If it's higher we only look on the right side. In this case, 6 is lower than 7 so we will look at the left side. Now the numbers we care about are these. Now we do the same thing we did before. We start to look at 4, is 6 higher or lower? We therefore move forward and our list only looks like this: [6]. Now when we do the same thing again, we'll see that the item we are looking at is the number we are searching for.

Boom, we found it! Now if instead, the number we were looking for was 5, we would not have found anything, then our program would return -1 instead. The code above is a simple implementation of binary search, there are different ways to implement a binary search and this is one of them.

This program will go through the list to search for the item we are looking for. Thank you for reading this short post about Big O notation and Binary Search. This post is part of a data structures and algorithms series I'm working on so make sure to leave a follow if you're interested to see more. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Pramit Marattha - Dec Ayyash - Dec Jatin Sharma - Dec 8.

Pragya Sapkota - Nov Once suspended, pluppen will not be able to comment or publish posts until their suspension is removed. Once unpublished, all posts by pluppen will become hidden and only accessible to themselves. If pluppen is not suspended, they can still re-publish their posts from their dashboard.

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We're a place where coders share, stay up-to-date and grow their careers. In this article, I will teach you about big O notation and we will also implement binary search in Python3. Big O notation is a special notation that tells you how fast your algorithm is. Why does big O notation matter?

When you use people's algorithms, it's nice to understand how fast or slow they are. When it comes to measuring algorithms it's not enough to measure the time in seconds because when an algorithm is working with small amounts of operations it can be hard to see a difference. But the more operations you will need to compute, the more time will scale.

With big O notation, you can quickly see how much the algorithm will scale based on input. The amount of input into the algorithm is called n. When you need to search for something you'll probably use some kind of algorithm. One popular algorithm is binary search.

One of the reasons because it's so popular is its efficiency O log n. Now how does binary search work? Imagine a sorted list of numbers. That's simple search. The problem with simple search is that with large lists it will be slow. If I would instead use binary search we could search the list fast even with a large list.

How binary search starts is as the following. Start looking at the number seven. If the number we are looking for is lower we move forward and only look at the left side of the list. If it's higher we only look on the right side. In this case, 6 is lower than 7 so we will look at the left side. Now the numbers we care about are these. Now we do the same thing we did before. We start to look at 4, is 6 higher or lower? We therefore move forward and our list only looks like this: [6].

Now when we do the same thing again, we'll see that the item we are looking at is the number we are searching for. Boom, we found it! Now if instead, the number we were looking for was 5, we would not have found anything, then our program would return -1 instead. The code above is a simple implementation of binary search, there are different ways to implement a binary search and this is one of them.

This program will go through the list to search for the item we are looking for. Thank you for reading this short post about Big O notation and Binary Search. This post is part of a data structures and algorithms series I'm working on so make sure to leave a follow if you're interested to see more. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink.

Pramit Marattha - Dec Ayyash - Dec Jatin Sharma - Dec 8. Pragya Sapkota - Nov Once suspended, pluppen will not be able to comment or publish posts until their suspension is removed.

Once unpublished, all posts by pluppen will become hidden and only accessible to themselves. If pluppen is not suspended, they can still re-publish their posts from their dashboard. Once unpublished, this post will become invisible to the public and only accessible to Elias Juremalm.

Create account Log in. Twitter Facebook Github Instagram Twitch. Big O notation Big O notation is a special notation that tells you how fast your algorithm is. Some common big O notations you will see are the following: O 1 static. O log n , also known as log time. Example: Binary search. O n , also known as linear time. Example: Simple search. Example: A fast sorting algorithm.

Example: A slow sorting algorithm. Example: A super slow algorithm, like the traveling salesperson problem. Binary Search When you need to search for something you'll probably use some kind of algorithm. We therefore move forward and our list only looks like this: [6] Now when we do the same thing again, we'll see that the item we are looking at is the number we are searching for. To sum it up Look at the item in the middle lowest value:highest value of the list If the list is empty return -1 Check if the item is the one you're looking for.

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Retrieved August 20, Archived from the original on June 4, ABN Botany Boyz. Originating from Houston, Texas, and as one of the founding members of the "Original Screwed Up Click," Big Moe started out in music by freestyling on DJ Screw mixtapes like many of his Houston peers before being signed to Wreckshop Records. Big O notation Big O notation is a special notation that tells you how fast your algorithm is.

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