#### 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.
Preface
Free Chapter
Hello Rust!
Cargo and Crates
Storing Efficiently
Lists, Lists, and More Lists
Robust Trees
Exploring Maps and Sets
Collections in Rust
Algorithm Evaluation
Ordering Things
Finding Stuff
Random and Combinatorial
Algorithms of the Standard Library
Assessments
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# Summary

The Big O notation is a way to describe the time and space requirements of an algorithm (or data structure). This is not an exact science, however; it's about finding the primary growth factor of each of the things mentioned to answer this question: what happens when the problem space grows bigger?

Any algorithm will fall within a few relevant classes that describe that behavior. By applying the algorithm to one more element, how many more steps have to be taken? One easy way is to visualize the individual charts and think of whether it will be linear (O(n)), quasilinear (O(n log(n))), quadratic (O(n²)), or even exponential (O(2n)). Whatever the case may be, it is always best to do less work than there are elements to be looked at, such as constant (O(1)) or logarithmic (O(log(n)) behaviors!

Selecting the operations is typically done based on the worst-case...