# Algorithmic Thinking: A Problem-Based Introduction

by Daniel Zingaro
October 2020, 430 pp.
ISBN-13:
9781718500808
Use coupon code PREORDER to get 25% off!

Programming is about using a computer to solve problems, and algorithms and data structures are the building blocks of computer programs. For each problem that a programmer wants to solve, they employ an algorithm: a sequence of steps for solving the problem. Many books teach algorithms independently of specific problems, but this book uses careful explanations, examples, and arguments, rather than formal mathematics and proofs which make it difficult for you to connect what you are learning to what you can do with that learning. Algorithmic Thinking: A Problem-Based Introduction teaches you to use the best algorithms and data structures for a given situation by walking you through solving problems pulled from international programming competitions, such as how to determine whether snowflakes are unique; how to win a game in the minimum number of moves; how to find the number of ways to get to someone's house; how to escape a cave in as few steps as possible; and so on.

You'll tackle challenging topics like recursion, dynamic programming, graphs, greedy algorithms, heaps, hash tables, segment trees, and other data structures for efficiently handling data. The book contains no pseudocode: all code is written in C and is thoroughly explained in the text (C is a de facto programming language for programming competitions). By the end of the book, you should understand the importance of carefully working through a problem, and why it pays to organize data using data structures.

Author Bio

Dr. Daniel Zingaro is an assistant teaching professor of computer science and award-winning teacher at the University of Toronto. His main area of research is computer science education research, where he studies how students learn (and sometimes don't learn) computer science material.

Introduction

Chapter 1: Hash Tables
Chapter 2: Trees and Recursion
Chapter 3: Memoization and Dynamic Programming
Chapter 4: Graphs and Breadth-First Search

Chapter 5: Shortest Paths in Weighted Graphs
Chapter 6: Binary Search
Chapter 7: Heaps and Segment Trees
Chapter 8: Union-Find
Afterword
Appendix A: Algorithm Runtime
Appendix B: Because I Can’t Resist
Appendix C: Problem Credits

The chapters in red are included in this Early Access PDF.