Scaling Up Coordinate Descent Algorithms for Large $\ell_1$ Regularization Problems

We present a generic framework for parallel coordinate descent (CD) algorithms that includes, as special cases, the original sequential algorithms Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm. We introduce two novel parallel algorithms that are also special cases--Thread-Greedy CD and Coloring-Based CD--and give performance measurements for an OpenMP implementation of these.