In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by
construction averaged operators. For filters with full length, we propose a stochastic gradient …

In this work, we introduce a novel stochastic proximal alternating linearized minimization
algorithm [J. Bolte, S. Sabach, and M. Teboulle, Math. Program., 146 (2014), pp. 459--494] …

Despite the rapid development of computational hardware, the treatment of large and high
dimensional data sets is still a challenging problem. This paper provides a twofold …

How can we efficiently find very large numbers of clusters C in very large datasets N of
potentially high dimensionality D? Here we address the question by using a novel …

The stochastic gradient descent (SGD) method has been applied to the tensor
CANDECOMP/PARAFAC (CP) decomposition problem to reduce the computational cost …

In this paper, for solving a broad class of large-scale nonconvex and nonsmooth
optimization problems, we propose a stochastic two-step inertial Bregman proximal …

We introduce SPRING, a novel stochastic proximal alternating linearized minimization
algorithm for solving a class of non-smooth and non-convex optimization problems. Large …

Normalizing flows are a powerful tool for generative modelling, density estimation and
posterior reconstruction in Bayesian inverse problems. In this paper, we introduce proximal …

This paper focuses on multi-block optimization problems over transport polytopes, which
underlie various applications including strongly correlated quantum physics and machine …

The majority of classic tensor CP decomposition models are designed for squared loss,
employing Euclidean distance as a local proximal term. However, the Euclidean distance is …