Demosaicking and denoising are among the most crucial steps of modern digital camera
pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two …

The {\L} ojasiewicz inequality shows that H\" olderian error bounds on the minimum of
convex optimization problems hold almost generically. Here, we clarify results of\citet …

We analyze the convergence of (stochastic) gradient descent algorithm for learning a
convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does …

Modern digital cameras rely on the sequential execution of separate image processing steps
to produce realistic images. The first two steps are usually related to denoising and …

First-order primal-dual methods are appealing for their low memory overhead, fast iterations,
and effective parallelization. However, they are often slow at finding high accuracy solutions …

We consider the setting of distributed empirical risk minimization where multiple machines
compute the gradients in parallel and a centralized server updates the model parameters. In …

In this paper, an inexact proximal-point penalty method is studied for constrained
optimization problems, where the objective function is non-convex, and the constraint …

We consider a combined restarting and adaptive backtracking strategy for the popular fast
iterative shrinking-thresholding algorithm (FISTA) frequently employed for accelerating the …

We propose generic acceleration schemes for a wide class of optimization and iterative
schemes based on relaxation and inertia. In particular, we introduce methods that …

Making the gradients small is a fundamental optimization problem that has eluded unifying
and simple convergence arguments in first-order optimization, so far primarily reserved for …