**by James Aspnes**

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Lecture notes for the Yale Computer Science course CPSC 469/569 Randomized Algorithms. Suitable for use as a supplementary text for an introductory graduate or advanced undergraduate course on randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, applications of martingales and Markov chains, and the LovĂˇsz Local Lemma. Algorithmic topics include analysis of classic randomized algorithms such as Quicksort and Hoare's FIND, randomized tree data structures, hashing, Markov chain Monte Carlo sampling, randomized approximate counting, derandomization, quantum computing, and some examples of randomized distributed algorithms. ### Table of Contents

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This open book is licensed under a Creative Commons License (CC BY-SA). You can download Notes on Randomized Algorithms ebook for free in PDF format (3.3 MB).

Chapter 1

Randomized algorithms

Chapter 2

Probability theory

Chapter 3

Random variables

Chapter 4

Basic probabilistic inequalities

Chapter 5

Concentration bounds

Chapter 6

Randomized search trees

Chapter 7

Hashing

Chapter 8

Martingales and stopping times

Chapter 9

Markov chains

Chapter 10

Approximate counting

Chapter 11

The probabilistic method

Chapter 12

Derandomization

Chapter 13

Quantum computing

Chapter 14

Randomized distributed algorithms

Title

Notes on Randomized Algorithms

Subject

Computer Science

Publisher

Self-publishing

Published

2020

Pages

459

Edition

1

Language

English

PDF Size

3.3 MB

License

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