What is amortized analysis of algorithms? [closed]

Amortized analysis doesn't naively multiply the number of invocations with the worst case for one invocation.

For example, for a dynamic array that doubles in size when needed, normal asymptotic analysis would only conclude that adding an item to it costs O(n), because it might need to grow and copy all elements to the new array. Amortized analysis takes into account that in order to have to grow, n/2 items must have been added without causing a grow since the previous grow, so adding an item really only takes O(1) (the cost of O(n) is amortized over n/2 actions).

Amortized analysis is not the same as an "average performance" - amortized analysis gives a hard guarantee on what the performance will do if you do so much actions.

There are a lot of answers to "what", but none to "why".

As everyone else has said, asymptotic analysis is about how the performance of a given operation scales to a large data set. Amortized analysis is about how the average of the performance of all of the operations on a large data set scales. Amortized analysis never gives worse bounds than asymptotic, and sometimes gives much better ones.

If you are concerned with the total running time of a longer job, the better bounds of amortized analysis are probably what you care about. Which is why scripting languages (for instance) are often happy to grow arrays and hash tables by some factor even though that is an expensive operation. (The growing can be a O(n) operation, but amortized is O(1) because you do it rarely.)

If you are doing real time programming (individual operations must complete in a predictable time), then the better bounds from amortized analysis don't matter. It doesn't matter if the operation on average was fast, if you failed to finish it in time to get back and adjust the bandsaw before it cut too far...

Which one matters in your case depends on exactly what your programming problem is.