#### Overview of this book

Data structures and algorithms are more than just theoretical concepts. They help you become familiar with computational methods for solving problems and writing logical code. Equipped with this knowledge, you can write efficient programs that run faster and use less memory. Hands-On Data Structures and Algorithms with Kotlin book starts with the basics of algorithms and data structures, helping you get to grips with the fundamentals and measure complexity. You'll then move on to exploring the basics of functional programming while getting used to thinking recursively. Packed with plenty of examples along the way, this book will help you grasp each concept easily. In addition to this, you'll get a clear understanding of how the data structures in Kotlin's collection framework work internally. By the end of this book, you will be able to apply the theory of data structures and algorithms to work out real-world problems.
Preface
Free Chapter
Section 1: Getting Started with Data Structures
A Walk Through - Data Structures and Algorithms
Arrays - First Step to Grouping Data
Section 2: Efficient Grouping of Data with Various Data Structures
Understanding Stacks and Queues
Maps - Working with Key-Value Pairs
Section 3: Algorithms and Efficiency
Deep-Dive into Searching Algorithms
Understanding Sorting Algorithms
Section 4: Modern and Advanced Data Structures
Collections and Data Operations in Kotlin
Introduction to Functional Programming
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Assessments

# Summary

In this chapter, we learned about a few of the most commonly used search algorithms. See the following table for a comparison of their relative performances. This will give you a better understanding when you come to choose which algorithm to use yourself:

 Algorithm Performance Linear Search O(n) Binary Search O(log n) Jump Search O(√n) Exponential Search O(log n)

Here, n is the size of the collection.

In addition to item search algorithms, we've also covered a few commonly used pattern matching algorithms. Here is a comparison of their relative performance:

 Algorithm Performance Naive Pattern Search O(m * n) Rabin-Karp Search O(m * n) Knuth-Morris-Prath Search O(m) + O(k) = O(n + k)

Here, m is the length of the text, n is the length of the pattern and k is the length of temporary array created for KMP search (the same as the pattern...