Explore the intricacies of approximate counting in this graduate-level lecture on Computational Complexity Theory. Delve into key concepts such as Chebyshev's Inequality, decision versions, interactive proofs, and the relationship between decision and approximation. Learn about the role of randomness in approximate counting algorithms. Part of Carnegie Mellon University's Course 15-855 for Fall 2017, this 80-minute lecture is taught by Professor Ryan O'Donnell and includes suggested readings from Arora-Barak Chapters 8.2.1 and 8.2.2.
Approximate Counting - Graduate Complexity Lecture at CMU
Overview
Syllabus
Introduction
Chebyshevs Inequality
Approximate counting
Decision version
Interactive proof
Decision vs approximation
Randomness
Taught by
Ryan O'Donnell
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