking into account practical considerations such as data uncertainty. It extends beyond the traditional realms of linear, nonlinear, and convex programming to address nonconvex and nonsmooth problems, as well as games, generalized equations, and stochastic optimization. By contextualizing theoretical concepts within concrete problem scenarios, the book provides an accessible entry point into variational analysis, integral functions, and approximation theory. Over 100 exercises and 200 fully developed examples further reinforce the understanding and application of the discussed concepts. While a basic understanding of differential calculus and linear algebra is assumed, exposure to real analysis would be beneficial but is not a prerequisite for engaging with this text.

Weight: 1631g
Dimension: 254 x 178 (mm)
ISBN-13: 9783030762742
Edition number: 1st ed. 2021