Andrew McGregor

Welcome to the Fall 2010 homepage for CMPSCI 611 - Advanced Algorithms

• Instructor:
  • Andrew McGregor. (mcgregor at cs.umass.edu)
  • Office hours: 10:30 am to 11:30 am on Tuesday in CMPS 334 or by appointment.
  • Bonus Revision Office hours: 3:00 pm to 4:00 pm on Friday 10th December in CMPS 334.
  • Lectures: Tuesday and Thursday, 2:30 to 3:45 pm in CMPS 142.
  
  
• TA:
  • Brandon McPhail (mcphailb at cs.umass.edu)
  • Office hours: 1.15 pm to 2.15 pm on Tuesdays and Thursdays in LGRT 220. Or by appointment.
  
  
• Textbook: The required textbook will be "Lecture Notes from CMPSCI 611" by Prof. Micah Adler and this will be available from the Textbook Annex. See here for a list corrections and typos. Other books you might find generally useful include:
  • Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (Library has the e-book)
  • Algorithm Design by Kleinberg and Tardos
  • Algorithms by Dasgupta, Papadimitriou, Vazirani (See here for penultimate draft.)
  You may also find sections of the following books helpful:
  • Randomized Algorithms by Motwani and Raghavan (Library has the e-book)
  • Probability and Computing by Mitzenmacher and Upfal (Library has the e-book)
  • Approximation Algorithms by Vazirani (Library has the e-book if you can read French)
  
  
• Homeworks and Exams:
  • Homework 1: Posted 9/16, Due 9/28
  • Homework 2: Posted 9/30, Due 10/7
  • Homework 3: Posted 10/21, Due 11/2
  • Homework 4: Posted 11/10, Due 11/23
  • Homework 5: Posted 11/23, Due 12/7
  • Practice Midterms: 09 Midterm, 09 Solutions, 05 Material, 06 Material
  • Midterm and Solutions
  • Practice Finals: 09 Final and Rough Solutions, 05 Practice Final and Solutions
  
  Homeworks are due at the start of class, either by email or as a hard copy. Late policy is that you're allowed to hand in at most one of the homework 48 hours late. You don't even need a good excuse! However, if later in the semester you don't hand in a homework on time, even if you have the best excuse in the world, then you'll get no credit for the second homework. Therefore, since you can't tell what might happen in the future, I'd suggest that you don't use your "lifeline" prematurely.
  
  
• Schedule and Slides: Here's an approximate schedule for the course. Note that this'll be updated as we go along depending on our progress and, hopefully, we'll get to squeeze in a couple of extra topcs. I'll add slides after each class (some links will be dead until the slides are added).
  
  • Lectures 1-3: Preliminaries, Divide and Conquer
    • 7 Sept: Preliminaries, Mergesort, Master Theorem (Slides)
    • 9 Sept: Strassen's Matrix Multiplication Algorithm, Closest Pairs (Slides)
    • 14 Sept: The Fast Fourier Transform (Slides)
  • Lectures 4-7: Greedy Algorithms and Matroids
    • 16 Sept: Minimum Spanning Trees, Subset Systems (Slides)
    • 21 Sept: Matroids (Slides)
    • 23 Sept: Bipartite Matchings (Slides)
    • 28 Sept: The Union-Find Problem, Kruskal's Algorithm (Slides, Visualization)
  • Lectures 8-11: Dynamic Programming, Shortest Paths, and Network Flow
    • 30 Oct: The Knapsack Problem, Dynamic Programming, Floyd-Warshall (Slides)
    • 5 Oct: The Dijkstra and Seidel Algorithms (Slides, Documentary)
    • 7 Oct: More Seidel, Network Flow (Slides)
    • 12 Oct: No class (Monday Schedule)
    • 14 Oct: Network Flow, Ford-Fulkerson Algorithm (Slides)
  • Midterm:
    • 19 Oct: In class midterm on the first eleven lectures.
  • Lectures 12-16: Randomized Algorithms
    • 21 Oct: Quicksort, Karger's Algorithm (Slides)
    • 26 Oct: No class (I'm away at a conference)
    • 28 Oct: Karger's Algorithm and Polynomial Identities (Slides)
    • 2 Nov: Tail Inequalities and Lazy Select (Slides)
    • 4 Nov: Chernoff Bounds and Balls and Bins (Slides)
    • 9 Nov: Hash Functions and Count-Min Sketch (Slides)
  • Lectures 17-21: Approximation Algorithms and NP-Completeness
    • 10 Nov: NP-Completeness (Slides, A Vote on P=NP))
    • 11 Nov: No class (Veterans' Day)
    • 16 Nov: Subset Sum, Max-Cut (Slides)
    • 18 Nov: P versus NP, Approximations, Independent Set Problem (Slides)
    • 23 Nov: More Approximations, Metric TSP Problem (Slides)
    • 25 Nov: No class (Thanksgiving)
    • 30 Nov: Set Cover Problem, Poly-Time Approximation Schemes (Slides)
    • 2 Dec: Linear Programming, Simplex Method (Slides)
  • Lectures 22-23: Linear Programming
    • 7 Dec: Analysis of the Simplex Method (Slides)
  • Lectures 24: Review and Any Questions
    • 9 Dec: Review (Slides)
  • Final:
    • 13 Dec (1:30pm to 3:30pm): Will cover all the material.