An Optimization Primer (1st ed. 2021)

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. The book teaches theoretical aspects in the context of concrete problems, which makes it an accessible onramp to variational analysis, integral functions and approximation theory. More than 100 exercises and 200 fully developed examples illustrate the application of the concepts. Readers should have some foundation in differential calculus and linear algebra. Exposure to real analysis would be helpful but is not prerequisite. 

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Series in Operations Research and Financial Engineering
Release date:	March 2023
First published:	2021
Authors:	Johannes O. Royset • Roger J-.B. Wets
Dimensions:	254 x 178mm (L x W)
Pages:	676
Edition:	1st ed. 2021
ISBN-13:	978-3-03-076277-3
Categories:	Books Promotions
LSN:	3-03-076277-7
Barcode:	9783030762773

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