Scaling Up Coordinate Descent Algorithms for Large ℓ_1 Regularization Problems

06/27/2012
by   Chad Scherrer, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/17/2012

Feature Clustering for Accelerating Parallel Coordinate Descent

Large-scale L1-regularized loss minimization problems arise in high-dime...
03/24/2022

A more flexible counterpart of a Huang-Kotz's copula-type

We propose a more flexible symmetric counterpart of the Huang-Kotz's cop...
09/28/2020

Gradient based block coordinate descent algorithms for joint approximate diagonalization of matrices

In this paper, we propose a gradient based block coordinate descent (BCD...
12/03/2019

On Extensions of Limited Memory Steepest Descent Method

We present some extensions to the limited memory steepest descent method...
11/18/2019

SySCD: A System-Aware Parallel Coordinate Descent Algorithm

In this paper we propose a novel parallel stochastic coordinate descent ...
10/07/2013

Parallel coordinate descent for the Adaboost problem

We design a randomised parallel version of Adaboost based on previous st...
11/20/2017

Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks

We present a stochastic first-order optimization algorithm, named BCSC, ...