What Is an Algorithm?

Person's hand typing code into an algorithm on a computer.
The algorithm is the basic technique used to get the job done. Laflor / Getty Images

In today's digital age, algorithms are the invisible architects shaping the landscape of technology and information. These complex sets of rules and instructions underpin almost every aspect of our digital lives, from the way search engines prioritize results to how social media feeds present content. But what is an algorithm, exactly?

At its core, an algorithm is a methodical, step-by-step procedure for solving problems or accomplishing tasks. Whether it's a simple formula for adding numbers or a sophisticated protocol for machine learning, algorithms act as the backbone of software applications, ensuring tasks are performed efficiently and effectively. In this article, we'll demystify the concept of algorithms, exploring their origins, how they work, and their impact on society and technology.


The Role of Algorithms in Computer Science

To make a computer do anything, you have to write a computer program. To write a computer program, you have to tell the computer, step by step, exactly what you want it to do. The computer then "executes" the program, following each step mechanically, to accomplish the end goal.

When you are telling the computer what to do, you also get to choose how it's going to do it. That's where computer algorithms come in. The algorithm is the basic technique used to get the job done. Let's follow an example to help get an understanding of the algorithm concept.


Multiple Algorithms For Different Circumstances

Let's say that you have a friend arriving at the airport, and your friend needs to get from the airport to your house. Here are four different algorithms that you might give your friend for getting to your home:

The taxi algorithm:


  1. Go to the taxi stand.
  2. Get in a taxi.
  3. Give the driver my address.

The call-me algorithm:

  1. When your plane arrives, call my cell phone.
  2. Meet me outside baggage claim.

The rent-a-car algorithm:

  1. Take the shuttle to the rental car place.
  2. Rent a car.
  3. Follow the directions to get to my house.

The bus algorithm:

  1. Outside baggage claim, catch bus number 70.
  2. Transfer to bus 14 on Main Street.
  3. Get off on Elm street.
  4. Walk two blocks north to my house.

All four of these algorithms accomplish exactly the same goal, but each algorithm does it in completely different way. Each algorithm also has a different cost and a different travel time. Taking a taxi, for example, is probably the fastest way, but also the most expensive. Taking the bus is definitely less expensive, but a whole lot slower. You choose the algorithm based on the circumstances.


Sorting Algorithms

In computer programming, there are often many different ways -- algorithms -- to accomplish any given task. Each algorithm has advantages and disadvantages in different situations. A sorting algorithm is one approach where a lot of research has been done, because computers spend a lot of time sorting lists.

Here are five different sorting algorithms:


  • Bin sort
  • Merge sort
  • Bubble sort
  • Shell sort
  • Quicksort

If you have a million integer values between 1 and 10 and you need to sort them, the bin sort is the right algorithm to use. If you have a million book titles, the quicksort might be the best algorithm. By knowing the strengths and weaknesses of the different algorithms, you pick the best one for the task at hand.


Types of Algorithms

In computer science, algorithms can be broadly categorized into several main types based on their methodology, purpose, and application area. From encryption algorithm to recursive algorithm, there are many uses for different programming languages.

Here's an overview of the main types of algorithms commonly used:


Searching Algorithm

A search algorithm is designed to retrieve information stored within a data structure. Examples include linear search, binary search, and search algorithms used in databases and search engines.

Dynamic Programming Algorithm

This type optimizes problems by breaking them down into simpler subproblems. Examples include the Fibonacci series generation, the knapsack problem, and algorithms for finding the shortest paths in a graph, like Bellman-Ford and Floyd-Warshall algorithms.

Greedy Algorithm

Greedy algorithms aim for the best solution at the moment without considering future consequences. They are used in problem solving, such as the Kruskal’s and Prim’s algorithms for finding the minimum spanning tree in a graph.

Backtracking Algorithm

This type is used in constraint satisfaction problems, where you incrementally build candidates to the solutions, and abandon a candidate ("backtrack") as soon as it determines that the candidate cannot possibly be completed to a valid solution. Examples include solving the N-Queens problem and puzzles like sudoku.

Machine Learning Algorithm

These are designed to allow computers to learn from data and make predictions or decisions. They can be further divided into categories like supervised learning, unsupervised learning, reinforcement learning, and deep learning algorithms.

Randomized Algorithm

Aptly, randomized algorithms use a degree of randomness as part of their logic. They are useful for problems where a deterministic approach is inefficient. Examples include the Monte Carlo method and the Randomized Quicksort.

Brute Force Algorithm

A brute force algorithm systematically explores all possible solutions to a problem to find the correct one. It is simple and guarantees a solution if it exists, but can be inefficient for large or complex problems due to its exhaustive nature.

These categories are not mutually exclusive, and many algorithms may fit into multiple categories depending on their application and methodology. Understanding these types helps in choosing the right algorithm for solving a particular problem efficiently.


Algorithms Work Toward the Optimal Solution

Regardless of the programming language being used, an algorithm produces a result or output based on a set of inputs and a defined series of computational steps. The nature of this output can vary widely depending on the algorithm's purpose and the problem it is designed to solve.

Here are some common types of outputs produced by algorithms:


  1. Solutions to problems: Algorithms are often designed to solve specific problems, such as calculating the shortest path between two points on a map, sorting a list of numbers, or finding the greatest common divisor of two integers.
  2. Decisions or classifications: In machine learning and decision-making algorithms, the output can be a decision or classification. For example, a spam detection algorithm classifies emails as "spam" or "not spam," while a recommendation algorithm decides which products or content to suggest to a user.
  3. Numerical values: Many algorithms perform calculations and produce numerical values as outputs. This includes algorithms for mathematical computations, such as finding the root of an equation or calculating statistical measures from a dataset.
  4. Data structures: Some algorithms organize or manipulate data and produce modified data structures as output. For instance, sorting algorithms rearrange lists or arrays into a specific order, while graph algorithms might output a tree or a set of connected components within a graph.
  5. Visual representations: Algorithms in computer graphics and visualization convert numerical data into visual forms, such as images, charts, or animations. For example, rendering algorithms generate the 2D images from 3D models that we see in video games and simulations.
  6. Textual content: Algorithms designed for natural language processing or generation can produce textual content as output. This includes algorithms that generate human-like text, translate languages, or summarize long documents.
  7. Signals or physical actions: In robotics and embedded systems, algorithms can produce signals that control motors or other actuators, resulting in physical actions in the real world. This could be as simple as turning on a light or as complex as coordinating the movements of a robotic arm.

Whether you're looking to identify patterns, comb through initial data, input data and variables, or find a mathematical process, efficient algorithms can automate these processes. Just imagine what they'll be capable of doing in the future!

This article was updated in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

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Algorithm FAQ

What is an algorithm in simple terms?
When you are telling the computer what to do, you also get to choose how it's going to do it. That's where computer algorithms come in. The algorithm is the basic technique, or set of instructions, used to get the job done.
What is an example of an algorithm?
A recipe is one example of an algorithm since it is a finite list of instructions, although an algorithm may be more specific than a recipe.
What are the three parts of an algorithm?
An algorithm needs data inputs, data processing, and data outputs.
What is the use of algorithm in computer programming?
Algorithms are integral to the way computer systems process data. Most computer programs consist of algorithms that follow specific instructions to conduct a simple task.