Research interests
Mathematical Optimization, Convex and Variational Analysis, Control Theory, Signal Processing, and Machine Learning.
Research interests
Mathematical Optimization, Convex and Variational Analysis, Control Theory, Signal Processing, and Machine Learning.
Preprint
[P6] Geometric stability analysis for differential inclusions governed by maximally monotone operators
H. Saoud, M. Théra, and M.N. Dao
[P5] Relocated fixed-point iterations with applications to variable stepsize resolvent splitting
F. Atenas, H.H. Bauschke, M.N. Dao, and M.K. Tam
[P4] Douglas--Rachford for multioperator comonotone inclusions with applications to multiblock optimization
J.H. Alcantara, M.N. Dao, and A. Takeda
[P3] A general approach to distributed operator splitting
M.N. Dao, M.K. Tam, and T.D. Truong
[P2] Projected proximal gradient trust-region algorithm for nonsmooth optimization
M.N. Dao, H.M. Phan, and L. Roberts
[P1] A distributed proximal splitting method with linesearch for locally Lipschitz data
F. Atenas, M.N. Dao, and M.K. Tam
Referred journal articles
[J47] Solving non-monotone inclusions using monotonicity of pairs of operators
B.K. Le, M.N. Dao, and M. Théra
[link] [arXiv:2501.13637]
Journal of Optimization Theory and Applications 207 (2025), no. 1, Paper No. 18, 19pp.
[J46] On the equivalence of a Hessian-free inequality and Lipschitz continuous Hessian
R.I. Boţ, M.N. Dao, T. Liu, B.F. Lourenço, and N. Marumo
MATRIX Annals, to appear.
[J45] Doubly relaxed forward-Douglas--Rachford splitting for the sum of two nonconvex and a DC function
M.N. Dao, T.N. Pham, and P.T. Tung
Journal of Scientific Computing 104 (2025), no 2., Paper No. 35, 30 pp.
[link] [full-text view-only] [arXiv:2405.08485]
[J44] A proximal splitting algorithm for generalized DC programming with applications in signal recovery
T.N. Pham, M.N. Dao, N. Amjady, and R. Shah
European Journal of Operational Research 326 (2025), no. 1, 42--53.
[link] [arXiv:2409.01535]
[J43] Douglas--Rachford is the best projection method in its family
M.N. Dao, M. Dressler, H. Liao, and V. Roshchina
Optimization, DOI: 10.1080/02331934.2024.2444623.
[link] [arXiv:2310.17077]
[J42] Low and medium voltage distribution network planning with distributed energy resources: a survey
T.N. Pham, R. Shah, M.N. Dao, N. Sultanova, and S. Islam
Electrical Engineering 107 (2025), no. 2, 1797--1828.
[link]
[J41] Bregman proximal linearized ADMM for minimizing separable sums coupled by a difference of functions
T.N. Pham, M.N. Dao, A. Eberhard, and N. Sultanova
Journal of Optimization Theory and Applications 203 (2024), no. 2, 1622-–1658.
[link] [full-text view-only] [arXiv:2401.02635]
[J40] Joint User Association and Power Control for Cell-Free Massive MIMO
C. Hao, T.T. Vu, H.Q. Ngo, M.N. Dao, X. Dang, C. Wang, and M. Matthaiou
IEEE Internet of Things Journal 11 (2024), no. 9, 15823--15841.
[link] [arXiv:2401.02701]
[J39] A new look and convergence rate of federated multi-task learning with Laplacian regularization
C.T. Dinh, T.T. Vu, N.H. Tran, M.N. Dao, and H. Zhang
IEEE Transactions on Neural Networks and Learning Systems 35 (2024), no. 6, 8075--8085.
[link] [arXiv:2102.07148]
[J38] Locating theorems of differential inclusions governed by maximally monotone operators
M.N. Dao, H. Saoud, and M. Théra
SIAM Journal on Optimization 33 (2023), no. 4, 2703--2720.
[link] [arXiv:2210.08950]
[J37] A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems
T.N. Pham, M.N. Dao, R. Shah, N. Sultanova, G. Li, and S. Islam
Numerical Algorithms 94 (2023), no. 4, 1763--1795.
