Algorithmic Thinking, 2nd Edition Unlock Your Programming Potential

Get in the game and learn essential computer algorithms by solving competitive programming problems, in the fully revised second edition of the bestselling original. (Still no math required!)

Are you hitting a wall with data structures and algorithms? Whether you’re a student prepping for coding interviews or an independent learner, this book is your essential guide to efficient problem-solving in programming.

UNLOCK THE POWER OF DATA STRUCTURES & ALGORITHMS:
Learn the intricacies of hash tables, recursion, dynamic programming, trees, graphs, and heaps. Become proficient in choosing and implementing the best solutions for any coding challenge.

REAL-WORLD, COMPETITION-PROVEN CODE EXAMPLES:
The programs and challenges in this book aren’t just theoretical—they’re drawn from real programming competitions. Train with problems that have tested and honed the skills of coders around the world.

GET INTERVIEW-READY:
Prepare yourself for coding interviews with practice exercises that help you think algorithmically, weigh different solutions, and implement the best choices efficiently.

WRITTEN IN C, USEFUL ACROSS LANGUAGES:
The code examples are written in C and designed for clarity and accessibility to those familiar with languages like C++, Java, or Python. If you need help with the C code, no problem: We’ve got recommended reading, too.

Algorithmic Thinking is the complete package, providing the solid foundation you need to elevate your coding skills to the next level.

Dr. Daniel Zingaro is an award-winning Assistant Professor of Mathematical and Computational Sciences at the University of Toronto Mississauga, where he is well known for his uniquely interactive approach to teaching, and internationally recognized for his expertise in Active Learning.

Foreword
Introduction
Acknowledgments
Chapter 1: Hash Tables
Chapter 2: Trees and Recursion
Chapter 3: Memoization and Dynamic Programming
Chapter 4: Advanced Memoization and Dynamic Programming
Chapter 5: Graphs and Breadth-First Search
Chapter 6: Shortest Paths in Weighted Graphs
Chapter 7: Binary Search
Chapter 8: Heaps and Segment Trees
Chapter 9: Union-Find
Chapter 10: Randomization
Afterword
Appendix A: Algorithm Runtime
Appendix B: Because I Can’t Resist
Appendix C: Problem Credits
Index