|Scribing or Grading||5%|
Lectures: 3 sessions / week, 1 hour / session
The need for efficient algorithms arises in nearly every area of computer science. But the type of problem to be solved, the notion of what algorithms are "efficient,'' and even the model of computation can vary widely from area to area. In this second class in algorithms, we will survey many of the techniques that apply broadly in the design of efficient algorithms, and study their application in a wide range of application domains and computational models.
The goal is for the class to be broad rather than deep. Our plan is to touch upon the following areas. This is a tentative list of topics that might be covered in the class; we will select material adaptively based on the background, interests, and rate of progress of the students.
More Advanced Solutions to Basic Data Structuring Problems: Fibonacci Heaps. Van Emde Boas Priority Queues. Dynamic Data Structures for Graph Connectivity/Reachability.
Word-level Parallelism. Transdichotomous Model. o(n \log n) Integer Sorting.
Rabin-Karp Fingerprinting Algorithm. Suffix Trees.
Augmenting Paths and Push-Relabel Methods. Minimum Cost Flows. Bipartite Matching.
Formulation of Problems as Linear Programs. Duality. Simplex, Interior Point, and Ellipsoid Algorithms.
Ski Rental. River Search Problem. Paging. The k-Server Problem. List Ordering and Move-to-Front.
One Way of Coping with NP-Hardness. Greedy Approximation Algorithms. Dynamic Programming and Weakly Polynomial-Time Algorithms. Linear Programming Relaxations. Randomized Rounding. Vertex Cover, Wiring, and TSP.
Another Way of Coping with NP-Hardness. Parameterized Complexity. Kernelization. Vertex Cover. Connections to Approximation.
PRAM. Pointer Jumping and Parallel Prefix. Tree Contraction. Divide and Conquer. Randomized Symmetry Breaking. Maximal Independent Set.
Accounting for the Cost of Accessing Data from Slow Memory. Sorting. B-trees. Buffer Trees. Cache-oblivious Algorithms for Matrix Multiplication and Binary Search.
Convex Hull. Line-segment Intersection. Sweep Lines. Voronoi Diagrams. Range Trees. Seidel's Low-dimensional LP Algorithm.
Sketching. Distinct and Frequent Elements.
Our hope is that this class will confer:
There are no textbooks covering a majority portion of the material we will be studying in this class. Roughly 20% of the material is covered in the lecture notes from another version of this course, by Prof. Michel Goemans. For a list of other readings, see the readings section.
Students are expected to help scribe a lecture in LaTeX and/or help grade a problem set. This work amounts to 5% of the grade for this course.
There will be weekly problem sets worth a total of 70% of the grade, with collaboration usually allowed.
There will be no exams in this course.
You will read a new (not yet textbook) algorithm from the recent research literature, and improve upon it via some mixture of the following:
Ideally, the algorithm you choose will be motivated by a computational problem you need to solve. If the project is large, it may be tackled by a group. This project is worth 25% of the grade in the course.
Collaboration is encouraged on all aspects of the class, except where explicitly forbidden. Note:
|Scribing or Grading||5%|