This post provides a comprehensive explanation of Big O Notation, which is used to classify algorithms based on their time and space complexity as the input size grows. Examples for different time complexities like O(n), O(1), O(n^2), O(n*m), O(log n), O(n log n), O(2^n), and O(n!) are provided with detailed algorithms and coding snippets. The goal is to help readers understand and recall these concepts for coding interviews.
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IntroductionWhat is Big O time complexity?O(n) - Sure, give me an exampleO(1) - First and only exceptionO(n^2) - This seems easy enoughO(n*m) - Okay, now you're just adding lettersO(log n) - What?O(n log n) - Now you're just making stuff upO(2^n) - I should have seen it comingO(n!) - Make it end, please!So there you have it!5 Comments
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