Optimization for Large-Scale Machine Learning with Distributed Features and Observations
This work addresses the challenge of scaling optimization for machine learning in distributed environments, offering a solution for practitioners dealing with massive datasets, though it is incremental as it builds on existing distributed methods.
The authors tackled the problem of large-scale machine learning where both features and observations are distributed across a cluster, proposing two doubly distributed optimization algorithms that outperform the only existing method, block distributed ADMM, in empirical evaluations.
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention in the literature. Although previous research has mostly focused on settings where either the observations, or features of the problem at hand are stored in distributed fashion, the situation where both are partitioned across the nodes of a computer cluster (doubly distributed) has barely been studied. In this work we propose two doubly distributed optimization algorithms. The first one falls under the umbrella of distributed dual coordinate ascent methods, while the second one belongs to the class of stochastic gradient/coordinate descent hybrid methods. We conduct numerical experiments in Spark using real-world and simulated data sets and study the scaling properties of our methods. Our empirical evaluation of the proposed algorithms demonstrates the out-performance of a block distributed ADMM method, which, to the best of our knowledge is the only other existing doubly distributed optimization algorithm.