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 11

What is the difference between PRNGs and RNGs?

Pseudo-random number generators (PRNGs) use a process to generate a close-to-random sequence of numbers that are as statistically independent as possible. Random number generators (RNGs) try to use true randomness (for example, phenomena from the physical world that cannot be predicted) to generate random numbers.

What crate provides random number generators in Rust?

rand is the most important one.

How can backtracking solve combinatorial problems?

Backtracking recursively tries out possible combinations and evaluates their validity as soon as possible. This allows you to backtrack the bad solutions and save good solutions.

What is dynamic programming?

A programming technique that saves and uses common intermediate solutions to improve the algorithm's runtime complexity.

How are metaheuristics a problem-agnostic approach...