# Basic Algorithmic Techniques

Chapter
Part of the Computational Biology book series (COBO, volume 7)

An algorithm is a step-by-step procedure for solving a problem by a computer. Although the act of designing an algorithm is considered as an art and can never be automated, its general strategies are learnable. Here we introduce a few frameworks of computer algorithms including greedy algorithms, divide-and-conquer strategies, and dynamic programming.

This chapter is divided into five sections. It starts with the definition of algorithms and their complexity in Section 2.1. We introduce the asymptotic O-notation used in the analysis of the running time and space of an algorithm. Two tables are used to demonstrate that the asymptotic complexity of an algorithm will ultimately determine the size of problems that can be solved by the algorithm.

Then, we introduce greedy algorithms in Section 2.2. For some optimization problems, greedy algorithms are more efficient. A greedy algorithm pursues the best choice at the moment in the hope that it will lead to the best solution in the end. It works quite well for a wide range of problems. Huffman’s algorithm is used as an example of a greedy algorithm.

## Keywords

Greedy Algorithm Output Sequence Recursive Call Fibonacci Number Longe Common Subsequence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.