Skip to content

README.md

Latest commit

 

History

History
5 lines (4 loc) · 587 Bytes

README.md

File metadata and controls

5 lines (4 loc) · 587 Bytes
Raw

HPC_for_numerical_methods

Code and final report for the 2 projects part of the class HPC for numerical methods and data analysis taught by Laura Grigori at EPFL.

  • The first project was individual. It compares 3 different algorithms (Classical Gram-Schmidt, Cholesky-QR and TSQR) which compute the thin QR factorization of a matrix.
  • The second project was implemented in a group of 2 students. It investigates the numerical stability and parallel performance of the randomized Nyström algorithm used to compute a rank-k approximation of a square positive semi-definite matrix.