737 research outputs found
First order algorithms in variational image processing
Variational methods in imaging are nowadays developing towards a quite
universal and flexible tool, allowing for highly successful approaches on tasks
like denoising, deblurring, inpainting, segmentation, super-resolution,
disparity, and optical flow estimation. The overall structure of such
approaches is of the form ; where the functional is a data fidelity term also
depending on some input data and measuring the deviation of from such
and is a regularization functional. Moreover is a (often linear)
forward operator modeling the dependence of data on an underlying image, and
is a positive regularization parameter. While is often
smooth and (strictly) convex, the current practice almost exclusively uses
nonsmooth regularization functionals. The majority of successful techniques is
using nonsmooth and convex functionals like the total variation and
generalizations thereof or -norms of coefficients arising from scalar
products with some frame system. The efficient solution of such variational
problems in imaging demands for appropriate algorithms. Taking into account the
specific structure as a sum of two very different terms to be minimized,
splitting algorithms are a quite canonical choice. Consequently this field has
revived the interest in techniques like operator splittings or augmented
Lagrangians. Here we shall provide an overview of methods currently developed
and recent results as well as some computational studies providing a comparison
of different methods and also illustrating their success in applications.Comment: 60 pages, 33 figure
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
The present article presents a summarizing view at differential-algebraic
equations (DAEs) and analyzes how new application fields and corresponding
mathematical models lead to innovations both in theory and in numerical
analysis for this problem class. Recent numerical methods for nonsmooth
dynamical systems subject to unilateral contact and friction illustrate the
topicality of this development.Comment: Preprint of Book Chapte
Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems -- Survey
This paper is a survey of methods for solving smooth (strongly) monotone
stochastic variational inequalities. To begin with, we give the deterministic
foundation from which the stochastic methods eventually evolved. Then we review
methods for the general stochastic formulation, and look at the finite sum
setup. The last parts of the paper are devoted to various recent (not
necessarily stochastic) advances in algorithms for variational inequalities.Comment: 12 page
Solving variational inequalities with Stochastic Mirror-Prox algorithm
In this paper we consider iterative methods for stochastic variational
inequalities (s.v.i.) with monotone operators. Our basic assumption is that the
operator possesses both smooth and nonsmooth components. Further, only noisy
observations of the problem data are available. We develop a novel Stochastic
Mirror-Prox (SMP) algorithm for solving s.v.i. and show that with the
convenient stepsize strategy it attains the optimal rates of convergence with
respect to the problem parameters. We apply the SMP algorithm to Stochastic
composite minimization and describe particular applications to Stochastic
Semidefinite Feasability problem and Eigenvalue minimization
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