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A Monotone+Skew Splitting Model for Composite Monotone Inclusions in Duality

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TLDR
In this paper, a primal-dual splitting algorithm for composite monotone inclusions is proposed. But it is based on the assumption that the duals of a maximally-monotone operator and a linear skew-adjoint operator can be decomposed in a fully decomposed fashion.
Abstract
The principle underlying this paper is the basic observation that the problem of simultaneously solving a large class of composite monotone inclusions and their duals can be reduced to that of finding a zero of the sum of a maximally monotone operator and a linear skew-adjoint operator. An algorithmic framework is developed for solving this generic problem in a Hilbert space setting. New primal-dual splitting algorithms are derived from this framework for inclusions involving composite monotone operators, and convergence results are established. These algorithms draw their simplicity and efficacy from the fact that they operate in a fully decomposed fashion in the sense that the monotone operators and the linear transformations involved are activated separately at each iteration. Comparisons with existing methods are made and applications to composite variational problems are demonstrated.

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Citations
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Journal ArticleDOI

A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms

TL;DR: This work brings together and notably extends several classical splitting schemes, like the forward–backward and Douglas–Rachford methods, as well as the recent primal–dual method of Chambolle and Pock designed for problems with linear composite terms.
Journal ArticleDOI

A splitting algorithm for dual monotone inclusions involving cocoercive operators

TL;DR: This work considers the problem of solving dual monotone inclusions involving sums of composite parallel-sum type operators and exploits explicitly the properties of the cocoercive operators appearing in the model.
Journal ArticleDOI

An introduction to continuous optimization for imaging

TL;DR: The state of the art in continuous optimization methods for such problems, and particular emphasis on optimal first-order schemes that can deal with typical non-smooth and large-scale objective functions used in imaging problems are described.
Journal ArticleDOI

Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators

TL;DR: A primal-dual splitting algorithm for solving monotone inclusions involving a mixture of sums, linear compositions, and parallel sums of set-valued and Lipschitzian operators was proposed in this paper.
Journal ArticleDOI

A Generalized Forward-Backward Splitting

TL;DR: This paper introduces the generalized forward-backward splitting algorithm for minimizing convex functions of the form F + G_i, and proves its convergence in infinite dimension, and its robustness to errors on the computation of the proximity operators and of the gradient of $F$.
References
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Book

Finite-Dimensional Variational Inequalities and Complementarity Problems

TL;DR: Newton Methods for Nonsmooth Equations as mentioned in this paper and global methods for nonsmooth equations were used to solve the Complementarity problem in the context of non-complementarity problems.
Book

Convex Analysis and Monotone Operator Theory in Hilbert Spaces

TL;DR: This book provides a largely self-contained account of the main results of convex analysis and optimization in Hilbert space, and a concise exposition of related constructive fixed point theory that allows for a wide range of algorithms to construct solutions to problems in optimization, equilibrium theory, monotone inclusions, variational inequalities, and convex feasibility.
Journal ArticleDOI

Monotone Operators and the Proximal Point Algorithm

TL;DR: In this paper, the proximal point algorithm in exact form is investigated in a more general form where the requirement for exact minimization at each iteration is weakened, and the subdifferential $\partial f$ is replaced by an arbitrary maximal monotone operator T.
Journal ArticleDOI

On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators

TL;DR: This paper shows, by means of an operator called asplitting operator, that the Douglas—Rachford splitting method for finding a zero of the sum of two monotone operators is a special case of the proximal point algorithm, which allows the unification and generalization of a variety of convex programming algorithms.
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