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: Paperback / softback
Length: 676 pages
Publication date: 29 March 2023
Publisher: Springer Nature Switzerland AG

This comprehensive and visually appealing book serves as an excellent introduction to the field of optimization for a wide range of readers. It provides a balanced treatment of theory, models, and algorithms, making it accessible to individuals with varying backgrounds. Through numerous real-world examples drawn from statistical learning, operations research, engineering, finance, and economics, the text demonstrates how to formulate and justify models while accounting for practical considerations such as data uncertainty. It extends beyond classical topics such as linear, nonlinear, and convex programming and addresses nonconvex and nonsmooth problems, as well as games, generalized equations, and stochastic optimization. The book employs a practical approach, teaching theoretical aspects within the context of concrete problems, thus serving as an accessible gateway to variational analysis, integral functions, and approximation theory.

To reinforce the learning experience, the book includes over 100 exercises and 200 fully developed examples that illustrate the application of optimization concepts. While a foundation in differential calculus and linear algebra is assumed, exposure to real analysis would be beneficial but is not a prerequisite. This book is an invaluable resource for students, researchers, and practitioners seeking to gain a deeper understanding of optimization and its applications in various fields.

Weight: 1303g
Dimension: 254 x 178 (mm)
ISBN-13: 9783030762773
Edition number: 1st ed. 2021