Results 11 to 20 of about 27,846 (265)
An Adaptive Alternating Direction Method of Multipliers
Journal of Optimization Theory and Applications, 2022 AbstractThe alternating direction method of multipliers (ADMM) is a powerful splitting algorithm for linearly constrained convex optimization problems. In view of its popularity and applicability, a growing attention is drawn toward the ADMM in nonconvex settings.
Sedi Bartz, Rubén Campoy, Hung M. Phan
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Distributed Alternating Direction Method of Multipliers [PDF]
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012 We consider a network of agents that are cooperatively solving a global unconstrained optimization problem, where the objective function is the sum of privately known local objective functions of the agents. Recent literature on distributed optimization methods for solving this problem focused on subgradient based methods, which typically converge at ...
Wei, Ermin, Ozdaglar, Asuman E.
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On the linear convergence of the alternating direction method of multipliers [PDF]
Mathematical Programming, 2016 We analyze the convergence rate of the alternating direction method of multipliers (ADMM) for minimizing the sum of two or more nonsmooth convex separable functions subject to linear constraints. Previous analysis of the ADMM typically assumes that the objective function is the sum of only two convex functions defined on two separable blocks of ...
Hong, Mingyi, Luo, Zhi-Quan
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Alternating direction method of multipliers for polynomial optimization
2023 European Control Conference (ECC), 2023 Multivariate polynomial optimization is a prevalent model for a number of engineering problems. From a mathematical viewpoint, polynomial optimization is challenging because it is non-convex. The Lasserre's theory, based on semidefinite relaxations, provides an effective tool to overcome this issue and to achieve the global optimum.
V Cerone, S Fosson, S Pirrera, D Regruto
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Convergence Analysis of Multiblock Inertial ADMM for Nonconvex Consensus Problem
Journal of Mathematics, 2023 The alternating direction method of multipliers (ADMM) is one of the most powerful and successful methods for solving various nonconvex consensus problem.
Yang Liu, Yazheng Dang
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Alternating Direction Method of Multipliers for Quantization
CoRR, 2020 Quantization of the parameters of machine learning models, such as deep neural networks, requires solving constrained optimization problems, where the constraint set is formed by the Cartesian product of many simple discrete sets. For such optimization problems, we study the performance of the Alternating Direction Method of Multipliers for ...
Tianjian Huang +4 more
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A parallel multi‐block alternating direction method of multipliers for tensor completion
IET Image Processing, 2021 This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global ...
Hu Zhu +5 more
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Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection [PDF]
, 2012 Chandrasekaran, Parrilo and Willsky (2010) proposed a convex optimization problem to characterize graphical model selection in the presence of unobserved variables.
Ma, Shiqian, Xue, Lingzhou, Zou, Hui
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Results in Control and Optimization, 2021 The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems. In this paper, we extend the ADMM naturally to nonlinear equality-constrained problems, called neADMM.
Junxiang Wang, Liang Zhao
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Alternating direction method of multipliers for penalized zero-variance discriminant analysis [PDF]
, 2015 We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations.
Ames, Brendan +2 more
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