Andrew McGregor

Welcome to the Fall 2020 homepage for CMPSCI 611 - Advanced Algorithms.

• Instructor:
  • Andrew McGregor.
  
  
• TA:
  • Raghav Addanki and Md Abdul Aowal.
  
  
• Textbook: The required textbook will be
  • Lecture Notes from CMPSCI 611 will be available soon via Moodle.
  Other books you might find generally useful (but aren't required) include:
  • Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein.
  • Algorithm Design by Kleinberg and Tardos.
  • Algorithms by Dasgupta, Papadimitriou, Vazirani.
  You may also find sections of the following books helpful:
  • Randomized Algorithms by Motwani and Raghavan.
  • Probability and Computing by Mitzenmacher and Upfal.
  • Approximation Algorithms by Vazirani.
  
  
• Homeworks and Exams:
  • Homework 1: Due 9/11
  • Homework 2: Due 9/25
  • Homework 3: Due 10/9
  • Homework 4: Due 10/23
  • Homework 5: Due 11/20 (link is dead until homework is posted)
  • Support Material:
    • If you would like to write your homework solutions in LaTeX, here's a template (and here's a compiled version.)
    • Practice Midterms: 2017 Midterm 1 and Solutions, 2017 Midterm 2 and Solutions, 2015 Midterm and Solutions, 2009 Midterm and Solutions, 2012 Midterm and Solutions, 2010 Midterm and Solutions.
    • Practice Finals: 2017 Final Exam and Solutions, 2015 Final Exam and Solutions, 2012 Final and Rough Solutions, 2010 Final and Rough Solutions, 2009 Final and Rough Solutions, 2005 Practice Final and Solutions.
  
  
  
  
• Honesty and Collaboration Policy: Violating any of the following rules risks an automatic F. Ask if you're unsure about any of the policies.
  • Homework: Collaborating with at most three other students in the homework is allowed and you should mention who you worked with. You're not allowed to use material from the web (or indeed any material except from that listed on the course page) or talk about the homework with anybody outside your collaboration group (aside from the lecturer or TA.)
  • Quizzes: No collaboration! But you can consult any material you like.
  • Exams: Closed book and no collaboration.
  
  
• 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).
  
  

  Date	Topic	Reading and Background
  25 Aug	Preliminaries, Mergesort, Master Theorem	Slides, Section 1, 2.1, 2.2
  27 Aug	Matrix Multiplication, Closest Pairs	Slides, Section 2.3, 2.4. A blog post describing recent progress on matrix multiplication.
  1 Sept	Fast Fourier Transform	Slides, Section 2.5
  3 Sept	Minimum Spanning Trees	Slides, Section 3.1
  8 Sept	Subset Systems, Matroids	Slides, Section 3.2, 3.3
  10 Sept	Cardinality Theorem and Examples	Slides, Section 3.4
  15 Sept	Bipartite Matchings, The Union-Find Problem	Slides, Section 3.4, 3.5
  17 Sept	Dynamic Programming, e.g., Knapsack Problem and Floyd-Warshall	Slides, Section 4.1-4.4
  22 Sept	Dijkstra	Slides, Section 4.5
  24 Sept	Seidel	Slides, Section 4.6
  29 Sept	Network Flow Part 1	Slides, Section 5.1-5.2
  1 Oct	Network Flow Part 2	Slides, Section 5.3-5.4
  6 Oct	No Class
  8 Oct	Quicksort	Slides, Section 6.1
  13 Oct	Karger's Algorithm	Slides, Section 6.2, 6.3
  15 Oct	Tail Inequalities and Lazy Select	Slides, Section 6.5
  20 Oct	Chernoff Bounds and Balls and Bins	Slides, Section 6.5, Video showing concentration of binomial distribution
  22 Oct	More Balls and Bins, Polynomial Multiplication	Slides
  27 Oct	Approximation Algorithms	Slides
  29 Oct	More Approximation Algorithms: TSP and Weighted Set Cover	Slides, Section 7.1-8.2.1.
  3 Nov	P versus NP, Approximations, Independent Set Problem	Slides, Section 8.2.2-8.2.4.
  5 Nov	NP Completeness	Slides, Section 8.2.4-8.4
  10 Nov	More NP Completeness and Approximation Algorithms	Slides,
  12 Nov	Linear Programming, Simplex Method	Slides,
  17 Nov	Analysis of the Simplex Method	Slides,
  19 Nov	Review	Slides,