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)

Advanced problem solving

Backtracking calculates and finds the best overall solution to a particular problem. However, as described in Chapter 8, Algorithm Evaluation, there are problems that have a really large computational complexity, which leads to a really long running time. Since this is unlikely to be solved by simply making computers faster, smarter approaches are required.

With several strategies and techniques available, the choice is yours to find an approach that best solves your problem. The position of Rust in this space can be critical, thanks to its great speed and memory efficiency, so keeping an eye on solutions for complex problems might pay off in the future (in the author's opinion).

First up is a surprising programming technique that is aimed at improving the complexities of backtracking algorithms: dynamic programming.