Quoc Tran-Dinh

research

∙ 03/30/2023

Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments

The extragradient (EG), introduced by G. M. Korpelevich in 1976, is a we...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 02/08/2023

Extragradient-Type Methods with 𝒪(1/k) Convergence Rates for Co-Hypomonotone Inclusions

In this paper, we develop two “Nesterov's accelerated” variants of the w...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 01/08/2023

Accelerated Randomized Block-Coordinate Algorithms for Co-coercive Equations and Applications

In this paper, we develop an accelerated randomized block-coordinate alg...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 12/19/2022

Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis

In this book chapter, we briefly describe the main components that const...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 12/01/2022

Data Integration Via Analysis of Subspaces (DIVAS)

Modern data collection in many data paradigms, including bioinformatics,...

0 Jack Prothero, et al. ∙

research

∙ 10/15/2021

Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions

In this paper, we develop a new type of accelerated algorithms to solve ...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 03/05/2021

Federated Learning with Randomized Douglas-Rachford Splitting Methods

In this paper, we develop two new algorithms, called, FedDR and asyncFed...

0 Nhan H. Pham, et al. ∙

research

∙ 11/24/2020

Shuffling Gradient-Based Methods with Momentum

We combine two advanced ideas widely used in optimization for machine le...

0 Trang H. Tran, et al. ∙

research

∙ 11/20/2020

Convergence Analysis of Homotopy-SGD for non-convex optimization

First-order stochastic methods for solving large-scale non-convex optimi...

0 Matilde Gargiani, et al. ∙

research

∙ 10/27/2020

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...

0 Marten van Dijk, et al. ∙

research

∙ 08/20/2020

An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization

In this note we propose a new variant of the hybrid variance-reduced pro...

0 Deyi Liu, et al. ∙

research

∙ 07/17/2020

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

The feasibility of federated learning is highly constrained by the serve...

0 Marten van Dijk, et al. ∙

research

∙ 06/27/2020

Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems

We develop a novel variance-reduced algorithm to solve a stochastic nonc...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 03/03/2020

Randomized Primal-Dual Algorithms for Composite Convex Minimization with Faster Convergence Rates

We develop two novel randomized primal-dual algorithms to solve nonsmoot...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 03/01/2020

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning

We propose a novel hybrid stochastic policy gradient estimator by combin...

0 Nhan H. Pham, et al. ∙

research

∙ 02/19/2020

A Unified Convergence Analysis for Shuffling-Type Gradient Methods

In this paper, we provide a unified convergence analysis for a class of ...

0 Lam M. Nguyen, et al. ∙

research

∙ 02/17/2020

Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization

We develop two new stochastic Gauss-Newton algorithms for solving a clas...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 02/17/2020

A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization

We demonstrate how to scalably solve a class of constrained self-concord...

0 Deyi Liu, et al. ∙

research

∙ 07/26/2019

Using positive spanning sets to achieve stationarity with the Boosted DC Algorithm

The Difference of Convex function Algorithm (DCA) is widely used for min...

0 Francisco J. Aragón Artacho, et al. ∙

research

∙ 07/08/2019

A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization

In this paper, we introduce a new approach to develop stochastic optimiz...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 05/15/2019

Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization

We introduce a hybrid stochastic estimator to design stochastic gradient...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 02/15/2019

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization

In this paper, we propose a new stochastic algorithmic framework to solv...

0 Nhan H. Pham, et al. ∙

research

∙ 01/11/2018

Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive stepsizes and convergence

We revisit the classical Douglas-Rachford (DR) method for finding a zero...

0 Dirk A. Lorenz, et al. ∙

research

∙ 11/09/2017

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

We propose a new randomized coordinate descent method for a convex optim...

0 Ahmet Alacaoglu, et al. ∙

research

∙ 03/14/2017

Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods

We study the smooth structure of convex functions by generalizing a powe...

0 Tianxiao Sun, et al. ∙

research

∙ 06/10/2016

Extended Gauss-Newton and Gauss-Newton-ADMM Algorithms for Low-Rank Matrix Optimization

We develop a generic Gauss-Newton (GN) framework for solving a class of ...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 03/21/2016

Convex block-sparse linear regression with expanders -- provably

Sparse matrices are favorable objects in machine learning and optimizati...

0 Anastasios Kyrillidis, et al. ∙

research

∙ 03/05/2016

A single-phase, proximal path-following framework

We propose a new proximal, path-following framework for a class of const...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 09/01/2015

Adaptive Smoothing Algorithms for Nonsmooth Composite Convex Minimization

We propose an adaptive smoothing algorithm based on Nesterov's smoothing...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 02/04/2015

Composite convex minimization involving self-concordant-like cost functions

The self-concordant-like property of a smooth convex function is a new a...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 06/20/2014

A Primal-Dual Algorithmic Framework for Constrained Convex Minimization

We present a primal-dual algorithmic framework to obtain approximate sol...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 05/13/2014

Scalable sparse covariance estimation via self-concordance

We consider the class of convex minimization problems, composed of a sel...

0 Anastasios Kyrillidis, et al. ∙

research

∙ 08/13/2013

Composite Self-Concordant Minimization

We propose a variable metric framework for minimizing the sum of a self-...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 01/08/2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

We propose an algorithmic framework for convex minimization problems of ...

0 Quoc Tran-Dinh, et al. ∙

Quoc Tran-Dinh

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