This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

Natural image statistics indicate that we should use nonconvex norms for most
regularization tasks in image processing and computer vision. Still, they are rarely used in …

In this work, we consider a class of differentiable criteria for sparse image computing
problems, where a nonconvex regularization is applied to an arbitrary linear transform of the …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

We describe a method for detecting, locating and quantifying sources of gas emissions to
the atmosphere using remotely obtained gas concentration data; the method is applicable to …

Stochastic approximation techniques play an important role in solving many problems
encountered in machine learning or adaptive signal processing. In these contexts, the …

We introduce the class of SO-friendly neural networks, which include several models used in
practice including networks with 2 layers of hidden weights where the number of inputs is …

Variational Bayesian approximations have been widely used in fully Bayesian inference for
approximating an intractable posterior distribution by a separable one. Nevertheless, the …

In this work, we propose an asynchronous Majorization-Minimization (MM) algorithm for
solving large scale differentiable non-convex optimization problems. The proposed …

Complex-valued data are encountered in many application areas of signal and image
processing. In the context of the optimization of functions of real variables, subspace …