programming pointers

Algorithms performance: big O notation: simplified short notes

 The big O notation is used to analyze runtime time complexity. big O notation provides an abstract measurement by which we can judge the performance of algorithms without using mathematical proofs. Some of the most common big O notations are:

Brief explanation:     
Cubic and exponential algorithms should only ever be used for very small problems (if ever!); avoid them if feasibly possible. If you encounter them then this is really a signal for you to review the design of your algorithm always look for algorithm optimization particularly loops and recursive calls. 

The biggest asset that big O notation gives us is that it allows us to essentially discard things like hardware means if you have two sorting algorithms, one with a quadric run time and the other with a logarithmic run time then logarithmic algorithm will always be faster than the quadratic one when the data set becomes suitably large. This applies even if the former is ran on a machine that is far faster than the latter, Why?

Because big O notation isolates a key factor in algorithm analysis: growth. An algorithm with quadratic run time grows faster than one with logarithmic run time.

Note: The above notes are for quick reference. Understanding algorithmic performance is a complex but interesting field. I would recommend picking a good book to understand the nitty-gritty of big O and other notations.

 

 

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