An efficient augmented Lagrangian method with applications to total variation minimization
Based on the classic augmented Lagrangian multiplier method, we propose, analyze and
test an algorithm for solving a class of equality-constrained non-smooth optimization …
test an algorithm for solving a class of equality-constrained non-smooth optimization …
Image compressive sensing recovery via collaborative sparsity
Compressive sensing (CS) has drawn quite an amount of attention as a joint sampling and
compression approach. Its theory shows that when the signal is sparse enough in some …
compression approach. Its theory shows that when the signal is sparse enough in some …
Computed tomography image reconstruction from few views via log-norm total variation minimization
Y Sun, H Chen, J Tao, L Lei - Digital Signal Processing, 2019 - Elsevier
In this paper, we propose an iterative algorithm for the computed tomography (CT) image
reconstruction from severely under-sampled data. Instead of using ℓ 1-norm sparse …
reconstruction from severely under-sampled data. Instead of using ℓ 1-norm sparse …
ECT image reconstruction based on alternating direction approximate Newton algorithm
C Wang, Q Guo, H Wang, Z Cui… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
To solve the underdetermined problem and the mismatches of position and size of the
objects caused by the ill-posed sensitivity matrix in electrical capacitance tomography (ECT) …
objects caused by the ill-posed sensitivity matrix in electrical capacitance tomography (ECT) …
[HTML][HTML] Constrained total generalized p-variation minimization for few-view X-ray computed tomography image reconstruction
H Zhang, L Wang, B Yan, L Li, A Cai, G Hu - PLoS One, 2016 - journals.plos.org
Total generalized variation (TGV)-based computed tomography (CT) image reconstruction,
which utilizes high-order image derivatives, is superior to total variation-based methods in …
which utilizes high-order image derivatives, is superior to total variation-based methods in …
An inexact symmetric ADMM algorithm with indefinite proximal term for sparse signal recovery and image restoration problems
F Jiang, Z Wu - Journal of Computational and Applied Mathematics, 2023 - Elsevier
Compared with the alternating direction method of multipliers (ADMM), the symmetric
ADMM, which updates the Lagrange multiplier twice in each iteration, is a more efficient …
ADMM, which updates the Lagrange multiplier twice in each iteration, is a more efficient …
Robust decentralized learning using ADMM with unreliable agents
Many signal processing and machine learning problems can be formulated as consensus
optimization problems which can be solved efficiently via a cooperative multi-agent system …
optimization problems which can be solved efficiently via a cooperative multi-agent system …
An efficient method for solving a matrix least squares problem over a matrix inequality constraint
In this paper, we consider solving a class of matrix inequality constrained matrix least
squares problems of the form min & 1 2 ‖ ∑\limits _ i= 1^ t A_iXB_i-C ‖^ 2\subject\text to & …
squares problems of the form min & 1 2 ‖ ∑\limits _ i= 1^ t A_iXB_i-C ‖^ 2\subject\text to & …
A proximal Peaceman–Rachford splitting method for compressive sensing
M Sun, J Liu - Journal of Applied Mathematics and Computing, 2016 - Springer
Recently, He et al. proposed a modified Peaceman–Rachford splitting method (MPRSM) for
separable convex programming, which includes compressive sensing (CS) as a special …
separable convex programming, which includes compressive sensing (CS) as a special …
[PDF][PDF] Robust federated learning using admm in the presence of data falsifying byzantines
In this paper, we consider the problem of federated (or decentralized) learning using ADMM
with multiple agents. We consider a scenario where a certain fraction of agents (referred to …
with multiple agents. We consider a scenario where a certain fraction of agents (referred to …