Discrete optimization challenges can be found everywhere, including in traditional operations research planning issues like scheduling, facility location, and network design, as well as in database issues in computer science and viral marketing. But the majority of these issues are NP-hard.

Therefore, there are no effective algorithms to identify the best solutions to such problems unless P = NP. This book demonstrates how to create efficient algorithms that locate solutions that are demonstrably close to ideal.

The book is divided into sections based on key algorithmic methods for creating approximation algorithms, such as greedy and local search algorithms, dynamic programming, linear and semidefinite programming, randomization, and others.

The first section of the book devotes each chapter to a specific algorithmic technique, which is subsequently used to solve a number of diverse problems. The second section reviews the techniques but does it in a more in-depth manner.

The book also discusses techniques for demonstrating how difficult it is to tackle approximation optimization problems. The book will act as a reference for scholars interested in the heuristic solution of discrete optimization problems and is written as a textbook for graduate-level algorithms courses.