Book Image

Hands-On Data Structures and Algorithms with Rust

By : Claus Matzinger
Book Image

Hands-On Data Structures and Algorithms with Rust

By: Claus Matzinger

Overview of this book

Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications.
Table of Contents (15 chapters)

Chapter 5

How does a binary search tree skip several nodes when searching?

By following one branch, it skips one subtree every time the decision for one branch is made. A subtree can be anything from a single node to all nodes except one.

What are self-balancing trees?

Trees that use some kind of logic to (roughly) equalize the number of nodes in each subtree. This ensures that all tree algorithms work at the best possible efficiency.

Why is balance in a tree important?

If a tree is skewed, any algorithm operating on it will encounter an uneven amount of work depending on the subtree it works on. The mismatch is the assumption that every branch of the tree leads to the same amount of work (for example, the same number of comparisons to make), which is what makes the tree data structure efficient.

Is a heap a binary tree?

Yes. Each node has two children.

What are good use cases...