Introduction When executing a particular task using algorithms, one common thing that hits every problem solver brain is the efficient and fast algorithm to solve that problem. But, what do exactly fast and efficient means? Is it the measure of real processing time? No, it's not the measure of real time like second and minutes because the old computer with Pentium processor may take a long time to process the same algorithm than the new Intel i3 processor, or, A bad algorithm written in Assembly may execute faster than a good algorithm written in python. So, run time of program can't be considered to measure algorithmic efficiency. Hence, to measure the algorithmic efficiency, the concept of Asymptotic Complexity of a program and a notation for describing this called Big Oh was introduced. Big Oh is the worst case, Big Omega and Big Theta are best and average case notations. Worst case means we are mostly unlucky for that problem domain , i.e precondition of task do
This blog is the part of my day to day learning. the only way to improve the efficiency of a programmer is by writing, Writing playing with codes and errors. I write about Software Engineering, Machine learning, Artificial intelligence & Data Structure and algorithms and for language like python, Ruby, Django and rails