Amortized Analysis and Hashing Ted Pyne Table of Contents ¡ñ Amortized Analysis ¡ñ Hashing Amortized Analysis ¡ñ Normal: Require our data structure to be fast on every operation ¡ñ Amortized: Require our data structure to be fast ¡°in total¡± Track runtime using potential function Cost of operation = real work + change in potential function […]
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CS 124 Programming Assignment 2: Spring 2021 Your name(s) (up to two): Collaborators: (You shouldn’t have any collaborators but the up-to-two of you, but tell us if you did.) No. of late days used on previous psets: No. of late days used after including this pset: Homework is due Wednesday 2021-03-24 at 11:59pm ET. You
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CS124 Lecture 12 12.1 Near duplicate documents1 Suppose we are designing a major search engine. We would like to avoid answering user queries with multiple copies of the same page. That is, there may be several pages with exactly the same text. These duplicates occur for a variety of reasons. Some are mirror sites, some
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CS124 Lecture 10 Spring 2016 10.1 The Birthday Paradox How many people do there need to be in a room before with probability greater than 1/2 some two of them have the same birthday? (Assume birthdays are distributed uniformly at random.) Surprisingly, only 23. This is easily determined as follows: the probability the first two
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\documentclass[11pt]{article} \usepackage{fullpage} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsmath} \usepackage{xcolor} \usepackage{algorithm} \usepackage{algorithmic} \newcommand{\F}{\mathbb{F}} \newcommand{\np}{\mathop{\rm NP}} %\newcommand{\binom}[2]{{#1 \choose #2}} \newcommand{\Z}{{\mathbb Z}} \newcommand{\vol}{\mathop{\rm Vol}} \newcommand{\conp}{\mathop{\rm co-NP}} \newcommand{\atisp}{\mathop{\rm ATISP}} \renewcommand{\vec}[1]{{\mathbf #1}} \newcommand{\cupdot}{\mathbin{\mathaccent\cdot\cup}} \newcommand{\mmod}[1]{\ (\mathrm{mod}\ #1)} \setlength{\parskip}{\medskipamount} \setlength{\parindent}{0in} %\input{dansmacs} \begin{document} \section*{CS 124 Homework 1: Spring 2021} \textbf{Your name:} \textbf{Collaborators:} \textbf{No. of late days used on previous psets: }\\ \textbf{No. of late days used
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Shortest paths and Minimum Spanning Tree CS 124 Section 2 Zuzanna Skoczylas •We use a queue (first-in first-out) to keep track of which vertices have already been visited. Unlike Depth-first search, we visit all of our closest neighbors before moving on to further ones. •Can be used to find the shortest path when all the
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CS 124 Homework 1: Spring 2021 Your name: Collaborators: No. of late days used on previous psets: No. of late days used after including this pset: Homework is due Wednesday at midnight ET. You are allowed up to twelve (college)/forty (ex- tension school) late days through the semester, but the number of late days you
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Internet Mathematics Vol. 1, No. 4: 485-509 Network Applications of Bloom Filters: A Survey Andrei Broder and Michael Mitzenmacher Abstract. A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the
\documentclass[11pt]{article} \usepackage{fullpage} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsmath} \usepackage{xcolor} \usepackage{algorithm} \usepackage{algorithmic} \newcommand{\F}{\mathbb{F}} \newcommand{\np}{\mathop{\rm NP}} %\newcommand{\binom}[2]{{#1 \choose #2}} \newcommand{\Z}{{\mathbb Z}} \newcommand{\vol}{\mathop{\rm Vol}} \newcommand{\conp}{\mathop{\rm co-NP}} \newcommand{\atisp}{\mathop{\rm ATISP}} \renewcommand{\vec}[1]{{\mathbf #1}} \newcommand{\cupdot}{\mathbin{\mathaccent\cdot\cup}} \newcommand{\mmod}[1]{\ (\mathrm{mod}\ #1)} \def\O{\mathop{\smash{O}}\nolimits} \def\o{\mathop{\smash{o}}\nolimits} \newcommand{\e}{{\rm e}} \newcommand{\R}{{\bf R}} \renewcommand{\Z}{{\bf Z}} \setlength{\parskip}{\medskipamount} \setlength{\parindent}{0in} %\input{dansmacs} \begin{document} \section*{CS 124 Homework 2: Spring 2021} \textbf{Your name:} \textbf{Collaborators:} \textbf{No. of late days used on
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CS 124 Lecture 11 11.1 Applications: Fingerprinting for pattern matching Suppose we are trying to find a pattern string P in a long document D. How can we do it quickly and efficiently? Hash the pattern P into say a 16 bit value. Now, run through the file, hashing each set of |P| consecutive characters
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