Modern matrix methods for large scale data and networks 2012

Minisymposium at SIAM Applied Linear Algebra

Organized by David F. Gleich

Every few years, the new applications for matrix methods arise and challenge existing paradigms. The talks in this mini-symposium sample some of the research that has arisen out of new applications in large scale machine learning, network problems, and data analysis. Much of the research presented at this mini-symposium will have an interesting twist on a classical matrix problem – linear systems, least squares, or eigenvalues – that better fits the current problems.

2012-07-04 Slides posted! Click the talk titles below.


Nonlinear Eigenproblems in Data Analysis and Graph Partitioning

Matthias Hein, Saarland University

LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems

Xiangrui Meng, Stanford University
with Michael Saunders (Stanford), and Michael Mahoney (Stanford)

Solving Large Dense Linear Systems with Covariance Matrices

Jie Chen, Argonne National Labs

Fast Coordinate Descent Methods with Variable Selection for Non-negative Matrix Factorization

Inderjit S. Dhillon, UT Austin
with Cho-Jui Hsieh, UT Austin


blog comments powered by Disqus