Linear and Integer Programming Made Easy

This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits for data and information science and technology. The authors’ approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision. The presentations enables the basis for numerous approaches to solving hard combinatorial optimization problems through randomization and approximation.  Readers will learn to cast various problems that may arise in their research as optimization problems, understand the cases where the optimization problem will be linear, choose appropriate solution methods and interpret results appropriately.

Table of Contents

Chapter 1: Preliminaries
Chapter 2: Introduction
Chapter 3: Dimension of the Solution Space
Chapter 4: Introduction to the Simplex Method
Chapter 5: Duality and Complementary Slackness
Chapter 6: Revised Simplex Method
Chapter 7: Column Generating Technique
Chapter 8: The Knapsack Problem
Chapter 9: Asymptotic Algorithms
Chapter 10: The World Map of Integer Programs
Chapter 11: Linear and Integer Programming in Practice
Appendix: The Branch and Bound Method of Integer Programming