[J36] Inertial proximal block coordinate method for a class of nonsmooth sum-of-ratios optimization problems
R.I. Boţ, M.N. Dao, and G. Li
SIAM Journal on Optimization 33 (2023), no. 2, 361--393.
[link] [freely accessible ePrint] [arXiv:2011.09782]
[J35] Energy-efficient massive MIMO for federated learning: Transmission designs and resource allocations
T.T. Vu, H.Q. Ngo, M.N. Dao, D.T. Ngo, E.G. Larsson, and T. Le-Ngoc
IEEE Open Journal of the Communications Society 3 (2022), 2329--2346.
[J34] Joint resource allocation to minimize execution time of federated learning in cell-free massive MIMO
T.T. Vu, D.T. Ngo, H.Q. Ngo, M.N. Dao, N.H. Tran, and R.H. Middleton
IEEE Internet of Things Journal 9 (2022), no. 21, 21736--21750.
[link] [arXiv:2009.02031]
[J33] Data size-aware downlink massive MIMO: A session-based approach
T.T. Vu, H.Q. Ngo, M.N. Dao, M. Matthaiou, and E.G. Larsson
IEEE Wireless Communications Letters 11 (2022), no. 7, 1468--1472.
[link] [arXiv:2205.04369]
[J32] Extrapolated proximal subgradient algorithms for nonconvex and nonsmooth fractional programs
R.I. Boţ, M.N. Dao, and G. Li
Mathematics of Operations Research 47 (2022), no. 3, 2415--2443.
[link] [arXiv:2003.04124]
[J31] Scheduling and power control for connectivity enhancement in multi-hop I2V/V2V networks
B.L. Nguyen, D.T. Ngo, M.N. Dao, V.N.Q. Bao, and H.L. Vu
IEEE Transactions on Intelligent Transportation Systems 23 (2022), no. 8, 10322--10332.
[link]
[J30] Conical averagedness and convergence analysis of fixed point algorithms
S. Bartz, M.N. Dao, and H.M. Phan
Journal of Global Optimization 82 (2022), no. 2, 351--373.
[link] [full-text view-only] [arXiv:1910.14185]
[J29] Dynamic V2I/V2V cooperative scheme for connectivity and throughput enhancement
B.L. Nguyen, D.T. Ngo, N.H. Tran, M.N. Dao, and H.L. Vu
IEEE Transactions on Intelligent Transportation Systems 23 (2022), no. 2, 1236--1246.
[link]
[J28] An adaptive splitting algorithm for the sum of three operators
M.N. Dao and H.M. Phan
Fixed Point Theory and Algorithms for Sciences and Engineering 2021, Paper No. 16, 19 pp.
[link] [SharedIt] [arXiv:2104.05460]
[J27] Generalized Bregman envelopes and proximity operators
R.S. Burachik, M.N. Dao, and S.B. Lindstrom
Journal of Optimization Theory and Applications 190 (2021), no. 3, 744--778.
[link] [full-text view-only] [arXiv:2102.10730]
[J26] Constraint reduction reformulations for projection algorithms with applications to wavelet construction
M.N. Dao, N.D. Dizon, J.A. Hogan, and M.K. Tam
Journal of Optimization Theory and Applications 190 (2021), no. 1, 201--233 .
[link] [arXiv:2006.05898]
[J25] The generalized Bregman distance
R.S. Burachik, M.N. Dao, and S.B. Lindstrom
SIAM Journal on Optimization 31 (2021), no. 1, 404--424.
[link] [arXiv:1909.08206]
[J24] A joint scheduling and power control scheme for hybrid I2V/V2V networks
B.L. Nguyen, D.T. Ngo, M.N. Dao, Q.-T. Duong, and M. Okada
IEEE Transactions on Vehicular Technology 69 (2020), no. 12, 15668--15681.
[link]
[J23] Cell-free massive MIMO for wireless federated learning
T.T. Vu, D.T. Ngo, N.H. Tran, H.Q Ngo, M.N. Dao, and R.H. Middleton
IEEE Transactions on Wireless Communications 19 (2020), no. 10, 6377--6392.
