Welcome to the Spring 2018 homepage for CMPSCI 711: More Advanced Algorithms.
- Instructor: Andrew McGregor.
- Lectures: Tuesday and Thursday, 8:30 to 9:45 am in CMPS 140.
- 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 (2015) (Chakrabarti)
- MIT (2007) (Indyk)
- Harvard (2017) (Nelson)
- University of Washington (2014) (Beame)
- 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)
- Graph Streaming Algorithms (McGregor)
- Sketching as a Tool for Numerical Linear Algebra (Woodruff)
- Slides:
- Course Overview
- Part 1: Graph Algorithms:
- Graphs-1: Connectivity, k-connectivity, Spanners, Sparsification
- Graphs-2: Connectivity via Sketching
- Graphs-3: Sparsification via Sketching
- Graphs-4: Insert-Only (Weighted) Matchings
- Graphs-5: Planar Matchings
- Graphs-6: Small Matchings
- Graphs-7: Multiple-Pass Matchings via Multiplicative Weights
- Graphs-8: Triangles and Densest Subgraph
- Graphs-9: Submodular Maximization and Covering Problems
- Graphs-10: Correlation Clustering
- 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)
- A (2+\epsilon)-Approximation for Maximum Weight Matching in the Semi-Streaming Model (Paz and Schwartzman)
- Part 2: Vectors:
- Vectors-1: Sampling
- Vectors-2: Intro to Sketches: F0 and F2
- Vectors-3: Multi-Purpose Sketches: Count-Min, Count-Sketch for Heavy Hitters, Range Queries, Quantiles
- Vectors-4: Sampling via Sketches: Lp sampling and Applications
- Vectors-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)
- Clustering and Geometry:
- Section 3-1: Coresets and Clustering
- Section 3-2: Gridbased Sketching
- Associated Papers:
- 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)