Lecture #15 and #16
نویسندگان
چکیده
In this lecture, we will cover amortized analysis, which is a method where we consider the average work done over an entire sequence of operations instead of just considering the worst-case cost/time for a single operation; by doing such an analysis, we can obtain tighter bounds in scenarios when expensive operations occur rarely enough that we can charge their cost to more frequent inexpensive operations. We cover such charging arguments as well as the potential method, which is another powerful tool for doing amortized analysis. We will apply both these techniques to binary counting, the union-find data structure (which is needed to implement Kruskal’s algorithm efficiently), and dynamic arrays.
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