Implementations of distributed algorithms using PySpark, including graph computaion, matrix computation, randomized algorithm, optimization and machine learning. In particular, We referenced the meterial presented in 《CME 323: Distributed Algorithms and Optimization》.
- Graph Computation
- PageRank [explanation]
- Transitive Closure
- Machine Learning
- K-means
- Logistic Regression [explanation]
- Matrix Computation
- Matrix Decomposition
- Optimization
- Synchronous Stochastic Gradient Descent (SSGD) [explanation] [paper]
- Model Average (MA) [explanation] [paper]
- Block-wise Model Update Filtering (BMUF) [explanation] [paper]
- Elastic Averaging Stochastic Gradient Descent (EASGD) [explanation] [paper]
- Randomized Algorithm
- Monte Carlo Method