Results 101 to 110 of about 7,185 (222)
Magnetic Resonance in Medicine, Volume 95, Issue 6, Page 3284-3297, June 2026.ABSTRACT Purpose Quantitative mapping of cardiac tissue properties is used clinically in diagnosis and monitoring of a wide variety of cardiac pathologies. Cardiac Magnetic Resonance Fingerprinting (cMRF) enables rapid and simultaneous quantification of multiple parameters in the myocardium from a single scan.
Evan Cummings +5 more
wiley +1 more source
Convergence analysis on a modified generalized alternating direction method of multipliers
Journal of Inequalities and Applications, 2018 The alternating direction method of multipliers (ADMM) is one of the most powerful and successful methods for solving convex composite minimization problem.
Sha Lu, Zengxin Wei
doaj +1 more source
Advanced Quantum Technologies, Volume 9, Issue 6, June 2026.Network monitoring is fundamental to effective traffic engineering (TE) in quantum networks and although nondestructive techniques such as weak measurement, quantum non‐demolition (QND) measurement, and protective measurement have been proposed, their roles in supporting TE have not been systematically examined. This paper proposes a unified analytical
Joachim Notcker +4 more
wiley +1 more source
ADMM for the SDP relaxation of the QAP
Mathematical Programming Computation, 2018 The semidefinite programming (SDP) relaxation has proven to be extremely strong for many hard discrete optimization problems. This is in particular true for the quadratic assignment problem (QAP), arguably one of the hardest NP-hard discrete optimization problems. There are several difficulties that arise in efficiently solving the SDP relaxation, e.g.,
Danilo Elias Oliveira +2 more
openaire +3 more sources
Evaluation of the Performance of the Alternating Direction Method of Multipliers in Artificial Neural Networks [PDF]
openThis thesis presents a comprehensive evaluation of the Alternating Direction Method of Multipliers (ADMM) algorithm for neural network optimization, comparing its performance against traditional gradient-based methods, including Gradient Descent (GD)
MEDA, ERGYS
core
Distributed Optimization of Finite Condition Number for Laplacian Matrix in Multi‐Agent Systems
International Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 5030-5043, June 2026.ABSTRACT This paper addresses the distributed optimization of the finite condition number of the Laplacian matrix in multi‐agent systems. The finite condition number, defined as the ratio of the largest to the second smallest eigenvalue of the Laplacian matrix, plays an important role in determining the convergence rate and performance of consensus ...
Yicheng Xu, Faryar Jabbari
wiley +1 more source
Computationally Efficient Short-Range 3D SAR Imaging via Modified Augmented Lagrangian-Based FA-ADMM and Deep Learning Models [PDF]
Journal of Electromagnetic Engineering and ScienceShort-range three-dimensional (3D) synthetic aperture radar (SAR) imaging has drawn significant attention across various domains, including security surveillance, non-destructive testing, and medical diagnostics.
The-Hien Pham, Ic-Pyo Hong
doaj +1 more source
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.Abstract The three‐dimensional (3D) gravity inversion problem is the process of delineating the volumetric mass distributions from the surface gravity anomalies. Recently, many innovative Convolutional Neural Network (CNN) based algorithms have found some success in reduction of computation costs and delineation of sharper boundaries of causative ...
Abhirup Chaudhuri +2 more
wiley +1 more source
Connecting Federated ADMM to Bayes
CoRRWe provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complementary strengths. Specifically, we show that the dual variables in ADMM naturally emerge through the 'site' parameters used in VB with isotropic Gaussian covariances.
Siddharth Swaroop +2 more
openaire +3 more sources
Fast-and-light stochastic ADMM
, 2016 The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that ...
Kwok, James T., Shuai, Zheng
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