Results 21 to 30 of about 166,468,241 (292)
Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier [PDF]
We propose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized ...
Ni, Ruiqi, Gao, Xifeng, Pan, Zherong
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Alternating Direction Method of Multipliers for Quantization
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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Solving Robbin's problem [PDF]
In this report the numerical integration of ellipticpartial differential equations under Robbin's boundaryconditions is attempted by means of the Extrapolatedform of the Alternating Direction Implicit methods.A set of varying extrapolation parameters is ...
Lordanidis, KI
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Modelling of Distributed Energy Resources Management in Microgird using Distributed Algorithm [PDF]
The smart energy management system as a powerful tool is implemented to manage both demands and generation units. The energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem.
Ghasem Mirbabaee +2 more
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A Variational Model for Removing Concentric Elliptical Artifacts from CT Images
In computed tomography (CT) imaging, artifacts will degrade the quality of reconstructed images. To solve this issue, in this paper we propose a method to remove concentric elliptical artifacts from CT images.
Qiaoxin LI, Ke JIN, Zhifeng PANG
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A Fast Symmetric Alternating Direction Method of Multipliers
Summary: In recent years, alternating direction method of multipliers (ADMM) and its variants are popular for the extensive use in image processing and statistical learning. A variant of ADMM: symmetric ADMM, which updates the Lagrange multiplier twice in one iteration, is always faster whenever it converges.
Luo, Gang, Yang, Qingzhi
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Radar Forward-looking Super-resolution Imaging Method Based on Sparse and Low-rank Priors
Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking ...
Junkui TANG +4 more
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Bregman Alternating Direction Method of Multipliers
The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alternating direction method of multipliers (ADMM) to Bregman ADMM (BADMM), which allows the choice of different Bregman divergences to exploit the structure of problems.
Huahua Wang, Arindam Banerjee 0001
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Analysis of the Alternating Direction Method of Multipliers for Nonconvex Problems [PDF]
This work investigates the theoretical performance of the alternating-direction method of multipliers (ADMM) as it applies to nonconvex optimization problems, and in particular, problems with nonconvex constraint sets. The alternating direction method of multipliers is an optimization method that has largely been analyzed for convex problems.
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Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification
In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification.
Yachao Zhang +4 more
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