This paper gives a unified convergence analysis of additive Schwarz methods for general
convex optimization problems. Resembling the fact that additive Schwarz methods for linear …

Total variation minimization is standard in mathematical imaging and there have been
numerous researches over the last decades. In order to process large-scale images in real …

We propose a novel dual‐primal finite element tearing and interconnecting method for
nonlinear variational inequalities. The proposed method is based on a particular Fenchel …

Based on an observation that additive Schwarz methods for general convex optimization
can be interpreted as gradient methods, we propose an acceleration scheme for additive …

This paper presents the domain decomposition methods (DDMs) for achieving fast parallel
computing on multi-core computers when dealing with the multi-phase labeling problem. To …

We consider sequential and parallel decomposition methods for a dual problem of a general
total variation minimization problem with applications in several image processing tasks, like …

Abstract Domain decomposition is one of the most efficient techniques to derive efficient
methods for large-scale problems. In this chapter such decomposition methods for the …

In this paper, we propose a novel overlapping domain decomposition method that can be
applied to various problems in variational imaging such as total variation minimization. Most …