Results 61 to 70 of about 7,185 (222)

Efficient State Estimation in Power Networks for Reactive Power Losses Compensation [PDF]

open access: yes, 2013
We consider the problem of optimal reactive power compensation for the minimization of power distribution losses in a smart microgrid with raw measurements. We provide two distributed estimation algorithms, one ADMM-based and one JACOBI-like, in order to
Di Vittorio, Alberto
core  

Wasserstein Consensus ADMM

open access: yes, 2023
We introduce Wasserstein consensus alternating direction method of multipliers (ADMM) and its entropic-regularized version: Sinkhorn consensus ADMM, to solve measure-valued optimization problems with convex additive objectives.
Halder, Abhishek, Nodozi, Iman
core  

Regularized extreme learning machine based on variable step alternating direction method of multipliers

open access: yesShenzhen Daxue xuebao. Ligong ban
To address the deficiency of slow convergence rate and stagnation of error decay during later iteration of alternating direction method of multipliers (ADMM) for regularized extreme learning machine (RELM), we propose a dynamic step size ADMM-based RELM ...
LU Huihuang, ZOU Weidong, LI Yuxiang
doaj   +1 more source

Simultaneous T2, T2*, and R2′ Mapping for Multiple Sclerosis Using Nonlinear Model‐Based Reconstruction of Undersampled Radial RARE‐EPI MRI

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To demonstrate the synergy of undersampled radial 2in1‐RARE‐EPI acquisition and nonlinear model‐based reconstruction for accelerated and simultaneous T2, T2*, and R2′ mapping in brains of patients with multiple sclerosis (MS). Methods 2in1‐RARE‐EPI combines a RARE module with an EPI module to capture T2 and T2* information.
Jose Raul Velasquez Vides   +16 more
wiley   +1 more source

Accelerated Stochastic ADMM with Variance Reduction

open access: yes, 2023
Alternating Direction Method of Multipliers (ADMM) is a popular method for solving large-scale Machine Learning problems. Stochastic ADMM was proposed to reduce the per iteration computational complexity, which is more suitable for big data problems ...
Zhou, Jianying   +5 more
core  

Optimizing linear energy transfer distribution in intensity-modulated proton therapy using the alternating direction method of multipliers

open access: yesFrontiers in Oncology
PurposeThis study develop a novel linear energy transfer (LET) optimization method for intensity-modulated proton therapy (IMPT) with minimum monitor unit (MMU) constraint using the alternating direction method of multipliers (ADMM).Material and ...
Qingkun Fan   +10 more
doaj   +1 more source

Free‐Breathing Fat Quantification Using a Phase Error‐Corrected Cartesian Acquisition With Spiral Profile Ordering

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a phase‐corrected time‐interleaved multi‐echo gradient echo Cartesian acquisition with spiral profile ordering (TIMGRECASPR) for abdominal large‐FOV proton density fat fraction (PDFF) mapping at 3 T, demonstrating its sampling flexibility and inherent self‐gating capabilities at high isotropic resolutions.
Philipp Braun   +7 more
wiley   +1 more source

Hybrid FSK-PSK Waveform Optimization for Radar Based on Alternating Direction Method of Multiplier (ADMM)

open access: yesSensors, 2021
In this paper, a new radar signal modulated with a hybrid of the frequency shift keying (FSK) and the phase shift keying (PSK) signal—i.e., the FSK-PSK signal—is studied.
Zhiting Fei   +4 more
doaj   +1 more source

Initial State Privacy of Nonlinear Systems on Riemannian Manifolds

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley   +1 more source

Distributed Stochastic ADMM for Matrix Factorization

open access: yes, 2014
Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community.
Yu, Zhiqin   +7 more
core   +1 more source

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