The Power of Randomization - Distributed Submodular Maximization on Massive Datasets

The Power of Randomization - Distributed Submodular Maximization on Massive Datasets

Hausdorff Center for Mathematics via YouTube Direct link

Analysis (Preliminaries)

13 of 17

13 of 17

Analysis (Preliminaries)

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The Power of Randomization - Distributed Submodular Maximization on Massive Datasets

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  1. 1 Distributed Submodular Maximization in Massive Datasets
  2. 2 Combinatorial Optimization
  3. 3 Submodularity
  4. 4 Example: Multimode Sensor Coverage
  5. 5 Example: Identifying Representative
  6. 6 Need for Parallelization
  7. 7 Problem Definition
  8. 8 Greedy Algorithm
  9. 9 Performance of Distributed Greedy
  10. 10 Revisiting the Analysis
  11. 11 Power of Randomness
  12. 12 Intuition
  13. 13 Analysis (Preliminaries)
  14. 14 Analysis (Sketch)
  15. 15 Generality
  16. 16 Non-monotone Functions
  17. 17 Future Directions

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