4 accepted papers
Distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter is updated using stale gradients, which are gradients calculated with respect to out…
We present convergence analysis of Anytime Minibatch with Delayed Gradients (AMB-DG) algorithm. In AMB-DG, workers compute gradients in epochs of fixed duration while the master uses stale gradients to update the optimization parameters. We analyze AMB-DG in terms of its regret bound and convergence…
Gradient-based algorithms play an important role in solving a wide range of stochastic optimization problems. In recent years, implementing such schemes in parallel has become the new paradigm. In this work, we focus on the asynchronous implementation of gradient-based algorithms. In asynchronous di…
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes complete their assigned tasks in each computational epoch before the system can proceed to the next epoch. In such sett…