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Lecture Notes for CS 6550: Advanced Graduate Algorithms (Randomized and Approximation Algorithms) by Eric Vigoda et al. is a nonfiction available to read on EtoBox.
What is Lecture Notes for CS 6550: Advanced Graduate Algorithms (Randomized and Approximation Algorithms) about?
Introduction Karger's min-cut algorithm The algorithm Analysis The Karger-Stein algorithm The algorithm Analysis Classical Median Finding Algorithms Find Median With High Probability Analysis Runtime Analysis Failure Probability Bernoulli Random Variables and Chebychev's Inequality Proofs Chernoff Bounds Markov and Chebyshev Inequality Chernoff ``argument" for Bin(n,12) Chernoff Inequality Warm-up example: Median estimate Problem definition Solution Analysis Streaming Problem definition Solution Analysis Example: Frequency Moments F2 Estimation Distinct Elements Pairwise Independent Simple Construction Hashing Application: Streaming The AMS algorithm Analysis of Algorithm Space Complexity Failure Probability Maximal Independent Sets Expected Running Time Derandomizing MIS History and Open Questions Data Streaming The algorithm Preliminaries: The Data Stream Model The Basic Setup The Quality of an Algorithm's Answer Variations of the Basic Setup Finding Frequent Items Deterministically The Problem Frequency Estimation: The Misra–Gries Algorithm Analysis of the Algorithm Finding the Frequent Items Exercises Estimating the Number of Distinct Elements The Problem The Tidemark Algorithm
Who reads Lecture Notes for CS 6550: Advanced Graduate Algorithms (Randomized and Approximation Algorithms)?
It is typically read by self-directed learners exploring a subject in depth.
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- Author
- Eric Vigoda et al.
- Publisher
- Georgia Institute of Technology
- Published
- 2019
- Language
- EN
- Category
- nonfiction
- Subjects
- Computer Science, Programming, Algorithms And Data Structures
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