42 citations · 98 across the 14 of their papers we have counts for
30 papers
A Unified Convergence Rate Analysis of The Accelerated Smoothed Gap Reduction Algorithm
Quoc Tran-Dinh
In this paper, we develop a unified convergence analysis framework for the Accelerated Smoothed GAp ReDuction algorithm (ASGARD) introduced in [20, Tran-Dinh et al, 2015] Unlike[20…
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan +1
We develop two new algorithms, called, FedDR and asyncFedDR, for solving a fundamental nonconvex composite optimization problem in federated learning. Our algorithms rely on a nove…
Convergence Analysis of Homotopy-SGD for non-convex optimization
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh +2
First-order stochastic methods for solving large-scale non-convex optimization problems are widely used in many big-data applications, e.g. training deep neural networks as well as…
SMG: A Shuffling Gradient-Based Method with Momentum
Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with mo…
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +3
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations an…
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
Deyi Liu, Lam M. Nguyen, Quoc Tran-Dinh
In this note we propose a new variant of the hybrid variance-reduced proximal gradient method in [7] to solve a common stochastic composite nonconvex optimization problem under sta…