As a developer, time complexity is one of the most interesting concepts you can learn from computer science, and you don’t need a degree to understand it!


It’s interesting because it helps you see why a particular algorithm or program may be slow and what can you do to make it faster.

Slow vs Fast

Is sorting 1 million numbers in 150ms (milliseconds) slow or fast? I think that’s pretty fast, but this is not the question that time complexity is trying to answer. We want to know how an algorithm is going to perform as the “input size” grows.

When we talk about “input size” we are talking about the arguments for the function or method. If a method takes one string as an argument then that’s the input, and the string’s length is the input size.

Big O Notation

With Big O notation we can classify algorithms according to their performance. Some examples of this notation are shown below:

1. O(1) - In this case, O(1) represents a “constant time” algorithm. That means that the algorithm will always take the same time to finish its work, regardless of how much work it has to do.

2. O(n) - In this case, O(n) represents "linear time". Where “n” represents the size of the input (string size, array size, etc). This means that the algorithm will finish its work in a 1:1 relation to input size, so doubling the input size will also double the time it takes to complete the work.

Happy Coding

harryobas:
As a developer, time complexity is one of the most interesting concepts you can learn from computer science, and you don’t need a degree to understand it!


It’s interesting because it helps you see why a particular algorithm or program may be slow and what can you do to make it faster.

Slow vs Fast

Is sorting 1 million numbers in 150ms (milliseconds) slow or fast? I think that’s pretty fast, but this is not the question that time complexity is trying to answer. We want to know how an algorithm is going to perform as the “input size” grows.

When we talk about “input size” we are talking about the arguments for the function or method. If a method takes one string as an argument then that’s the input, and the string’s length is the input size.

Big O Notation

With Big O notation we can classify algorithms according to their performance. Some examples of this notation are shown below:

1. O(1) - In this case, O(1) represents a “constant time” algorithm. That means that the algorithm will always take the same time to finish its work, regardless of how much work it has to do.

2. O(n) - In this case, O(n) represents "linear time". Where “n” represents the size of the input (string size, array size, etc). This means that the algorithm will finish its work in a 1:1 relation to input size, so doubling the input size will also double the time it takes to complete the work.

Happy Coding

is that all?

There is also quadratic, logarithmic,exponential,polynomial and many more complexities.

My work diz semester.
Data structures and algorithm is really cool tho!