Results 241 to 250 of about 27,846 (265)

A Riemannian Alternating Direction Method of Multipliers

Mathematics of Operations Research
We consider a class of Riemannian optimization problems where the objective is the sum of a smooth function and a nonsmooth function considered in the ambient space. This class of problems finds important applications in machine learning and statistics, such as sparse principal component analysis, sparse spectral clustering, and orthogonal dictionary ...
Jiaxiang Li, Shiqian Ma, Tejes Srivastava   +2 more
openaire   +1 more source

Alternating direction method of multipliers with difference of convex functions

Advances in Computational Mathematics, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tao Sun 0005, Penghang Yin, Lizhi Cheng, Hao Jiang 0001   +3 more
openaire   +2 more sources

Stochastic alternating direction method of multipliers

2015
The alternating direction method of multipliers (ADMM) is an efficient optimization solver for a wide variety of machine learning models. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to the SAG-ADMM and SDCA-ADMM algorithms that have fast convergence rates and low iteration complexities.
openaire   +2 more sources

Efficient JPEG decompression by the alternating direction method of multipliers

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Standard decompression of JPEG images produces artifacts along edges and a disturbing checkerboard pattern. To reduce these artifacts, decompression can be formulated as an image reconstruction problem within Bayesian maximum a posteriori probability framework. In this type of problem, the prior information about an image is typically given by the l 1
Michal Sorel, Michal Bartos
openaire   +1 more source

Stochastic Alternating Direction Method of Multipliers with Conjugate Gradient

2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), 2018
The Alternating Direction Method of Multipliers(ADMM) is an important method for machine learning. A large number of stochastic versions of ADMM continue to emerge. But almost all the algorithm focused on the Steepest Descent, which cause slow convergence rate.
Mingyuan Ma, Dongyang Zhao
openaire   +1 more source

Decentralizing Consensus-Alternating Direction Method of Multipliers

2023 European Control Conference (ECC), 2023
Chinmay Routray, Soumya Ranjan Sahoo
openaire   +1 more source

Alternating direction method of multipliers for nonconvex log total variation image restoration

Applied Mathematical Modelling, 2023
Guopu Zhu, Zhibin Zhu, Sam Kwong
exaly  

Emulation Alternating Direction Method of Multipliers

2022 Eighth Indian Control Conference (ICC), 2022
Chinmay Routray, Soumya Ranjan Sahoo
openaire   +1 more source

Acoustic tomography of temperature and velocity fields by using the radial basis function and alternating direction method of multipliers

International Journal of Heat and Mass Transfer, 2022
Juqi Zhang, Hong Qi, Ya-Tao Ren
exaly  

An alternating direction method of multipliers for solving user equilibrium problem

European Journal of Operational Research, 2023
Xinyuan Chen
exaly