[link] [arXiv:1909.12567]
[J22] Energy-efficient full-duplex enabled cloud radio access networks
T.T. Vu, D.T. Ngo, M.N. Dao, Q.-T. Duong, M. Okada, H. Nguyen-Le, and R.H. Middleton
IEICE Transactions on Communications E103-B (2020), no. 1, 71--78.
[link]
[J21] Computing the resolvent of the sum of operators with application to best approximation problems
M.N. Dao and H.M. Phan
Optimization Letters 14 (2020), no. 5, 1193--1205.
[link] [full-text view-only] [arXiv:1809.03921]
[J20] Adaptive Douglas--Rachford splitting algorithm for the sum of two operators
M.N. Dao and H.M. Phan
SIAM Journal on Optimization 29 (2019), no. 4, 2697--2724.
[link] [arXiv:1809.00761]
[J19] Linear convergence of projection algorithms
M.N. Dao and H.M. Phan
Mathematics of Operations Research 44 (2019), no. 2, 715--738.
[link] [arXiv:1609.00341]
[J18] The Douglas--Rachford algorithm for a hyperplane and a doubleton
H.H. Bauschke, M.N. Dao, and S.B. Lindstrom
Journal of Global Optimization 74 (2019), no. 1, 79--93.
[link] [full-text view-only] [arXiv:1804.08880]
[J17] Union averaged operators with applications to proximal algorithms for min-convex functions
M.N. Dao and M.K. Tam
Journal of Optimization Theory and Applications 181 (2019), no. 1, 61--94.
[link] [full-text view-only] [arXiv:1807.05810]
[J16] A Lyapunov-type approach to convergence of the Douglas-Rachford algorithm
M.N. Dao and M.K. Tam
Journal of Global Optimization 73 (2019), no. 1, 83--112.
[link] [full-text view-only] [arXiv:1706.04846]
[J15] Regularizing with Bregman-Moreau envelopes
H.H. Bauschke, M.N. Dao, and S.B. Lindstrom
SIAM Journal on Optimization 28 (2018), no. 4, 3208--3228.
[link] [arXiv:1705.06019]
[J14] Spectral and energy efficiency maximization for content-centric C-RANs with edge caching
T.T. Vu, D.T. Ngo, M.N. Dao, S. Durrani, and R.H. Middleton
IEEE Transactions on Communications 66 (2018), no. 12, 6628--6642.
[link]
[J13] Energy efficiency maximization for downlink cloud radio access networks with data sharing and data compression
T.T. Vu, D.T. Ngo, M.N. Dao, S. Durrani, D.H.N. Nguyen, and R.H. Middleton
IEEE Transactions on Wireless Communications 17 (2018), no. 8, 4955--4970.
[link]
[J12] Linear convergence of the generalized Douglas-Rachford algorithm for feasibility problems
M.N. Dao and H.M. Phan
Journal of Global Optimization 72 (2018), no. 3, 443--474.
[link] [full-text view-only] [arXiv:1710.09814]
[J11] On the finite convergence of the Douglas-Rachford algorithm for solving (not necessarily convex) feasibility problems in Euclidean spaces
H.H. Bauschke and M.N. Dao
SIAM Journal on Optimization 27 (2017), no. 1, 507--537.
[link] [arXiv:1604.04657]
[J10] Nonconvex bundle method with application to a delamination problem
M.N. Dao, J. Gwinner, D. Noll, and N. Ovcharova
Computational Optimization and Applications 65 (2016), no. 1, 173--203.
[link] [full-text view-only] [arXiv:1401.6807]
[J09] On Slater's condition and finite convergence of the Douglas-Rachford algorithm
H.H. Bauschke, M.N. Dao, D. Noll, and H.M. Phan
Journal of Global Optimization 65 (2016), no. 2, 329--349. This was selected for the 2016 Journal of Global Optimization Best Paper Award.
[link] [full-text view-only] [arXiv:1504.06969]
[J08] The Douglas-Rachford algorithm in the affine-convex case
H.H. Bauschke, M.N. Dao, and W.M. Moursi
Operations Research Letters 44 (2016), no. 3, 379--382.
