You are a software developer, and you often use algorithms to solve problems and create applications. But what are algorithms, and why are they important for your work? In this article, you will learn how to explain algorithms to your friend in a simple and engaging way.
An algorithm is a set of instructions or rules that tells a computer how to perform a specific task. For example, if you want to sort a list of names alphabetically, you need an algorithm that tells the computer how to compare and arrange the names in order. Algorithms can be written in different programming languages, such as Python, Java, or C++, and they can vary in complexity and efficiency.
A algorithm is recipe. Not literally. How do you find a word in (printed) dictionary? There is an algorithm (recipe) for that? You open the dictionary at the middle and look at the words at that page? If the word you are looking for is alphabetically after the words on that page you open the at the middle the remaining pages. And so on. Well, this is exactly the binary search algorithm.
To design an algorithm, you need to first understand the problem you want to solve and the input and output you expect. For example, if you want to find the shortest path between two cities on a map, you need to know the locations of the cities, the distances between them, and the criteria for choosing the shortest path. Then, you need to break down the problem into smaller and simpler steps, and write the algorithm using pseudocode or flowcharts. Pseudocode is a way of writing the algorithm in plain English, while flowcharts are diagrams that show the logic and flow of the algorithm.
To test an algorithm, you need to run it on some sample input and check if it produces the correct output. For example, if you have an algorithm that calculates the average of a list of numbers, you need to test it with different lists of numbers and compare the results with the expected average. You also need to check if the algorithm handles errors and exceptions, such as invalid input, empty input, or division by zero. Testing helps you find and fix bugs and improve the quality and performance of your algorithm.
To analyze an algorithm, you need to measure how well it performs in terms of time and space. Time refers to how long it takes for the algorithm to complete the task, and space refers to how much memory or storage it uses. You can use different tools and techniques to analyze algorithms, such as Big O notation, time complexity, and space complexity. Big O notation is a way of expressing how the time or space of an algorithm changes as the input size increases. Time complexity and space complexity are measures of how efficient an algorithm is in terms of time or space.
To compare algorithms, you need to consider different factors, such as the problem domain, the input size, the accuracy, the reliability, and the trade-offs. For example, if you have two algorithms that sort a list of numbers, you need to compare how they work on different types of lists, such as sorted, reverse-sorted, random, or large lists. You also need to compare how accurate and reliable they are, and what trade-offs they make between time and space. Comparing algorithms helps you choose the best algorithm for your problem and optimize your solution.
To learn algorithms, you need to practice and apply them to different problems and scenarios. You can use online platforms and resources, such as books, courses, blogs, podcasts, and videos, to learn the concepts and techniques of algorithms. You can also use online platforms and tools, such as Codecademy, HackerRank, LeetCode, and GitHub, to practice and improve your skills in writing and testing algorithms. Learning algorithms helps you develop your logical thinking, problem-solving, and coding abilities, and enhance your career prospects.
Knowing about the practical applications of algorithms in the real world also helps to learn and remember them. So, while learning any algorithm, make sure to look at the practical applications.