A Distributed Algorithm for Lasso Variable Selection

Lasso is a variable selection method commonly used in machine learning, which is suitable for regression problems with sparsity. Distributed computing is an important way to reduce computing time and improve efficiency when large sample sizes or massive amounts of data are stored on different agents. Based on the equivalent optimization model of Lasso model and the idea of alternating stepwise iteration, this paper constructs a distributed algorithm suitable for Lasso variable selection. And the convergence of the algorithm is also proved. Finally, the distributed algorithm constructed in this paper is compared with cyclic-coordinate descent and ADMM algorithm through numerical experiments. For the sparse regression problem with large sample set, the algorithm proposed in this paper has better advantages in computing time and precision.