Various description of Big O – Time complexity algorithms
Understanding the time complexity of Big O and log.
O(log N) means time goes up linearly while the n goes up exponentially. So if it takes 1 second to compute 10 elements, it will take 2 seconds to compute 100 elements, 3 seconds to compute 1000 elements, and so on. https://stackoverflow.com/a/2307330/2489973
What’s logb(n)?
It is the number of times you can repeatedly cut a log of length n into b equal parts before reaching a section of size 1.
https://stackoverflow.com/a/2480061/2489973
The Stackoverflow link covers complexity very nicely. I am putting it here for easy reference.
Big O complexities cheatsheet
Related Articles
Thread Communication in Java
Various description of Big O – Time complexity algorithms 0 Comments Written by Buddhi2017-07-31Interthread communication in Java package org.example; public class ThreadCommunication { private static final int DATA_SIZE = 1000; public static void…
Dependency Injection Summary
Various description of Big O – Time complexity algorithms 0 Comments Written by Buddhi2017-07-31Summary of dependency injection in Spring Boot. All the necessary dependencies are handed over to the object by spring. It frees the object from resolving the…
Spring Annotations and Component Scanning
Various description of Big O – Time complexity algorithms 0 Comments Written by Buddhi2017-07-31Best practices for defining components and scanning them Annotate the class by including the exact place where to scan for components with…