The Basics of Algorithms: Making Computers Think

Introduction

Algorithms are at the heart of everything a computer does. In simple terms, an algorithm is a set of precise instructions that solve a particular problem or accomplish a task. It’s like a recipe for the computer: given some input, it runs through a specific sequence of pre-determined steps and produces an output. For example, the input could be a scrambled list of numbers, which could be run through a sorting algorithm and the output produced would be the sorted list. In everyday life and technology, algorithms are the backbone of all our digital tools, allowing computers to “think”. 

How Algorithms Work

Technically, an algorithm takes input data, processes it step by step, and outputs a result. For instance, a simple algorithm to square a number would take the input N, multiply N by itself, and output the product. In computer science, we often create an algorithm in pseudocode or as a flowchart before its actual code form. This pseudocode describes the logic in plain language, it's exactly like giving instructions. Once we finalise on the algorithm, we have to implement it in a programming language. The algorithm is the complete plan while the code is just one implementation of that plan in languages like Python or Java.

Algorithms can include different control structures. Some are linear (running steps in sequence), some use conditional branches (if/else conditions), and others keep looping until a goal is met. For example, a conditional step might say “if the list is empty, stop; else continue.” A looping step might repeat sorting sublists until the entire list is ordered. Most importantly, every algorithm must eventually terminate rather than loop forever. It must solve the problem in a finite number of steps and be correct (always give the right answer for valid input) otherwise some kind of change is required.

To illustrate, consider two fundamental types of algorithms: sorting and searching. A sorting algorithm arranges elements into order (like alphabetical names or ascending numbers). Common sorting methods include Bubble Sort, Merge Sort, and Quick Sort, each having a different approach. A searching algorithm looks for an item in data. For example, Linear Search checks each element in a list until it finds a match, while Binary Search (for sorted data) can find an item by repeatedly halving the search range. These examples show how algorithms break complex tasks into clear steps. Analysts often compare algorithms by how fast they run (time complexity) or how much memory they take up(space complexity).

Everyday Examples of Algorithms

Algorithms may sound abstract, but we use them every day, often without realizing it. Many popular apps and devices rely on small but clever algorithms behind the scenes. For example, your smartphone’s predictive text feature (suggesting words as you type) is powered by complex pattern-recognition algorithms. Likewise, when you use a browser like Google Chrome, algorithms quickly scan and list billions of web pages so that relevant results appear in a matter of seconds. In fact, search engines locate the most pertinent webpages to your search by using multiple  complex ranking algorithms to give you the best results.
Social media and online shopping also run on algorithms. Platforms like Facebook or Instagram use algorithms to curate your newsfeed, picking which posts and ads to show you based on your interests and behavior. Shopping sites (such as Amazon or Netflix) use recommendation algorithms that suggest products or shows you might like, based on what you and others with similar habits have viewed. These make browsing more personalized and efficient.
Navigation is another everyday example. When you get directions from Google Maps or Waze, an algorithm is calculating the fastest route for you in real time. It takes into account map data, distances, and even live traffic conditions to update your route as you drive.

Cutting-Edge Algorithms and Research

Computing is always evolving, and researchers are constantly creating new, more complex algorithms to tackle harder problems. A fairly new area is quantum computing, which uses quantum-mechanical phenomena to process information. Quantum algorithms leverage qubits that can exist in superposition (more than one  state at once) and be entangled with each other. For example, Shor’s algorithm is a quantum algorithm that can factor extremely large numbers exponentially faster than any other conventional methods. Grover’s algorithm is another amazing example which uses quantum parallelism to search an unsorted database in roughly √N time instead of N, resulting in faster output. Both of these instances show the tru potential of quantum computing and using algorithms, rather than conventional manual methods.
Conclusion

Algorithms truly are the building blocks of a computer. They let us break down complex problems into simple steps and create logical,understandable solutions. From the basic sorting and searching routines to the exotic quantum algorithms of tomorrow, algorithms are not just flowcharts but a way to think. As computers grow more advanced, I expect algorithms will become even more important because they could become self-sustaining or even non-terminating.

Bibliography

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“Algorithms Tutorial.” *GeeksforGeeks*, updated 12 Apr. 2025, www.geeksforgeeks.org/dsa/fundamentals-of-algorithms/.

“7 Examples of Algorithms in Everyday Life for Students.” *Learning.com*, 18 Dec. 2024, www.learning.com/blog/7-examples-of-algorithms-in-everyday-life-for-students/.

Nikolopoulou, Kassiani. *“What Is an Algorithm? | Definition & Examples.”* Scribbr, 29 Aug. 2023, www.scribbr.com/ai-tools/what-is-an-algorithm/.

“Top Quantum Algorithms to Know in 2024.” *Quantum Zeitgeist*, 10 Nov. 2024, quantumzeitgeist.com/top-quantum-algorithms-to-know-in-2024/. (No author listed).