What is Big O in JavaScript - Part-1
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Big O notation is a method for measuring the efficiency of algorithms in terms of time and space complexity relative to input size. It categorizes algorithms into various expressions like O(1), O(n), and O(n^2) based on their performance in the worst-case scenario. The document provides examples comparing different algorithm implementations for summing numbers and emphasizes the importance of understanding Big O for optimizing solutions.
What is Big O in JavaScript - Part-1
- 2. What is Big O? Big O Notation is used to show how efficient an algorithm or function is, in relative to its input size. To measure the efficiency of an algorithm, we need to consider two things: Big-O captures the upper bound to show how much time or memory an algorithm would require in the worst case scenario as the input size grows. ● Time Complexity: How much time does it take to run completely? ● Space Complexity: How much extra space does it require in the process?
- 3. Each and every function or logic or methods or class that we write falls under one of the Big O expressions listed below, O(1) 🤩 Best O(Log n) 😃 Better O(n) O(n log n) 🙂 Normal O(n^2) 🙃 Not Bad ☹ Bad O(n^3) 😖 Worst How Big O is expressed?
- 4. We programmers always come up with multiple solutions. But how do we make sure that approach we took is the best? This is where Big O comes into picture. Let us consider an example to sum up the numbers from 1 to n. // A function with O(n) function addUpto(n) { let total = 0; for(let i=1; i<=n; i++){ total += i; } return total; } addUpto(10); //55 // A function with O(1) function addUpto(n) { return n * ( n + 1 ) / 2; } addUpto(10); //55 Why we need a Big O?
- 5. STAY AROUND FOR THE NEXT PART! We will cover a bit more on the Big O.