A survey on some recent developments of alternating direction method of multipliers
DR Han - Journal of the Operations Research Society of China, 2022 - Springer
Recently, alternating direction method of multipliers (ADMM) attracts much attentions from
various fields and there are many variant versions tailored for different models. Moreover, its …
various fields and there are many variant versions tailored for different models. Moreover, its …
[HTML][HTML] Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model
As remote sensing (RS) data obtained from different sensors become available largely and
openly, multimodal data processing and analysis techniques have been garnering …
openly, multimodal data processing and analysis techniques have been garnering …
Learning tensor low-rank representation for hyperspectral anomaly detection
Recently, low-rank representation (LRR) methods have been widely applied for
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
Global convergence of ADMM in nonconvex nonsmooth optimization
In this paper, we analyze the convergence of the alternating direction method of multipliers
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …
The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …
methods result in misleading results and waste of computing resources due to lack of timely …
Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems
The alternating direction method of multipliers (ADMM) is widely used to solve large-scale
linearly constrained optimization problems, convex or nonconvex, in many engineering …
linearly constrained optimization problems, convex or nonconvex, in many engineering …
A three-operator splitting scheme and its optimization applications
Operator-splitting methods convert optimization and inclusion problems into fixed-point
equations; when applied to convex optimization and monotone inclusion problems, the …
equations; when applied to convex optimization and monotone inclusion problems, the …
On the linear convergence of the alternating direction method of multipliers
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 …
for minimizing the sum of two or more nonsmooth convex separable functions subject to …
Parallel Multi-Block ADMM with o(1 / k) Convergence
This paper introduces a parallel and distributed algorithm for solving the following
minimization problem with linear constraints: minimize~~ &f_1 (x _1)+ ⋯+ f_N (x _N)\subject …
minimization problem with linear constraints: minimize~~ &f_1 (x _1)+ ⋯+ f_N (x _N)\subject …
Guaranteed tensor recovery fused low-rankness and smoothness
Tensor recovery is a fundamental problem in tensor research field. It generally requires to
explore intrinsic prior structures underlying tensor data, and formulate them as certain forms …
explore intrinsic prior structures underlying tensor data, and formulate them as certain forms …