The design of approximation algorithms

Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.

Can be used as a textbook, but also as a way for students to get the background to read current research in the area of approximation algorithms
Explores the heuristic solution of discrete optimization problems
Explains the principles of designing approximation algorithms, around algorithmic ideas that have been used in different ways and applied to different optimization problems

(https://www.cambridge.org/us/universitypress/subjects/computer-science/algorithmics-complexity-computer-algebra-and-computational-g/design-approximation-algorithms?format=HB&isbn=9780521195270)