algorithm - Difference between average case and amortized analysis

Question

I am reading an article on amortized analysis of algorithms. The following is a text snippet.

Amortized analysis is similar to average-case analysis in that it is concerned with the cost averaged over a sequence of operations. However, average case analysis relies on probabilistic assumptions about the data structures and operations in order to compute an expected running time of an algorithm. Its applicability is therefore dependent on certain assumptions about the probability distribution of algorithm inputs.

An average case bound does not preclude the possibility that one will get “unlucky” and encounter an input that requires more-than-expected time even if the assumptions for probability distribution of inputs are valid.

My questions about above text snippet are:

1. In the first paragraph, how does average-case analysis “rely on probabilistic assumptions about data structures and operations?” I know average-case analysis depends on probability of input, but what does the above statement mean?

2. What does the author mean in the second paragraph that average case is not valid even if the input distribution is valid?

Thanks!

Answer 1

Average case analysis makes assumptions about the input that may not be met in certain cases. Therefore, if your input is not random, in the worst case the actual performace of an algorithm may be much slower than the average case.

Amortized analysis makes no such assumptions, but it considers the total performance of a sequence of operations instead of just one operation.

Dynamic array insertion provides a simple example of amortized analysis. One algorithm is to allocate a fixed size array, and as new elements are inserted, allocate a fixed size array of double the old length when necessary. In the worst case a insertion can require time proportional to the length of the entire list, so in the worst case insertion is an O(n) operation. However, you can guarantee that such a worst case is infrequent, so insertion is an O(1) operation using amortized analysis. Amortized analysis holds no matter what the input is.