Before you jump into the study of algorithms, you need a little background. To begin with, you need to know that, simply stated, an *algorithm* is a recipe for getting something done. *It defines the steps for performing a task in a certain way*.

That definition seems simple enough, but no one writes algorithms for performing extremely simple tasks. No one writes instructions for how to access the fourth element in an array. It is just assumed that this is part of the definition of an array and that you know how to do it (if you know how to use the programming language in question).

Normally people write algorithms only for difficult tasks. Algorithms explain how to find the solution to a complicated algebra problem, how to find the shortest path through a network containing thousands of streets, or how to find the best mix of hundreds of investments to optimize profits.

This chapter explains some of the basic algorithmic concepts you should understand if you want to get the most out of your study of algorithms.

It may be tempting to skip this chapter and jump to studying specific algorithms, but you should at least skim this material. Pay close attention to the section “Big O Notation,” because a good understanding of runtime performance can mean the difference between an algorithm performing its task in seconds, hours, or not at all.

To get the most out of an algorithm, you must be able to do more than simply follow its steps. You need to understand ...

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