Welcome to the Fall 2019 homepage for CMPSCI 611 - Advanced Algorithms.
- Instructor:
- Andrew McGregor.
- Office hours: 3-4pm Tuesday in CS 334.
- TA:
- Raghav Addanki and David Tench.
- Office hours: 4-5pm Monday in CS314 (Raghav), 2-3pm Thursday in CS207 (David)
- Textbook:
The required textbook will be
- Lecture Notes from CMPSCI 611 by Prof. Micah Adler and this will be available (probably by the end of the first week of class) from Collective Copies. See here for a list corrections and typos.
- Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein.
- Algorithm Design by Kleinberg and Tardos.
- Algorithms by Dasgupta, Papadimitriou, Vazirani.
- Randomized Algorithms by Motwani and Raghavan.
- Probability and Computing by Mitzenmacher and Upfal.
- Approximation Algorithms by Vazirani.
- Homeworks and Exams:
- Homework 1: Due 9/20
- Homework 2: Due 10/11
- Homework 3: Due 11/4
- Homework 4: Due 11/18
- Homework 5: Due 12/6
- Exam Times:
- Midterm 1: Wednesday, 10/16 from 7:00-9:00 pm in ELABII 119.
- Midterm 2: Thursday, 11/21 from 7:00-9:00 pm in ILC S331.
- Final: Friday, 12/13 from 1:00-3:00 in Goessmann Lab 64.
- 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.
- Late Policy: Homeworks are due at 8pm in Gradescope.
- 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 3 Sept Preliminaries, Mergesort, Master Theorem Slides, Section 1, 2.1, 2.2 5 Sept Matrix Multiplication, Closest Pairs Slides, Section 2.3, 2.4. A blog post describing recent progress on matrix multiplication. 10 Sept Fast Fourier Transform Slides, Section 2.5 12 Sept Minimum Spanning Trees Slides, Section 3.1 17 Sept Subset Systems, Matroids Slides, Section 3.2, 3.3 19 Sept No class (Instructor at workshop) 21 Sept Cardinality Theorem and Examples Slides, Section 3.4 26 Sept Bipartite Matchings, The Union-Find Problem Slides, Section 3.4, 3.5 1 Oct Dynamic Programming, e.g., Knapsack Problem and Floyd-Warshall Slides, Section 4.1-4.4 3 Oct Dijkstra Slides, Section 4.5 8 Oct Seidel Slides, Section 4.6 10 Oct Network Flow Part 1 Slides, Section 5.1-5.2 15 Oct No Class (Monday Schedule) 17 Oct Network Flow Part 2 Slides, Section 5.3-5.4 22 Oct Quicksort Slides, Section 6.1 24 Oct Karger's Algorithm Slides, Section 6.2, 6.3 29 Oct Tail Inequalities and Lazy Select Slides, Section 6.5 31 Oct Chernoff Bounds and Balls and Bins Slides, Section 6.5, Video showing concentration of binomial distribution 5 Nov More Balls and Bins, Polynomial Multiplication Slides, 7 Nov Data Streams and Count-Min Sketch Slides, Original Paper 12 Nov Approximation Algorithms Slides, Section 7.1-8.2.1. 14 Nov P versus NP, Approximations, Independent Set Problem Slides, Section 8.2.2-8.2.4. 19 Nov NP Completeness Slides, Section 8.2.4-8.4 21 Nov More NP Completeness and Approximation Algorithms Slides, 26 Nov No class (Thanksgiving) 28 Nov No class (Thanksgiving) 3 Dec Linear Programming, Simplex Method Slides, 5 Dec Analysis of the Simplex Method Slides, 10 Dec Review and Any Questions Slides,