Probabilistic complexity classes
In complexity theory, ZPP (zero-error probabilistic polynomial time) is the complexity class of problems for which a probabilistic Turing machine exists with these properties: * It always returns the correct YES or NO answer. * The running time is polynomial in expectation for every input. In other words, if the algorithm is allowed to flip a truly-random coin while it is running, it will always return the correct answer and, for a problem of size n, there is some polynomial p(n) such that the average running time will be less than p(n), even though it might occasionally be much longer. Such an algorithm is called a Las Vegas algorithm. Alternatively, ZPP can be defined as the class of problems for which a probabilistic Turing machine exists with these properties: * It always runs in polynomial time. * It returns an answer YES, NO or DO NOT KNOW. * The answer is always either DO NOT KNOW or the correct answer. * It returns DO NOT KNOW with probability at most 1/2 for every input (and the correct answer otherwise). The two definitions are equivalent. The definition of ZPP is based on probabilistic Turing machines, but, for clarity, note that other complexity classes based on them include BPP and RP. The class BQP is based on another machine with randomness: the quantum computer. (Wikipedia).
Depth complexity and communication games - Or Meir
Or Meir Institute for Advanced Study; Member, School of Mathematics September 30, 2013 For more videos, visit http://video.ias.edu
From playlist Mathematics
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This is part of a series of lectures on the Zermelo-Fraenkel axioms for set theory. We discuss the powerset axiom, the strongest of the ZF axioms, and explain why the notion of a powerset is so hard to pin down precisely. For the other lectures in the course see https://www.youtube.com
From playlist Zermelo Fraenkel axioms
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