168 citations · 486 across the 27 of their papers we have counts for
31 papers
Locating Theorems of Differential Inclusions Governed by Maximally Monotone Operators
Minh N. Dao, Hassan Saoud, Michel Théra
In this paper, we are interested in studying the asymptotic behavior of the solutions of differential inclusions governed by maximally monotone operators. In the case where the LaS…
A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems
Tan Nhat Pham, Minh N. Dao, Rakibuzzaman Shah +3
In this paper, we consider a class of structured nonconvex nonsmooth optimization problems, in which the objective function is formed by the sum of a possibly nonsmooth nonconvex f…
Data Size-Aware Downlink Massive MIMO: A Session-Based Approach
Tung T. Vu, Hien Quoc Ngo, Minh N. Dao +2
This letter considers the development of transmission strategies for the downlink of massive multiple-input multiple-output networks, with the objective of minimizing the completio…
Graph Augmentation Learning
Shuo Yu, Huafei Huang, Minh N. Dao +1
Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-…
Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations
Tung T. Vu, Hien Q. Ngo, Minh N. Dao +3
This work proposes novel synchronous, asynchronous, and session-based designs for energy-efficient massive multiple-input multiple-output networks to support federated learning (FL…
Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups
Tung T. Vu, Hien Quoc Ngo, Duy T. Ngo +2
With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond 5G and towards 6G systems. This work looks…
An adaptive splitting algorithm for the sum of three operators
Minh N. Dao, Hung M. Phan
Splitting algorithms for finding a zero of sum of operators often involve multiple steps which are referred to as forward or backward steps. Forward steps are the explicit use of t…
Generalized Bregman envelopes and proximity operators
Regina S. Burachik, Minh N. Dao, Scott B. Lindstrom
Every maximally monotone operator can be associated with a family of convex functions, called the Fitzpatrick family or family of representative functions. Surprisingly, in 2017, B…
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization
Canh T. Dinh, Tung T. Vu, Nguyen H. Tran +2
Non-Independent and Identically Distributed (non- IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL). Sev…
Inertial Proximal Block Coordinate Method for a Class of Nonsmooth Sum-of-Ratios Optimization Problems
Radu Ioan Boţ, Minh N. Dao, Guoyin Li
In this paper, we consider a class of nonsmooth sum-of-ratios fractional optimization problems with block structure. This model class is ubiquitous and encompasses several importan…
Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO
Tung T. Vu, Duy T. Ngo, Hien Quoc Ngo +3
Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless n…
Constraint reduction reformulations for projection algorithms with applications to wavelet construction
Minh Dao, Neil Dizon, Jeffrey Hogan +1
We introduce a reformulation technique that converts a many-set feasibility problem into an equivalent two-set problem. This technique involves reformulating the original feasibili…
Extrapolated Proximal Subgradient Algorithms for Nonconvex and Nonsmooth Fractional Programs
Radu Ioan Boţ, Minh N. Dao, Guoyin Li
In this paper, we consider a broad class of nonsmooth and nonconvex fractional programs, where the numerator can be written as the sum of a continuously differentiable convex funct…
Conical averagedness and convergence analysis of fixed point algorithms
Sedi Bartz, Minh N. Dao, Hung M. Phan
We study a conical extension of averaged nonexpansive operators and the role it plays in convergence analysis of fixed point algorithms. Various properties of conically averaged op…
Cell-Free Massive MIMO for Wireless Federated Learning
Tung T. Vu, Duy T. Ngo, Nguyen H. Tran +3
This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each…
The Generalized Bregman Distance
Regina S. Burachik, Minh N. Dao, Scott B. Lindstrom
Recently, a new distance has been introduced for the graphs of two point-to-set operators, one of which is maximally monotone. When both operators are the subdifferential of a prop…
Computing the resolvent of the sum of operators with application to best approximation problems
Minh N. Dao, Hung M. Phan
We propose a flexible approach for computing the resolvent of the sum of weakly monotone operators in real Hilbert spaces. This relies on splitting methods where strong convergence…
Adaptive Douglas-Rachford splitting algorithm for the sum of two operators
Minh N. Dao, Hung M. Phan
The Douglas-Rachford algorithm is a classical and powerful splitting method for minimizing the sum of two convex functions and, more generally, finding a zero of the sum of two max…
Union Averaged Operators with Applications to Proximal Algorithms for Min-Convex Functions
Minh N. Dao, Matthew K. Tam
In this paper we introduce and study a class of structured set-valued operators which we call union averaged nonexpansive. At each point in their domain, the value of such an opera…
The Douglas--Rachford algorithm for a hyperplane and a doubleton
Heinz H. Bauschke, Minh N. Dao, Scott B. Lindstrom
The Douglas--Rachford algorithm is a popular algorithm for solving both convex and nonconvex feasibility problems. While its behaviour is settled in the convex inconsistent case, t…
Linear convergence of the generalized Douglas-Rachford algorithm for feasibility problems
Minh N. Dao, Hung M. Phan
In this paper, we study the generalized Douglas-Rachford algorithm and its cyclic variants which include many projection-type methods such as the classical Douglas-Rachford algorit…
A Lyapunov-type approach to convergence of the Douglas-Rachford algorithm
Minh N. Dao, Matthew K. Tam
The Douglas-Rachford projection algorithm is an iterative method used to find a point in the intersection of closed constraint sets. The algorithm has been experimentally observed…
Regularizing with Bregman-Moreau envelopes
Heinz H. Bauschke, Minh N. Dao, Scott B. Lindstrom
Moreau's seminal paper, introducing what is now called the Moreau envelope and the proximity operator (also known as the proximal mapping), appeared in 1965. The Moreau envelope of…
Linear Convergence of Projection Algorithms
Minh N. Dao, Hung M. Phan
Projection algorithms are well known for their simplicity and flexibility in solving feasibility problems. They are particularly important in practice due to minimal requirements f…