Until now, distributed acoustic beamforming has focused on optimizing for a beamformer
over an entire network, with each node contributing to the beamformer output. We present a …

In recent years, signal processing with ad hoc microphone arrays has attracted a lot of
attention. Speech enhancement in noisy, interfered, and reverberant environments is one of …

Multi-instance Learning (MIL) is a popular learning paradigm arising from many real
applications. It assigns a label to a set of instances, which is called a bag, and the bag's …

In this article, a centralized two-block separable convex optimization with equality constraint
and its extension to multi-block optimization are considered. The first fully parallel primal …

We propose two algorithms based on the Primal-Dual Method of Multipliers (PDMM) to be
used in distributed network optimization: Function Split PDMM (FS-PDMM) and …

In this paper, a centralized two-block separable convex optimization with equality constraint
is considered. The first fully parallel primal-dual discrete-time algorithm called Parallel …

In this paper, we analyze the convergence rate of the bi-alternating direction method of
multipliers (BiADMM). Differently from ADMM that optimizes an augmented Lagrangian …

Large-scale networks of computing units, often characterised by the absence of central
control, have become commonplace in many applications. To facilitate data processing in …

This paper proposes a spatio-temporal model (VCGA) based on variational mode
decomposition (VMD) and attention mechanism. The proposed prediction model combines a …

In this paper, we propose two novel Alternating Direction Method of Multipliers (ADMM)
algorithms for the sparse portfolio problem via sorted ℓ 1-norm penalization (SLOPE). The …