Consensus optimization has received considerable attention in recent years. A number of
decentralized algorithms have been proposed for convex consensus optimization. However …

We consider the problem of estimating a vector from its noisy measurements using a prior
specified only through a denoising function. Recent work on plug-and-play priors (PnP) and …

Nonconvex optimization is central in solving many machine learning problems, in which
block-wise structure is commonly encountered. In this work, we propose cyclic block …

Asynchronous-parallel algorithms have the potential to vastly speed up algorithms by
eliminating costly synchronization. However, our understanding of these algorithms is …

We propose a general framework to accelerate significantly the algorithms for nonnegative
matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to …

Primal–dual hybrid gradient (PDHG) and alternating direction method of multipliers (ADMM)
are popular first-order optimization methods. They are easy to implement and have diverse …

Exploiting partial first-order information in a cyclic way is arguably the most natural strategy
to obtain scalable first-order methods. However, despite their wide use in practice, cyclic …

The Krasnosel'skiı–Mann (KM) iterative method has extensively been employed to find fixed
points of nonlinear mappings (in particular, nonexpansive mappings) and solve convex …

Density-functional theory (DFT) has revolutionized computer simulations in chemistry and
material science. A faithful implementation of the theory requires self-consistent calculations …

We introduce two algorithms for nonconvex regularized finite sum minimization, where
typical Lipschitz differentiability assumptions are relaxed to the notion of relative …