**Algorithm Speed in Practice**

We may not be dealing with sorting or searching algorithms more often in the real life. But there are situations where we need to think of estimation. While encountering a single loop, it’s easy to identify now that we are dealing with a O(n) algorithm. It is O(n×m) if it has a nested loop.

*Estimate the Order of Your Algorithms*

If we have an algorithm of order O(n2), we can try to bring it down to O(nlog(n)). If we don’t know how long it takes, the easiest way is to test with different set of inputs and plot a graph. With around 3 - 4 points in the graph, we’ll be able to estimate the order of the algorithm.

But this is not always the case. A simple O(n2) algorithm works better than O(nlogn) for smaller values of n. At the end of the day, what really matters is how long our code takes to execute with real data in the production environment. So, always

*Test Your Estimates*

In some other cases, the fastest is not always the best to do the job. We have to make sure that the algorithm is apt for our problem, before going any further.

*- summary of*

**Algorithm Speed**, from**The Pragmatic Programmer: from Journeyman to Master**
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