Stephens - Essential algorithms: a practical approach to computer algorithms using Python and C#

1. Chapter 1
2. Chapter 2
3. Chapter 4
4. Chapter 6
5. Chapter 7
6. Chapter 9
7. Chapter 10
8. Chapter 13
9. Chapter 14
10. Chapter 15
11. Chapter 16
12. Chapter 18
13. Appendix B

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10
11. Chapter 11
12. Chapter 12
13. Chapter 13
14. Chapter 14
15. Chapter 15
16. Chapter 16
17. Chapter 17
18. Chapter 18
19. Appendix B

Rod Stephens

Algorithms are the recipes that make efficient programming possible. They explain how to sort records, search for items, calculate numeric values such as prime factors, find the shortest path between two points in a street network, and determine the maximum flow of information possible through a communications network. The difference between using a good algorithm and a bad one can mean the difference between solving a problem in seconds, hours, or never.

Studying algorithms lets you build a useful toolkit of methods for solving specific problems. It lets you understand which algorithms are most effective under different circumstances so that you can pick the one best suited for a particular program. An algorithm that provides excellent performance with one set of data may perform terribly with other data, so it is important that you know how to pick the algorithm that is the best match for your scenario.

Even more important, by studying algorithms, you can learn general problem-solving techniques that you can apply to other problemseven if none of the algorithms you already know is a perfect fit for your current situation. These techniques let you look at new problems in different ways so that you can create and analyze your own algorithms to solve your problems and meet unanticipated needs.

In addition to helping you solve problems while on the job, these techniques may even help you land the job where you can use them! Many large technology companies, such as Microsoft, Google, Yahoo!, IBM, and others, want their programmers to understand algorithms and the related problem-solving techniques. Some of these companies are notorious for making job applicants work through algorithmic programming and logic puzzles during interviews.

The better interviewers don't necessarily expect you to solve every puzzle. In fact, they will probably learn more about you when you don't solve a puzzle. Rather than wanting to know the answer, the best interviewers want to see how you approach an unfamiliar problem. They want to see whether you throw up your hands and say the problem is unreasonable in a job interview. Or perhaps you analyze the problem and come up with a promising line of reasoning for using algorithmic approaches to attack the problem. Gosh, I don't know. Maybe I'd search the Internet, would be a bad answer. It seems like a recursive divide-and-conquer approach might work would be a much better answer.

This book is an easy-to-read introduction to computer algorithms. It describes a number of important classical algorithms and tells when each is appropriate. It explains how to analyze algorithms to understand their behavior. Most importantly, it teaches techniques that you can use to create new algorithms on your own.

Here are some of the useful algorithms that this book describes:

• Numerical algorithms, such as randomization, factoring, working with prime numbers, and numeric integration
• Methods for manipulating common data structures, such as arrays, linked lists, trees, and networks
• Using more-advanced data structures, such as heaps, trees, balanced trees, and B-trees
• Sorting and searching
• Network algorithms, such as shortest path, spanning tree, topological sorting, and flow calculations

Here are some of the general problem-solving techniques this book explains:

• Brute-force or exhaustive search
• Divide and conquer
• Backtracking
• Recursion
• Branch and bound
• Greedy algorithms and hill climbing
• Least-cost algorithms
• Constricting bounds
• Heuristics

To help you master the algorithms, this book provides exercises that you can use to explore ways that you can modify the algorithms to apply them to new situations. This also helps solidify the main techniques demonstrated by the algorithms.

Finally, this book includes some tips for approaching algorithmic questions that you might encounter in a job interview. Algorithmic techniques let you solve many interview puzzles. Even if you can't use algorithmic techniques to solve every puzzle, you will at least demonstrate that you are familiar with approaches that you can use to solve other problems.

There are several reasons why you should study algorithms. First, they provide useful tools that you can use to solve particular problems such as sorting or finding shortest paths. Even if your programming language includes tools to perform tasks that are handled by an algorithm, it's useful to learn how those tools work. For example, understanding how array and list sorting algorithms work may help you decide which of those data structures would work best in your programs.

Algorithms also teach you methods that you may be able to apply to other problems that have a similar structure. They give you a collection of techniques that you can apply to other problems. Techniques such as recursion, divide and conquer, Monte Carlo simulation, linked data structures, network traversal, and others apply to a wide variety of problems.

Perhaps most importantly, algorithms are like a workout for your brain. Just as weight training can help a football or baseball player build muscle, studying algorithms can build your problem-solving abilities. A professional athlete probably won't need to bench press weights during a game. Similarly, you probably won't need to implement a simple sorting algorithm in your project. In both cases, however, practice can help improve your game, whether it's baseball or programming.

Finally, algorithms can be interesting, satisfying, and sometimes surprising. It never ceases to amaze me when I dump a pile of data into a program and a realistic three-dimensional rendering pops out. Even after decades of study, I still feel the thrill of victory when a particularly complicated algorithm produces the correct result. When all of the pieces fit together perfectly to solve an especially challenging problem, it feels like something at least is right in the world.

Each of the algorithms in this book was included for one or more of the following reasons:

• The algorithm is useful, and a seasoned programmer should be expected to understand how it works and how to use it correctly in programs.
• The algorithm demonstrates important algorithmic programming techniques that you can apply to other problems.
• The algorithm is commonly studied by computer science students, so the algorithm or the techniques it uses could appear in a technical interview.