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Mingyi Hong
Mingyi Hong
Associate Professor, University of Minnesota; Amazon Scholar
Verified email at umn.edu - Homepage
Title
Cited by
Cited by
Year
A unified convergence analysis of block successive minimization methods for nonsmooth optimization
M Razaviyayn, M Hong, ZQ Luo
SIAM Journal on Optimization 23 (2), 1126-1153, 2013
12262013
Learning to optimize: Training deep neural networks for interference management
H Sun, X Chen, Q Shi, M Hong, X Fu, ND Sidiropoulos
IEEE Transactions on Signal Processing 66 (20), 5438-5453, 2018
1046*2018
Towards k-means-friendly spaces: Simultaneous deep learning and clustering
B Yang, X Fu, ND Sidiropoulos, M Hong
international conference on machine learning, 3861-3870, 2017
10412017
Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems
M Hong, ZQ Luo, M Razaviyayn
SIAM Journal on Optimization 26 (1), 337-364, 2016
9532016
On the linear convergence of the alternating direction method of multipliers.
M Hong, ZQ Luo
Mathematical Programming 162, 2017
8262017
Multi-agent distributed optimization via inexact consensus ADMM
TH Chang, M Hong, X Wang
IEEE Transactions on Signal Processing 63 (2), 482-497, 2015
4642015
A unified algorithmic framework for block-structured optimization involving big data: With applications in machine learning and signal processing
M Hong, M Razaviyayn, ZQ Luo, JS Pang
IEEE Signal Processing Magazine 33 (1), 57-77, 2015
4552015
Topology attack and defense for graph neural networks: An optimization perspective
K Xu, H Chen, S Liu, PY Chen, TW Weng, M Hong, X Lin
Proceedings of the 28th International Joint Conference on Artificial …, 2019
3982019
Joint base station clustering and beamformer design for partial coordinated transmission in heterogeneous networks
M Hong, R Sun, H Baligh, ZQ Luo
IEEE Journal on Selected Areas in Communications 31 (2), 226-240, 2013
3432013
On the convergence of a class of adam-type algorithms for non-convex optimization
X Chen, S Liu, R Sun, M Hong
International Conference on Learning Representations, 2018
3322018
A deep learning method for online capacity estimation of lithium-ion batteries
S Shen, M Sadoughi, X Chen, M Hong, C Hu
Journal of Energy Storage 25, 100817, 2019
3002019
Fedpd: A federated learning framework with adaptivity to non-iid data
X Zhang, M Hong, S Dhople, W Yin, Y Liu
IEEE Transactions on Signal Processing 69, 6055-6070, 2021
2542021
A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic
M Hong, HT Wai, Z Wang, Z Yang
SIAM Journal on Optimization 33 (1), 147-180, 2023
2372023
Asynchronous distributed ADMM for large-scale optimization—Part I: Algorithm and convergence analysis
TH Chang, M Hong, WC Liao, X Wang
IEEE Transactions on Signal Processing 64 (12), 3118-3130, 2016
2292016
Asynchronous distributed ADMM for large-scale optimization—Part I: Algorithm and convergence analysis
TH Chang, M Hong, WC Liao, X Wang
IEEE Transactions on Signal Processing 64 (12), 3118-3130, 2016
2292016
Transmit solutions for MIMO wiretap channels using alternating optimization
Q Li, M Hong, HT Wai, YF Liu, WK Ma, ZQ Luo
IEEE Journal on Selected Areas in Communications 31 (9), 1714-1727, 2013
2262013
Fedbcd: A communication-efficient collaborative learning framework for distributed features
Y Liu, X Zhang, Y Kang, L Li, T Chen, M Hong, Q Yang
IEEE Transactions on Signal Processing 70, 4277-4290, 2022
224*2022
Hybrid block successive approximation for one-sided non-convex min-max problems: algorithms and applications
S Lu, I Tsaknakis, M Hong, Y Chen
IEEE Transactions on Signal Processing 68, 3676-3691, 2020
209*2020
Iteration complexity analysis of block coordinate descent methods
M Hong, X Wang, M Razaviyayn, ZQ Luo
Mathematical Programming 163, 85-114, 2017
1972017
Penalty dual decomposition method for nonsmooth nonconvex optimization
Q Shi, M Hong, X Fu, TH Chang
IEEE Transactions on Signal Processing, 2020
196*2020
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