Distributed Dynamic Safe Screening Algorithms for Sparse Regularization

TL;DR

This paper introduces the first distributed dynamic safe screening method for sparse regularization models, significantly speeding up large-scale high-dimensional optimization without sacrificing accuracy.

Contribution

It proposes a novel distributed safe screening algorithm applicable to shared-memory and distributed-memory systems, achieving linear convergence and near-complete feature elimination.

Findings

Achieves significant speedup in large-scale sparse model training.

Proves linear convergence rate and finite-time feature elimination.

Outperforms existing methods on benchmark datasets.

Abstract

Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional features widely exist in the era of big data. Safe screening is a popular technique to speed up high-dimensional models by discarding the inactive features with zero coefficients. Nevertheless, existing safe screening methods are limited to the sequential setting. In this paper, we propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively, which can achieve significant speedup without any loss of accuracy by simultaneously enjoying the sparsity of the model and dataset. To the best of our knowledge, this is the first work of distributed safe dynamic screening method. Theoretically, we prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely. Finally, extensive experimental results on benchmark datasets confirm the superiority of our proposed method.