Название: An Illustrative Introduction to Algorithms Автор: Dino Cajic
Издательство: Amazon Digital Services LLC
Год: 2019
Страниц: 387
Язык: английский
Формат: epub
Размер: 65.5 MB
This book was written to fill the gap that exists when Computer Science students, and programmers, attempt to learn and analyze the different algorithms that currently exist. I took a course on Algorithms and was disappointed in the type of material that’s currently available. There are two types of books that I kept running into:
1) First, the overly complex book. This book seems like it's designed for people that are already fluent in the topics and wanted a more detailed and mathematical approach to algorithms.
2) Second, the overly simple book. A basic introduction to algorithms. This is a high-level overview of some algorithms, and most complex algorithms are not mentioned. After completion, the person is still incapable of showing how the algorithm runs when a problem is presented.
There are three approaches to reading this book:
1. Read about the algorithm online and then see if you can follow the traversal illustrated in this book.
2. View the algorithm in action by going through the chapter designated for the algorithm and then go online and read further about it.
3. Just view the algorithm as is outlined in this book. A significant amount of time was dedicated to make these algorithms easy to follow.
This book is designed for undergraduate upper-class students and programmers that want to expand their horizon. It can be used as a supplementary book alongside the complex book. Readers will gain the knowledge necessary to solve those mathematically intensive algorithmic problems that were presented in the complex book.
Each chapter consists of a brief description of how the algorithm works followed by a detailed example or two. No steps are skipped during the traversal process. The reader is presented with a clear, simplified approach to solving the algorithm that the chapter is dedicated to. Each chapter follows a natural progression from the previous chapter. If certain algorithms rely heavily on prior knowledge, the previous chapter covers that topic. For example, Kruskal’s algorithm relies heavily on prior knowledge of Minimum Spanning Trees and Greedy Algorithms. Each of those topics receives a chapter of its own.
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