CS711 (S12) | Andrew McGregor (original) (raw)

Welcome to the Spring 2012 homepage for CMPSCI 711: More Advanced Algorithms.

• Instructor:
  • Andrew McGregor.
  • Office hours: By appointment.
• Lectures:
  • Monday, Wednesday, 2:05 to 3:20 pm in LGRC A310.
• Textbooks:
  • There is no official textbook for the class and all required material will be distributed in class.
  • Background on Randomized Algorithms:
    * Randomized Algorithms by Motwani and Raghavan
    * Probability and Computing by Mitzenmacher and Upfal
    * Concentration of Measure for the Analysis of Randomised Algorithms (Dubhashi, Panconesi)
  • Related Courses:
    * Dartmouth (Chakrabarti)
    * MIT (Indyk)
  • Useful Surveys:
    * Sketch Techniques for Apporximate Query Processing (Cormode)
    * Sparse Recovery Using Sparse Matrices (Gilbert, Indyk)
    * Data Streams: Algorithms and Applications (Muthukrishnan)
    * Chapter on Communication Complexity (Arora, Barak)
• Homeworks:
  • Homework 1: Due 2/22.
  • Homework 2: Due 3/14.
  • Homework 3: Due 4/16.
  • Homework 4: Due 4/30.
• Slides:
  • Section 0: Overview
  • Numbers:
    * Section 1-1: Sampling
    * Section 1-2: Intro to Sketches: F0 and F2
    * Section 1-3: Multi-Purpose Sketches: Count-Min, Count-Sketch for Heavy Hitters, Range Queries, Quantiles
    * Section 1-4: Sampling via Sketches: Lp sampling and Applications
    * Section 1-5: Approximate Representations and Signal Reconstruction
    * Associated Papers:
    * An Improved Data Stream Summary: The Count-Min Sketch and its Applications (Cormode, Muthukrishnan)
    * The space complexity of approximating the frequency moments (Alon, Matias, Szegedy)
    * Streaming Algorithms from Precision Sampling (Andoni, Krauthgamer, Onak)
    * Tight Bounds for Lp Samplers, Finding Duplicates in Streams, and Related Problems (Jowhari, Sağlam, Tardos)
  • Graphs:
    * Section 2-1: Connectivity, k-connectivity, Spanners, Sparsification
    * Section 2-2: Graph Sketches
    * Section 2-3: Matchings and Bipartiteness
    * Associated Papers:
    * Analyzing Graph Structure via Linear Measurements (Ahn, Guha, McGregor)
    * On Graph Problems in a Semi-streaming Model (Feigenbaum et al.)
    * Finding Matchings in the Streaming Model (McGregor)
  • Clustering and Geometry:
    * Section 3-1: Coresets and Clustering
    * Section 3-2: Gridbased Sketching
    * Associated Papers:
    * Tight results for clustering and summarizing data streams (Guha)
    * Algorithms for Dynamic Geometric Problems over Data Streams (Indyk)
  • Sequences:
    * Section 4-1: Longest Increasing Sequences and DYCK
    * Section 4-2: Memory Checking
    * Associated Papers:
    * Recognizing well-parenthesized expressions in the streaming model (Magniez, Mathieu, Nayak)
    * Information Cost Tradeoffs for Augmented Index and Streaming Language Recognition (Chakrabarti, Cormode, Kondapally, McGregor)
    * Estimating the Sortedness of a Data Stream (Gopalan, Jayram, Krauthgamer, Kumar)
  • Lower Bounds:
    * Section 5-1: Basic Connections, Examples, Hamming Distance
    * Section 5-2: Information Statistics and Disjointness
    * Associated Papers:
    * An information statistics approach to data stream and communication complexity (Bar-Yossef, Jayram, Kumar, Sivakumar)
    * The One-Way Communication Complexity of Hamming Distance (Jayram, Kumar, Sivakumar)
  • Special Topics:
    * Section 6-1: Sliding Windows
    * Section 6-2: Distributed Streams and Functional Monitoring
    * Section 6-3: Stochastic Streams
    * Associated Papers:
    * Selection and Sorting with Limited Storage (Munro and Paterson)
    * Smooth Histograms for Sliding Windows (Braverman and Ostrovsky)
    * Optimal Random Sampling from Distributed Streams Revisited (Tirthapura and Woodruff)
    * Algorithms for Distributed Functional Monitoring (Cormode, Muthukrishnan, Yi)