62 citations · 315 across the 65 of their papers we have counts for
75 papers
Distributed Fast Fixed-Point Algorithms for Composite Monotone Inclusions over Networks
Nghia Nguyen-Trung, Ion Necoara, Quoc Tran-Dinh
This paper aims to develop new and efficient distributed algorithms for solving a class of monotone inclusions, , over a connected network of …
Provable Parameter-Free Fixed-Point Algorithms with Linear Convergence Rates
Quoc Tran-Dinh, Pham Ngoc Anh, Ha Manh Tien
In this paper, we develop provable parameter-free fixed-point algorithms for contractive mappings, with an emphasis on automatically exploiting hidden contractivity without requiri…
Adaptive Regularization within Trust Region Methods for Stochastic Nonconvex Optimization
Yunsoo Ha, Sara Shashaani, Quoc Tran-dinh
We propose a stochastic nonconvex optimization algorithm that achieves almost sure iteration complexity for problems with smooth objective functions…
Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
Quoc Tran-Dinh, Nghia Nguyen-Trung
This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly nonmonotone stochastic composite inclu…
A Class of Accelerated Fixed-Point-Based Methods with Delayed Inexact Oracles and Its Applications
Nghia Nguyen-Trung, Quoc Tran-Dinh
In this paper, we develop a novel accelerated fixed-point-based framework using delayed inexact oracles to approximate a fixed point of a nonexpansive operator (or equivalently, a…
New Accelerated Past-Extragradient Methods with Variance Reduction for Generalized Equations
Quoc Tran-Dinh, Nghia Nguyen-Trung
We develop a novel past-extragradient-type algorithmic framework, combining both Nesterov's \textit{acceleration} and \textit{variance-reduction} techniques, to solve a class of ge…
Variance-Reduced Fast Operator Splitting Methods for Generalized Equations
Quoc Tran-Dinh
We develop two variance-reduced fast operator splitting methods to approximate solutions of a class of generalized equations, covering fundamental problems such as \rvs{minimizatio…
Complexity of Linearized Perturbed Augmented Lagrangian Methods for Nonsmooth Nonconvex Optimization with Nonlinear Equality Constraints
Lahcen El Bourkhissi, Ion Necoara, Panagiotis Patrinos +1
This paper addresses a class of general nonsmooth and nonconvex composite optimization problems subject to nonlinear equality constraints. We assume that a part of the objective fu…
Accelerated Extragradient-Type Methods -- Part 2: Generalization and Sublinear Convergence Rates under Co-Hypomonotonicity
Quoc Tran-Dinh, Nghia Nguyen-Trung
Following the first part of our project, this paper comprehensively studies two types of extragradient-based methods: anchored extragradient and Nesterov's accelerated extragradien…
Lagrange Multipliers and Duality with Applications to Constrained Support Vector Machine
Nguyen Mau Nam, Gary Sandine, Quoc Tran-Dinh
In this paper, we employ the concept of quasi-relative interior to analyze the method of Lagrange multipliers and establish strong Lagrangian duality for nonsmooth convex optimizat…
Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
Quoc Tran-Dinh, Trang H. Tran, Lam M. Nguyen
This paper aims at developing novel shuffling gradient-based methods for tackling two classes of minimax problems: nonconvex-linear and nonconvex-strongly concave settings. The fir…
Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates
Quoc Tran-Dinh, Nghia Nguyen-Trung
This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linea…
Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problems
Quoc Tran-Dinh
We propose a new class of fast Krasnoselkii--Mann methods with variance reduction to solve a finite-sum co-coercive equation . Our algorithm is single-loop and leverages a…
Stochastic Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations
Quoc Tran-Dinh
We develop two novel stochastic variance-reduction methods to approximate solutions of a class of nonmonotone [generalized] equations. Our algorithms leverage a new combination of…
Shuffling Momentum Gradient Algorithm for Convex Optimization
Trang H. Tran, Quoc Tran-Dinh, Lam M. Nguyen
The Stochastic Gradient Descent method (SGD) and its stochastic variants have become methods of choice for solving finite-sum optimization problems arising from machine learning an…
Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments
Quoc Tran-Dinh
The extragradient (EG), introduced by G. M. Korpelevich in 1976, is a well-known method to approximate solutions of saddle-point problems and their extensions such as variational i…
Extragradient-Type Methods with Last-Iterate Convergence Rates for Co-Hypomonotone Inclusions
Quoc Tran-Dinh
We develop two "Nesterov's accelerated" variants of the well-known extragradient method to approximate a solution of a co-hypomonotone inclusion constituted by the sum of two opera…
Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems
Quoc Tran-Dinh, Yang Luo
In this paper, we develop two new randomized block-coordinate optimistic gradient algorithms to approximate a solution of nonlinear equations in large-scale settings, which are cal…
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis
Quoc Tran-Dinh, Marten van Dijk
In this book chapter, we briefly describe the main components that constitute the gradient descent method and its accelerated and stochastic variants. We aim at explaining these co…
Data Integration Via Analysis of Subspaces (DIVAS)
Jack B. Prothero, Meilei Jiang, Jan Hannig +3
Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e. platforms). We call this data mu…
From Halpern's Fixed-Point Iterations to Nesterov's Accelerated Interpretations for Root-Finding Problems
Quoc Tran-Dinh
We derive an equivalent form of Halpern's fixed-point iteration scheme for solving a co-coercive equation (also called a root-finding problem), which can be viewed as a Nesterov's…
Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions
Quoc Tran-Dinh, Yang Luo
In this paper, we develop a new type of accelerated algorithms to solve some classes of maximally monotone equations as well as monotone inclusions. Instead of using Nesterov's acc…
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…