Linear and Convex Optimization - by Michael H Veatch (Hardcover)

About the Book

"This book introduces and explains the mathematics behind convex and linear optimization, focusing on developing insights in problem complexity, modelling and algorithms. Although many introductory books pay little attention to nonlinear optimization, convex problems deserve attention because of their many applications and the fast algorithms that have been developed to solve them. The main algorithms used in linear, integer, and convex optimization are presented in a mathematical style. The emphasis is on what makes a class of problems practically solvable and developing insight into algorithms geometrically. Principles of algorithm design are explained, making it accessible to those with no background in algorithms. The important issue of speed of algorithms is discussed and addressed theoretically where appropriate. A breadth of recent applications are presented to demonstrate the many areas in which optimization is successfully used. The process of formulating optimization problems is included throughout, both to develop the ability to formulate large problems and to appreciate that some formulations are more tractable"--

Book Synopsis

Discover the practical impacts of current methods of optimization with this approachable, one-stop resource

Linear and Convex Optimization: A Mathematical Approach delivers a concise and unified treatment of optimization with a focus on developing insights in problem structure, modeling, and algorithms. Convex optimization problems are covered in detail because of their many applications and the fast algorithms that have been developed to solve them.

Experienced researcher and undergraduate teacher Mike Veatch presents the main algorithms used in linear, integer, and convex optimization in a mathematical style with an emphasis on what makes a class of problems practically solvable and developing insight into algorithms geometrically. Principles of algorithm design and the speed of algorithms are discussed in detail, requiring no background in algorithms.

The book offers a breadth of recent applications to demonstrate the many areas in which optimization is successfully and frequently used, while the process of formulating optimization problems is addressed throughout.

Linear and Convex Optimization contains a wide variety of features, including:

• Coverage of current methods in optimization in a style and level that remains appealing and accessible for mathematically trained undergraduates
• Enhanced insights into a few algorithms, instead of presenting many algorithms in cursory fashion
• An emphasis on the formulation of large, data-driven optimization problems
• Inclusion of linear, integer, and convex optimization, covering many practically solvable problems using algorithms that share many of the same concepts
• Presentation of a broad range of applications to fields like online marketing, disaster response, humanitarian development, public sector planning, health delivery, manufacturing, and supply chain management

Ideal for upper level undergraduate mathematics majors with an interest in practical applications of mathematics, this book will also appeal to business, economics, computer science, and operations research majors with at least two years of mathematics training.

Software to accompany the text can be found here: https: //www.gordon.edu/michaelveatch/optimization

From the Back Cover

Discover the practical impacts of current methods of optimization with this approachable, one-stop resource

Linear and Convex Optimization: A Mathematical Approach delivers a concise and unified treatment of optimization with a focus on developing insights in problem structure, modeling, and algorithms. Convex optimization problems are covered in detail because of their many applications and the fast algorithms that have been developed to solve them.

Experienced researcher and undergraduate instructor Mike Veatch presents the main algorithms used in linear, integer, and convex optimization in a mathematical style with an emphasis on what makes a class of problems practically solvable and developing insight into algorithms geometrically. Principles of algorithm design and the speed of algorithms are discussed in detail, requiring no background in algorithms.

The book offers a breadth of recent applications to demonstrate the many areas in which optimization is successfully and frequently used, while the process of formulating optimization problems is addressed throughout.

Linear and Convex Optimization contains a wide variety of features, including:

• Coverage of current methods in optimization in a style and level that remains appealing and accessible for mathematically trained undergraduates
• Enhanced insights into a few algorithms, instead of presenting many algorithms in cursory fashion
• An emphasis on the formulation of large, data-driven optimization problems
• Inclusion of linear, integer, and convex optimization, covering many practically solvable problems using algorithms that share many of the same concepts
• Presentation of a broad range of applications to fields like online marketing, disaster response, humanitarian development, public sector planning, health delivery, manufacturing, and supply chain management

Ideal for upper level undergraduate mathematics majors with an interest in practical applications of mathematics, this book will also appeal to business, economics, computer science, and operations research majors with at least two years of mathematics training.

About the Author

Michael H. Veatch, PhD, is Professor of Mathematics at Gordon College, in Wenham, Massachusetts, United States. He obtained his PhD in Operations Research from the Massachusetts Institute of Technology in Cambridge, MA and has been working in operations research for 40 years.