CS 591-06 "Algorithms in the Real World" Fall 2002

Instructor:

• Jared Saia
• Office: FEC 337, phone: 277-3149
• Office Hours: Tuesdays and Thursdays 3:15-4:15, Weds 2-3pm (or by appointment)

Meeting Times:

• Tuesdays and Thursdays 2:00-3:15pm in Mechanical Engineering, Room #208

cs591-06 E-mail List:

• Directions for subscribing to the class email list are here. The above link also contains an archive of the mailing list.

Assignments:

• Homework 1
• Homework 2, The solution to the last problem is given in the manuscript "Tossing a Biased Coin" by Michael Mitzenmacher which is here.

Reading:

Slides from Lectures:

• Lecture 1
• Lecture 2
• Lecture 3
• Lecture 4
• Lecture 5
• Lectures 6 and 7
• Lecture 8
• Lecture 9
• Lecture 10
• Lecture 11
• Lecture 12
• Lecture 13
• Lecture 14
• Lecture 15
• Lecture 16
• Lecture 17
• Lecture 18
• Lecture 19
• Lecture 20

Syllabus

In this course, we will study several algorithms which have been successful in the real world. We will learn some new algorithmic tools that have proven successful for real world problems including: ways to create approximation algorithms for NP-Hard problems, ways to exploit the power of randomness, and ways to create tractable abstract problems from messy real-world problems. We will also study some important open algorithmic problems whose solutions would have a big "real-world" impact.
Texts: There is no required text but the following texts are recommended for reference: "Approximation Algorithms for NP-Hard Problems" Edited by Dorit S. Hochbaum and "Randomized Algorithms" by Raghavan and Motwani.

Topics

• Advanced graph theory
  • Hall's Theorem and Data Migration.
    "On Algorithms for Efficient Data Migration" by Joe Hall, Jason Hartline, Anna Karlin, Jared Saia and John Wilkes. Symposium on Discrete Algorithms 2001. ( ps , pdf).
  • "An Experimental Study of Data Migration Algorithms" by Eric Anderson, Joe Hall, Jason Hartline, Michael Hobbes, Anna Karlin, Jared Saia, Ram Swaminathan and John Wilkes. Workshop on Algorithm Engineering 2001. ( ps , pdf).
  • Expander graphs and Attack-Resistant P2P Networks.
    "Censorship Resistant Peer-To-Peer Content Addressable Networks" by Amos Fiat and Jared Saia. Journal of Algorithms 2002 ( ps , pdf).
  • Szemeredi's Regularity Lemma ???
• Randomized Algorithms
  • Random Construction of Expander Graphs
    "Censorship Resistant Peer-To-Peer Content Addressable Networks" by Amos Fiat and Jared Saia. Journal of Algorithms 2002 ( ps , pdf).
  • Web Caching with Consistent Hashing (the Akamai problem)
    "Consistent Hashing and Random Trees: Tools for Relieving Hot Spots on the World Wide Web" by David Karger, Eric Lehman, Tom Leighton, Matthew Levine, Daniel Lewin and Rina Panigrahy. STOC 1997. ( ps ). See also the simpler paper in WWW8 located here
  • Pattern Matching and Fingerprinting ???
    "Some applications of Rabin's fingerprinting method" by Andrei Z. Broder in Renato Capocelli, Alfredo De Santis, and Ugo Vaccaro, editors, Sequences II: Methods in Communications, Security, and Computer Science, pages 143--152. Springer-Verlag (.ps)
• Spectral Analysis and Search Engines
  • Clever Paper: "Authoritative sources in a hyperlinked environment" by Jon Kleinberg in Symposium on Discrete Algorithms, 1998 (.ps)
  • Google Paper: "The Anatomy of a Large-Scale Hypertextual Web Search Engine" by Sergey Brin and Lawrence Page in www7 ( html )
  • "Spectral Analysis of Data" by Yossi Azar, Amos Fiat, Anna Karlin, Frank McSherry and Jared Saia. Symposium on Theory of Computing 2001. ( ps , pdf).
• Online Algorithms
  • Randomized Caching Algorithms
    Pages 374-377 in the "Randomized Algorithms" textbook
  • Online Learning Algorithms
    Section 3.2 (The "Winnow Algorithm") of the survey paper "Online Algorithms in Machine Learning" by Avrim Blum. (.ps)
    See also "Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain" by Avrim Blum in 12th International Conference on Machine Learning (.ps)
  • Online Auctions ???
• Miscellanious Algorithmic Tricks
  • The String Alignment Algorithm for Computational Biology

Text Books

• Randomized Algorithms by Rajeev Motwani and Prabhakar Raghavan
• Approximation Algorithms for NP-Hard Problems Edited by Dorit S. Hochbaum

Tentative Grading Weights

• Homeworks (60%)
• Project (40%)

Grading Methodology

Homeworks

• Clarity: Make sure all of your work and answers are clearly legible and well separated from other problems. If we can't read it, then we can't grade it. Likewise, if we can't immediately find all of the relevant work for a problem, then we will be more likely to grade only what we see at first.
• Completeness: Full credit for all problems is based on both sufficient intermediate work (the lack of which often produces a 'justify' comment) and the final answer. There are many ways of doing most problems, and we need to understand exactly how YOU chose to solve each problem. Here is a good rule of thumb for deciding how much detail is sufficient: if you were to present your solution to the class and everyone understood the steps, then you can assume it is sufficient.
• Succinctness: The work and solutions which you hand-in should be long enough to convey exactly why the answer you get is correct, yet short enough to be easily digestible by someone with a basic knowledge of this material. If you find yourself doing more than half a page of dense algebra, generating more than a dozen numeric values or using more than a page or two of paper per problem for your solution, you're probably doing too much work. Don't turn in pages with scratch work or multiple answers - if you need to do scratch work, do it on separate scratch paper. Clearly indicate your final answer (circle, box, underline, etc.). Note: It's usually best to rewrite your solution to a problem before you hand it in. If you do this, you'll find you can usually make the solution much more succinct.

Project

Prerequisites

Policies