This time complexity is defined as a function of the input size n using Big-O notation. You can get the time complexity by “counting” the number of operations performed by your code. To recap time complexity estimates how an algorithm performs regardless of the kind of machine it runs on. So, this is paramount to know how to measure algorithms’ performance. In most cases, faster algorithms can save you time, money and enable new technology. In the previous post, we saw how Alan Turing saved millions of lives with an optimized algorithm. By the end of it, you would be able to eyeball different implementations and know which one will perform better without running the code! Also, it’s handy to compare multiple solutions for the same problem. Knowing these time complexities will help you to assess if your code will scale. We are going to learn the top algorithm’s running time that every developer should be familiar with. Learn how to compare algorithms and develop code that scales! In this post, we cover 8 Big-O notations and provide an example or 2 for each.
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