Results 31 to 40 of about 16,083 (234)
Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014 In this paper, we analyze the behavior of the alternating direction method of multipliers (ADMM), for solving a family of nonconvex problems. Our focus is given to the well-known consensus and sharing problems, both of which have wide applications in ...
Mingyi Hong, Z. Luo, Meisam Razaviyayn
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Distributed Convex Optimisation using the Alternating Direction Method of Multipliers (ADMM) in Lossy Scenarios [PDF]
, 2022 The Alternating Direction Method of Multipliers (ADMM) is an extensively studied algorithm suitable for solving convex distributed optimisation problems.
Bastianello, Nicola
core
On the Convergence of Bregman ADMM With Variational Inequality
IEEE Access, 2020 The alternating direction method of multipliers (ADMM) is one of most foundational algorithms for linear constrained composite minimization problems. For different specific problems, variations of ADMM (like linearized ADMM, proximal ADMM) are developed.
Peibing Du, Hao Jiang
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Inertial alternating direction method of multipliers for non-convex non-smooth optimization [PDF]
Computational optimization and applications, 2021 In this paper, we propose an algorithmic framework, dubbed inertial alternating direction methods of multipliers (iADMM), for solving a class of nonconvex nonsmooth multiblock composite optimization problems with linear constraints. Our framework employs
L. Hien, D. Phan, Nicolas Gillis
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Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving [PDF]
IEEE transactions on intelligent transportation systems (Print), 2020 In the context of autonomous driving, the iterative linear quadratic regulator (iLQR) is known to be an efficient approach to deal with the nonlinear vehicle model in motion planning problems.
Jun Ma +4 more
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Linear Convergence Rate of Splitting Algorithms for Multi-Block Constrained Convex Minimizations
IEEE Access, 2020 Multi-block linear constrained separable convex minimizations are ubiquitous and have been drawing increasing attention in recent researches. The alternating direction method of multipliers (ADMM) has been well studied and used in the literature for the ...
Xiaoge Deng, Feng Liu, Feng Huang
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Alternating direction method of multipliers for the extended trust region subproblem [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2017 The extended trust region subproblem has been the focus of several research recently. Under various assumptions, strong duality and certain SOCP/SDP relaxations have been proposed for several classes of it.
Maziar Salahi, Akram Taati
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Journal of Inequalities and Applications, 2016 In this paper, we show that when the alternating direction method of multipliers (ADMM) is extended directly to the 3-block separable convex minimization problems, it is convergent if one block in the objective possesses sub-strong monotonicity which is ...
Huijie Sun, Jinjiang Wang, Tingquan Deng
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Sensors, 2023 In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm.
Ding Dong +4 more
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Dual Alternating Direction Method of Multipliers for Inverse Imaging
IEEE Transactions on Image Processing, 2022 Inverse imaging covers a wide range of imaging applications, including super-resolution, deblurring, and compressive sensing. We propose a novel scheme to solve such problems by combining duality and the alternating direction method of multipliers (ADMM).
Li Song, Zhou Ge, E. Lam
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