The randomized coordinate descent (RCD) method is a simple but powerful approach to solving inconsistent linear systems. In order accelerate this approach, the Nesterov accelerated (NARCD) proposed. with momentum (RCDm) proposed by Nicolas Loizou, we will provide new convergence boundary. global rates of two methods are established in our paper. addition, show that RCDm has an rate choosing pro...

Coordinate descent (CD) algorithms have become the method of choice for solving a number of optimization problems in machine learning. They are particularly popular for training linear models, including linear support vector machine classification, LASSO regression, and logistic regression. We consider general CD with non-uniform selection of coordinates. Instead of fixing selection frequencies...

Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stability, as well as its competitive performance on l1 regularized smooth optimization problems. Surprisingly, very little is known about its finite time convergence behavior on these problems. Most existing results eithe...