Results 51 to 60 of about 166,464,526 (278)
Fast ℓ1-regularized space-Time adaptive processing using alternating direction method of multipliers [PDF]
, 2017 Motivated by the sparsity of filter coefficients in full-dimension space-Time adaptive processing (STAP) algorithms, this paper proposes a fast ℓ1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the ...
Wu, Manqing +12 more
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Learning-based accelerated sparse signal recovery algorithms
ICT Express, 2021 In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method ...
Dohyun Kim, Daeyoung Park
doaj +1 more source
Fast Alternating Direction Multipliers Method by Generalized Krylov Subspaces [PDF]
, 2022 The Alternating Direction Multipliers Method (ADMM) is a very popular and powerful algorithm for the solution of many optimization problems. In the recent years it has been widely used for the solution of ill-posed inverse problems.
Buccini A.
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Scalable Stochastic Alternating Direction Method of Multipliers
CoRR, 2015 Stochastic alternating direction method of multipliers (ADMM), which visits only one sample or a mini-batch of samples each time, has recently been proved to achieve better performance than batch ADMM. However, most stochastic methods can only achieve a convergence rate $O(1/\sqrt T)$ on general convex problems,where T is the number of iterations ...
Shen-Yi Zhao, Wu-Jun Li, Zhi-Hua Zhou
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Fast Stochastic Alternating Direction Method of Multipliers
CoRR, 2013 In this paper, we propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, the proposed algorithm improves the convergence rate on convex problems from
Leon Wenliang Zhong, James T. Kwok
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PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging [PDF]
, 2014 In recent works, compressed sensing (CS) and convex opti- mization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest con- tributions [1,
Wiaux, Yves; id_orcid +8 more
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Advanced Functional Materials, EarlyView.Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Low‐frequency noise spectroscopy defines the resolvable conductance states of synaptic FeFETs by coupling read‐current fluctuation with usable dynamic range. The resulting noise‐limited bit precision establishes a universal, device‐agnostic reliability metric beyond the memory window, enabling quantitative benchmarking and rational design of high ...
Jaehong Park +12 more
wiley +1 more source
Fast Model Predictive Control Based on Adaptive Alternating Direction Method of Multipliers
Journal of Chemistry, 2019 Model Predictive Control (MPC) can effectively handle control problem with disturbances, multicontrol variables, and complex constraints and is widely used in various control systems.
Yu Li +4 more
doaj +1 more source
A Consensus Approach to Distributed Convex Optimization in Multi-Agent Systems [PDF]
, 2013 In this thesis we address the problem of distributed unconstrained convex optimization under separability assumptions, i.e., the framework where a network of agents, each endowed with local private convex cost and subject to communication constraints ...
Filippo Zanella, Zanella, Filippo
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