An Optimization Primer

Johannes O. Royset

ISBN 10: 3030762777 / ISBN 13: 9783030762773

Edité par Springer, Palgrave Macmillan, 2023

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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 analysiswould be helpful but is not prerequisite.

N° de réf. du vendeur 9783030762773

Détails bibliographiques

• Titre: An Optimization Primer
• Éditeur: Springer, Palgrave Macmillan
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 3030762777
• ISBN à 13 chiffres: 9783030762773
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 254 L x 178 H x 38 P (en millimètres) 254 millimètres de largeur par 178 millimètres de hauteur par 38 millimètres de profondeur
• Poids: 1286 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Johannes O. Royset, a Professor of Operations Research at the Naval Postgraduate School, is the recipient of the Barchi Prize, the Military OR Journal Award and the Goodeve Medal. He has been a visiting scholar at Stanford University and University of California, Davis as well as an associate editor of SIAM Journal on Optimization, Operations Research and Set-Valued and Variational Analysis. His experience with teaching optimization at the Naval Postgraduate School and University of California, Berkeley extends more than 15 years. He has authored 90 papers and a monograph.
Roger J-B Wets, a Distinguished Professor Emeritus of Mathematics at University of California, Davis, has received the G. B. Dantzig Prize in Mathematical Programming, a Pioneer Award for contributions to stochastic optimization and a Doctor Honoris Causa from the University of Vienna. He has been a Guggenheim Fellow as well as editor of SIAM Journal on Control and Optimization and Journal of Convex Analysis. Wets has published more than 200 papers on the theory and application of optimization. His previous book Variational Analysis received the F. W. Lanchester Prize.