arXiv research

LightOn OPUs accelerate randomized numerical linear algebra, reducing computational costs.

RandNLA uses randomness for matrix problems in machine learning.

Two algorithms improve fitting autoregressive models for big data.

Develops asymptotic analysis for RandNLA sampling estimators in least-squares problems.

SALSA efficiently approximates leverage scores for big data, improving ARMA model fitting.

LSAR efficiently estimates AR models for big time series data.

Corrects bias in random sampling matrices for improved ML methods.

Many data-fitting applications require the solution of an optimization problem involving a sum of large number of functions of high dimensional parameter. Here, we consider the problem of minimizing a sum of $n$ functions over a convex constraint set $\mathcal{X} \subseteq \mathbb{R}^{p}$ where both $n$ and $p$ are lar…