Algorithms Illuminated
Omnibus Edition

Algorithms Illuminated teaches the basics and key techniques of algorithms in the most accessible way imaginable.

Tim Roughgarden (Author)

9780999282984, Soundlikeyourself Publishing

Hardback, published 15 September 2022

690 pages, 350 b/w illus. 60 tables 200 exercises
26.2 x 18.3 x 4.8 cm, 1.61 kg

'Look through the Table of Contents and you might conclude that this is just another algorithms book. Don't be fooled. What makes this book special – what makes this book the first of its kind – is Tim Roughgarden's singular ability to weave algorithm design with pedagogical design. Learners need opportunities to check their understanding at key points, to study examples, to see algorithms in contexts that they care about, to confront the needed mathematical background buffered by these motivating contexts. It's all here, carried along by Roughgarden's enthusiasm not only for algorithms but also for the people who want to learn them.' Daniel Zingaro, University of Toronto

In Algorithms Illuminated, Tim Roughgarden teaches the basics of algorithms in the most accessible way imaginable. This Omnibus Edition contains the complete text of Parts 1-4, with thorough coverage of asymptotic analysis, graph search and shortest paths, data structures, divide-and-conquer algorithms, greedy algorithms, dynamic programming, and NP-hard problems. Hundreds of worked examples, quizzes, and exercises, plus comprehensive online videos, help readers become better programmers; sharpen their analytical skills; learn to think algorithmically; acquire literacy with computer science's greatest hits; and ace their technical interviews.

Part I. The Basics: 1. Introduction
2. Asymptotic notation
3. Divide-and-Conquer algorithms
4. The master method
5. QuickSort
6. Linear-time selection
Part II. Graph Algorithms and Data Structures: 7. Graphs: the Basics
8. Graph search and its applications
9. Dijkstra's shortest-path algorithm
10. The heap data structure
11. Search trees
12. Hash tables and Bloom filters
Part III. Greedy Algorithms and Dynamic Programming
13. Introduction to greedy algorithms
14. Huffman codes
15. Minimum spanning trees
16. Introduction to dynamic programming
17. Advanced dynamic programming
18. Shortest paths revisited
Part IV. Algorithms for NP-Hard Problems
19. What is NP-Hardness?
20. Compromising on correctness: efficient inexact algorithms
21. Compromising on speed: exact inefficient algorithms
22. Proving problems NP-hard
23. P, NP, and all that
24. Case study: the FCC incentive auction
Appendix A. Quick review of proofs By induction
Appendix B. Quick review of discrete probability
Epilogue. A field guide to algorithm design
Hints and solutions.

Subject Areas: Computer science [UY], Algorithms & data structures [UMB], Optimization [PBU]