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Minh N. Dao

Federation University Australia

31 papers hereh-index 221.2k citations55 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author10
  • middle author20
  • last author1

Across the 31 of 31 papers where every author was matched, so the position is known.

fields
  • math.OC20
  • cs.IT4
  • math.FA4
  • cs.LG2
  • eess.SP1
affiliations
  • Federation University Australia
Homepage
same name
  • Minh N. Dao — 7 papers, h 4
  • Minh N. Dao — 6 papers, h 2
  • Minh N. Dao — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142022
most citedParametric robust structured control design

168 citations · 486 across the 27 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

math.OC2022★ 2 cited

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…

math.OC2022★ 6 cited

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…

cs.IT2022

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…

cs.LG2022

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-…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.