# Algorithms

## Introduction

An algorithm is a procedure that takes input and produces output to accomplish a specific task [1, P. 3].

An algorithm should solve a general, well-specified problem. A specification for an algorithm should include the complete set of instances it will operate on, and the algorithm’s output after running on one of these instances [1, P. 3].

A good algorithm is:

• Correct
• Efficient
• Easy to implement

It’s not always possible to meet these goals [1, P. 4].

An algorithm is commonly expressed in one of the following forms:

1. English
2. Pseudocode
3. A programming language

[1, P. 12]

The most important property of an algorithm is that it’s correct.

One way to demonstrate the correctness of an algorithm is a mathematical proof.

A mathematical proof consists of four parts:

1. A statement of what you’re trying to achieve
2. A set of assumptions you take to be true
3. A chain of reasoning that takes you from the assumptions to the statement you are attempting to prove
4. A little square (∎) or QED to denote the end of the proof

[1, P. 11]

### Problems and properties

In order to demonstrate correctness, your problem must be well-specified.

Problem specifications have two parts:

1. The set of allowed input instances
2. The required properties of the algorithm output

[1, P. 12]

You should avoid asking ill-defined questions. Asking “what is the best path?” is ill-defined: what does best mean? You should be more specific, for example: “which is the fastest path to take?” [1, P. 13].

### Demonstrating incorrectness

You can prove an algorithm incorrect by providing an input that produces the incorrect output. These are called counter-examples [1, P. 13].

Good counter-examples are verifiable and simple [1, P. 13].

### Induction and recursion

Failure to prove an algorithm as incorrect does not make it correct. Mathematical induction is a common method to prove the correctness of an algorithm.

The way to prove a predicate $P$ through induction is to:

• Prove the case $P(0)$
• Assume that $P(k)$ is true
• Prove $P(k+1)$

see this video teaching proof by induction for further explanation

### Sigma notation

Summations are common in algorithm analysis.

Sigma notation is a way of expressing summation formulas. For example, the sum of 1 to $n$ in sigma notation is:

see this video explaining sigma notation for further explanation

## Modelling the problem

Modelling is the process of formulating your application in terms of well-defined, well-understood problems. Modelling can eliminate the need to create your own algorithm, since you can rephrase your problem to use a pre-written algorithm [1, P. 19].

Most problems are real-world problems. For example, you might need to create a system to route traffic. Algorithms don’t work on real-world objects, they work abstractions, like a graph. In order to write effective algorithms you must learn how to describe your problems in terms of abstract strictures [1, P. 19].

In order to model a problem, you should have a solid understanding of the data structures available to you.

## Conclusion

Algorithms are procedures that accomplish a specific task.

You can determine the correctness of an algorithm using a mathematical proof, one way of doing this is by using mathematical induction.

Algorithms work on abstract objects. In order to write algorithms for real-world problems, you need to model your real-world problems in terms of abstract objects that an algorithm can work on.

1.  S. Skiena, The Algorithm Design Manual, 2nd ed. Springer, 2008.