Thank you for your article.
It's easy to read and understand.
I particularly like the diagram regarding Big O notations.
Also, I would have liked to see a simple example of the O(Log N) example, but I understand that the O(Log N) is a bit difficult to explain.
Would you like to learn about complex technical concepts in a very simplified and down-to-earth way? Join me as I help you gain a solid understanding of them in a clear, basic, and concise manner.
Thank you for reading, and your very honest and beautiful comments, it means alot.
Examples on O(Log N) are quite long and alittle bit complex, adding them here would have overstepped the goal of this article, which is making this subject as simple as possible.
You can also go through some resources on binary search algorithm.
Would you like to learn about complex technical concepts in a very simplified and down-to-earth way? Join me as I help you gain a solid understanding of them in a clear, basic, and concise manner.
Hello Onyeanuna Prince,
Thank you for your article.
It's easy to read and understand.
I particularly like the diagram regarding Big O notations.
Also, I would have liked to see a simple example of the O(Log N) example, but I understand that the O(Log N) is a bit difficult to explain.
Thank you for reading, and your very honest and beautiful comments, it means alot.
Examples on O(Log N) are quite long and alittle bit complex, adding them here would have overstepped the goal of this article, which is making this subject as simple as possible.
You can also go through some resources on binary search algorithm.
The binary search algorithm is simple.
Basically:
Do this until you run out of an array.
The concept of Log N (base 2, also written Lg N sometimes) is to discard half your problem with each iteration.
For such cases you're problem will grow only logarithmically.
This is perfectly explained!!!
This is amazing 🤩