PyRLA: Randomized Linear Algebra in Python

This package implements the most common randomized matrix computation algorithms.

Demos

1. Prepare: download and process data

Download the "Year Prediction Million Song Dataset"

• go to the directory "data/"
• Linux: bash LinuxDownloadData.sh
• Mac: bash MacDownloadData.sh

Convert the data to NumPy data file

• python processLibSVMData.py

Wait a while. The output file is "YearPredictionMSD.npy"

2. Run demos.

Here are some examples.

• Matrix sketching

  • "sketch/demo/demo_rft.py": matrix coherence after the randomized Fourier transform (RFT) gets much smaller.
  • "sketch/demo/demo_sketch.py": apply SRFT, count sketch, and leverage score sampling to matrix multiplication and compare their errors.

• Optimization

  • "optimization/demo/demo_precondition_cg": the converge of CG with/without preconditioning.