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 …

The alternating direction method of multipliers (ADMM) is a popular approach for solving
optimization problems that are potentially non-smooth and with hard constraints. It has been …

We propose new methods to speed up convergence of the Alternating Direction Method of
Multipliers (ADMM), a common optimization tool in the context of large scale and distributed …

Blockchain shows a huge prospective in the coming future. It is atechnology that provides
the possibility of generating and sharing transaction ledgers that are tamper proof. Use …

In this work, we introduce the graph regularized autoencoder. We propose three variants.
The first one is the unsupervised version. The second one is tailored for clustering, by …

An efficient strategy for weakly-supervised segmentation is to impose constraints or
regularization priors on target regions. Recent efforts have focused on incorporating such …

The simplicity of gradient descent (GD) made it the default method for training ever-deeper
and complex neural networks. Both loss functions and architectures are often explicitly tuned …

Due to environmental concerns of rising energy consumption caused by explosive growth in
the demands of wireless multimedia services, energy efficiency has become an important …

Skeleton-based action recognition has attracted increasing attention due to its strong
adaptability to dynamic circumstances and potential for broad applications such as …

The alternating direction method of multipliers (ADMM) is a common optimization tool for
solving constrained and non-differentiable problems. We provide an empirical study of the …