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I've found many but most of them are too complicated for me to understand.
In computer science Big O refers to the complexity or rate that something increases. For example consider the simple bubble sort with n elements. The number of comparisons and potential swaps is (n-1)+(n-2)+.... We would say the simple bubble sort has a time complexity of O(n^2) (n squared) meaning the time it takes to sort n elements increases at about the square of n (big O only deals with the main variable). This is why the simple bubble sort is a poor choice for big lists. The more efficient heap sort has a time complexity of O(n log(n)) (n times log base 2 n). Big O doesn't always refer to time. It can also describe how space, memory, bandwidth or other resources increases.
< Been using textbooks, one Discrete Mathematics for computer science, Absolute java, and a bunch of other crap my professor has written lol and given to us as a "textbook".
Youtube :P or read a book
Thank you!!!
youtube
