# Tag algorithm analysis

## Asymptotic Notations – II

In this post, we will see the remaining two asymptotic notations. The algorithm we were discussing was the linear search. We had calculated the estimated time complexity as 2n + 3. To read the previous post, click here. Big-Omega – The Lower Bound function f (n) belongs to big-omega of g (n) if there exist

## Asymptotic Notations – I

In this post, I will discuss the time functions in asymptotic notations. You probably have heard about asymptotic notations. They are the Big-Oh, Big-Omega, and Theta functions. I had read in details about them when I was in doing my B. Tech [I couldn’t recollect which semester, and that is a pity]. It is going

## Why do Exponential Functions Hurt?

Before we move further, let us recap the types of time complexity functions we have encountered till now. We will also see that algorithms with exponential functions are evil. Here is the list of functions we have seen till now. The sequence of these functions is the same as written above. That is, a quadratic

## Time & Space Complexity – IV

In this post, I am going to talk about the time complexity of one more type of algorithms. Once again, we do not need to discuss the space complexity as it is going to be the same as all the other for loops. In the previous post, I had given a small introduction of logarithmic

## Time & Space Complexity – II

Click here to go to the previous post. While calculating the space complexity of the algorithm, we established that one variable would take one unit of space. Space Complexity of Algorithm 2 Click here to view Algorithm 2. The easiest way to calculate the space complexity is to list out the variables and add 1