An Optimization Primer
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An Optimization Primer
Author: Johannes O. Royset
language: en
Publisher: Springer Nature
Release Date: 2022-03-28
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.
An Optimization Primer
This book introduces a general audience to the main facets of optimization. Very little mathematical background is assumed. It should appeal to students, teachers, and a general audience interested in how optimization affects their everyday life, such as people in business.
An Optimization Primer
Author: Lawrence Nazareth
language: en
Publisher: Springer Science & Business Media
Release Date: 2004-05-18
This book introduces a general audience to the main facets of optimization. Very little mathematical background is assumed. It should appeal to students, teachers, and a general audience interested in how optimization affects their everyday life, such as people in business.