To practice the algorithms and data structures we'll learn from this book, we need the Kotlin compiler to be installed on our PC. There are many ways to set your PC up for this. Let's look into that. Here is the GitHub link of this book:

The GitHub URL for the chapter is here:

https://github.com/PacktPublishing/Hands-On-Data-Structures-and-Algorithms-with-Kotlin/tree/master/Chapter01

The Kotlin binary is released over GitHub. So you need to find the latest release and download the ZIP file if you want to install the Kotlin compiler manually. Currently, the latest release is 1.2.40, which can be found at this GitHub URL: https://github.com/JetBrains/kotlin...

In computer programming, the sole purpose of building any software is to solve a problem existing in the real world and make the world a better place to live in. When we talk about a solution to a problem, it's obvious that we're talking about the procedure to be followed to solve the problem.

Let's consider a case where your teacher comes to you and gives you a bunch of candy, asking you to distribute it equally among your classmates. If you consider this as a problem and try to achieve it with ease, then you might do it as follows:

1. Count the number of candies you have.
2. Count the number of students present in your class.
3. Calculate how many candies should be given to each student.
4. Ask all of the students form a queue so that no student can take the required candies twice.
5. Start distributing the candies to each student.
6. Maintain a separate...

We solved the previous example by taking a few steps, such as asking all students to stand in a queue and, once a student got candy, we moved them to a separate group. We could have also done the same task without asking them to maintain a queue or separating them into a separate group once they got the candy. But we did that to make the task go smoother. Of course, we could have done the task without these two sub-tasks, but that would have created a lot of confusion and made it tedious to finish the task on time.

If we consider that example, we can treat candies and students as data. And to solve the problem given by the teacher, we first structured the data and solved it with ease. This is what we call data structures in computer science.

In this book, we'll cover the most commonly used data structures such as an array, string, list, linked...

So far, we've gained a good understanding of what an algorithm is and how data structures can be used in an algorithm to make it work better. In our day-to-day life, any task can be completed in many ways. Similarly, in computer science, any computational problem can be solved using many solutions; in other words, they can be solved using many algorithms. Since we've a one-to-many relationship with respect to the problem versus solutions, we might easily get confused while choosing a solution for a particular problem. This is the time for us to understand how to analyze an algorithm and choose the one that suits our problem.

Whenever we're trying to fix a particular computational problem, it's obvious that we'll consider all possible algorithms that can solve it. But we can't use them all together while writing our solution. In...

So far, we've understood why analyzing the complexity of an algorithm is really important. Now it's time to understand how to analyze the complexity. Before moving ahead with the how-to part, let's understand that, once the complexity of an algorithm is analyzed, there should be some way to represent it. For that, we usually use different notations.

As mentioned earlier, an algorithm can behave differently based on the size of the input given to it. And in the computer world, if you're building software, your algorithm should be prepared for any size of input. So, it's obvious that we need to analyze the complexity of every possible case.

The complexity of an algorithm can broadly fall into the following three categories:

• Best case analysis: Best case defines the minimum time required by an algorithm to produce the output. It...

So far, we've understood what an algorithm is and how it can be used with any data structures to solve problems easily. We also understood how to analyze the complexity of an algorithm. But this is just the beginning. As the chapters go on, all of the examples we discuss will relate to complexity analysis and similar information.

We will discuss about many commonly used data structures in further chapters and will try to do the complexity analysis for all those data structures and algorithms.

• https://kotlinlang.org/docs/tutorials/command-line.html
• https://kotlinlang.org/docs/tutorials/getting-started.html
• https://kotlinlang.org/docs/tutorials/getting-started-eclipse.html