[link] [arXiv:1505.06408]
[J07] Proximal point algorithm, Douglas-Rachford algorithm and alternating projections: a case study
H.H. Bauschke, M.N. Dao, D. Noll, and H.M. Phan
Journal of Convex Analysis 23 (2016), no. 1, 237--261.
[link] [arXiv:1501.06603]
[J06] On Fejér monotone sequences and nonexpansive mappings
H.H. Bauschke, M.N. Dao, and W.M. Moursi
Linear and Nonlinear Analysis 1 (2015), no. 2, 287--295.
[link] [arXiv:1507.05585]
[J05] Bundle method for nonconvex nonsmooth constrained optimization
M.N. Dao
Journal of Convex Analysis 22 (2015), no. 4, 1061--1090.
[J04] Robust eigenstructure clustering by nonsmooth optimization
M.N. Dao, D. Noll, and P. Apkarian
International Journal of Control 88 (2015), no. 8, 1441--1455.
[J03] Parametric robust structured control design
P. Apkarian, M.N. Dao, and D. Noll
IEEE Transactions on Automatic Control 60 (2015), no. 7, 1857--1869.
[J02] Minimizing memory effects of a system
M.N. Dao and D. Noll
Mathematics of Control, Signals, and Systems 27 (2015), no. 1, 77--110.
[J01] The total specialization of modules over a local ring
Dao N.M. and Dam V.N.
VNU Journal of Science, Mathematics - Physics 25 (2009), no. 1, 39--45.
Referred conference proceedings articles
[C9] Multipair DF Relaying with Network-Assisted Full-Duplex Cell-Free Massive MIMO
T.T. Vu, H.Q. Ngo, M.N. Dao, and E.G. Larsson
Proc. 31st European Signal Processing Conference (EUSIPCO), Helsinki, September 2023, pp. 1474--1478.
[link]
[C8] User Association and Power Control in Cell-Free Massive MIMO with the APG Method
C. Hao, T.T. Vu, H.Q. Ngo, M.N. Dao, X. Dang, and M. Matthaiou
Proc. 31st European Signal Processing Conference (EUSIPCO), Helsinki, September 2023, pp. 1469--1473.
[link]
[C7] Graph augmentation learning
S. Yu, H. Huang, M.N. Dao, and F. Xia
Companion Proc. Web Conference (WWW '22 Companion), Lyon, April 2022.
[link] [arXiv:2203.09020]
[C6] Energy-efficient massive MIMO for serving multiple federated learning groups
T.T. Vu, H.Q. Ngo, D.T. Ngo, M.N. Dao, and E.G. Larsson
Proc. IEEE Global Communications Conference (GLOBECOM), Madrid, December 2021.
[link] [arXiv:2108.13512]
[C5] Straggler effect mitigation for federated learning in cell-free massive MIMO
T.T. Vu, D.T. Ngo, H.Q. Ngo, M.N. Dao, N.H. Tran, and R.H. Middleton
Proc. IEEE Int. Conf. Communications (ICC), Montreal, June 2021.
[link]
[C4] Energy-efficient design for downlink cloud radio access networks
T.T. Vu, D.T. Ngo, M.N. Dao, S. Durrani, D.H.N. Nguyen, and R.H. Middleton
Proc. IEEE Int. Conf. Communications (ICC), Kansas City, May 2018.
[link]
[C3] Simultaneous plant and controller optimization based on non-smooth techniques
M.N. Dao and D. Noll
Lecture Notes in Engineering and Computer Science: Proc. World Congress Eng. Comp. Sci. (WCECS), vol. II, San Francisco, 2013, pp. 855--861.
[C2] Optimized eigenstructure assignment
M.N. Dao, D. Noll, and P. Apkarian
Proc. Int. Conf. Informatics in Control, Automation and Robotics (ICINCO), Reykjavík, July 2013, pp. 307--314.
[C1] Minimizing the memory of a system
M.N. Dao and D. Noll
Proc. Asian Control Conf. (ASCC), Istanbul, June 2013.