Algorithm
A precise, finite sequence of instructions for solving a problem – the basis of all software, from search results to AI decisions.
Algorithms are the invisible foundation of the digital world. Every Google search, every Spotify recommendation, every GPS route is based on algorithms. In software development, choosing the right algorithm drives performance, scalability and user experience. In the age of AI, algorithms take on a new role: they learn from data and make decisions on their own.
What is Algorithm?
An algorithm is a precise, finite sequence of instructions that turns a defined input into a desired output. Algorithms solve problems systematically: sorting data, finding the shortest path, encrypting messages or recognising patterns in images. They must be unambiguous (each step is clearly defined), finite (the algorithm terminates after finitely many steps) and general (they solve a class of problems, not just one case). In practice algorithms are implemented in programming languages such as Python, Java or TypeScript.
How does Algorithm work?
An algorithm takes input, processes it according to fixed rules and produces output. Example sorting: input is an unsorted list of numbers; the algorithm compares and swaps elements by defined rules; output is the sorted list. Efficiency is measured with Big O notation: O(n log n) for efficient sorting, O(n²) for naive. In machine learning, the algorithm learns its rules from training data instead of a developer programming them manually.
Practical Examples
Google PageRank: Scores relevance of web pages from link structure and thus determines search result order.
Dijkstra's algorithm: Finds the shortest path in a graph – basis for every navigation app and logistics planning.
AES encryption: Symmetric encryption algorithm securing data with 128, 192 or 256 bits – standard for HTTPS and data protection.
TF-IDF: Scores importance of terms in documents – foundation of many search engines and text analysis.
Gradient descent: Optimisation algorithm that trains neural networks by gradually minimising error.
Typical Use Cases
Search and recommendation: Filter relevant results from billions of data points
Cryptography and security: Encrypt and decrypt data to protect sensitive information
Routing and logistics: Compute optimal routes considering cost, time and capacity
Machine learning: Algorithms that learn from data and produce predictions, classifications or recommendations
Image processing: Object detection, face recognition and medical image analysis
Advantages and Disadvantages
Advantages
- Systematic problem-solving: Complex tasks are broken into manageable steps
- Reproducibility: Same input always yields same output (for deterministic algorithms)
- Scalability: Efficient algorithms process billions of data points in seconds
- Automation: Repetitive tasks are solved without human intervention
Disadvantages
- Bias risk: AI algorithms can inherit and amplify bias from training data
- Opacity: Complex algorithms (e.g. deep learning) are hard to interpret (black box)
- Limited flexibility: Classical algorithms fail at tasks requiring human intuition
- Resource use: Complex algorithms need significant compute and energy
Frequently Asked Questions about Algorithm
What is the difference between an algorithm and a program?
Can algorithms be creative?
Why does algorithm efficiency matter?
Related Terms
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