Table of Contents for
Python Algorithms: Mastering Basic Algorithms in the Python Language, Second Edition
Python Algorithms: Mastering Basic Algorithms in the Python Language, Second Edition
Published by
Apress, 2015
- Cover
- Title
- Copyright
- Dedication
- Contents at a Glance
- Contents
- About the Author
- About the Technical Reviewer
- Acknowledgments
- Preface
- Chapter 1: Introduction
- Chapter 2: The Basics
- Chapter 3: Counting 101
- Chapter 4: Induction and Recursion … and Reduction
- Chapter 5: Traversal: The Skeleton Key of Algorithmics
- Chapter 6: Divide, Combine, and Conquer
- Chapter 7: Greed Is Good? Prove It!
- Chapter 8: Tangled Dependencies and Memoization
- Chapter 9: From A to B with Edsger and Friends
- Chapter 10: Matchings, Cuts, and Flows
- Chapter 11: Hard Problems and (Limited) Sloppiness
- Appendix A: Pedal to the Metal: Accelerating Python
- Appendix B: List of Problems and Algorithms
- Appendix C: Graph Terminology
- Appendix D: Hints for Exercises
- Index
Contents
What the book covers only briefly or partially:
Taking the Asymptotics for a Spin
Empirical Evaluation of Algorithms
A Multitude of Representations
Subsets, Permutations, and Combinations
The Master Theorem: A Cookie-Cutter Solution
Chapter 4: Induction and Recursion … and Reduction
Designing with Induction (and Recursion)
Relaxation and Gradual Improvement
Reduction + Contraposition = Hardness Proof
Chapter 5: Traversal: The Skeleton Key of Algorithmics
How to Stop Walking in Circles
Depth-First Timestamps and Topological Sorting (Again)
Infinite Mazes and Shortest (Unweighted) Paths
Chapter 6: Divide, Combine, and Conquer
Tree-Shaped Problems: All About the Balance
Traversing Search Trees … with Pruning
Chapter 7: Greed Is Good? Prove It!
Chapter 8: Tangled Dependencies and Memoization
Shortest Paths in Directed Acyclic Graphs
Longest Increasing Subsequence
Chapter 9: From A to B with Edsger and Friends
Chapter 10: Matchings, Cuts, and Flows
Cheapest Flow and the Assignment Problem*
Chapter 11: Hard Problems and (Limited) Sloppiness
But Where Do You Start? And Where Do You Go from There?
When the Going Gets Tough, the Smart Get Sloppy
And the Moral of the Story Is …
Appendix A: Pedal to the Metal: Accelerating Python
Appendix B: List of Problems and Algorithms
Algorithms and Data Structures