随机算法 (Fall 2011)

Lecture Notes

1. Introduction
   • Complexity Classes
2. Probability Basics
   • Probability Space
   • Verifying Matrix Multiplication
   • Conditional Probability
   • Randomized Min-Cut
   • Random Variables and Expectations
   • Randomized Quicksort
3. Balls and Bins
   • Distributions of Coin Flipping
   • Birthday Problem
   • Coupon Collector
   • Balls-into-balls Occupancy Problem
   • Bloom Filter
   • Stable Marriage
4. Moment and Deviation
   • Markov's Inequality
   • Chebyshev's Inequality
   • Median Selection
   • Random Graphs
5. Hashing and Fingerprinting
   • Pair-wise Independence
   • Derandomization: Two-Point Sampling
6. Chernoff Bound
   • Chernoff Bound
   • Parameter Estimation
   • Set Balancing
   • Routing in a Parallel Network
7. Concentration of Measure
   • Martingales
   • Azuma's Inequality
   • The Method of Bounded Differences
8. Dimension Reduction
   • Johnson-Lindenstrauss Theorem
   • Locality Sensitive Hashing
9. The Probabilistic Method
   • The Probabilistic Method
   • Lovász Local Lemma
   • Derandomization: Conditional Expectation
10. Approximation Algorithms
    • Randomized Rounding
11. Markov Chain and Random Walk
    • Markov Chains
    • Random Walks on Undirected Graphs
12. Random Walk Algorithms
13. Coupling and Mixing Time
14. Expander Graphs
    • Expander Graphs
    • Graph Spectrum
    • Random Walks on Expander Graphs
15. Sampling and Counting
16. MCMC
17. On-line Algorithms
18. Complexity

1. Rapid mixing random walks