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Finite-Dimensional Variational Inequalities and Complementarity Problems
TLDR
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.Abstract:
Newton Methods for Nonsmooth Equations.- Global Methods for Nonsmooth Equations.- Equation-Based Algorithms for Complementarity Problems.- Algorithms for Variational Inequalities.- Interior and Smoothing Methods.- Methods for Monotone Problems.- Notes and comments.read more
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Amir Beck,Marc Teboulle +1 more
TL;DR: A new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global rate of convergence which is proven to be significantly better, both theoretically and practically.
Book
Proximal Algorithms
Neal Parikh,Stephen Boyd +1 more
TL;DR: The many different interpretations of proximal operators and algorithms are discussed, their connections to many other topics in optimization and applied mathematics are described, some popular algorithms are surveyed, and a large number of examples of proxiesimal operators that commonly arise in practice are provided.
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Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
Amir Beck,Marc Teboulle +1 more
TL;DR: A fast algorithm is derived for the constrained TV-based image deblurring problem with box constraints by combining an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA).
Journal ArticleDOI
Constrained Consensus and Optimization in Multi-Agent Networks
TL;DR: In this article, the authors present a distributed algorithm that can be used by multiple agents to align their estimates with a particular value over a network with time-varying connectivity.
Journal ArticleDOI
A coordinate gradient descent method for nonsmooth separable minimization
Paul Tseng,Sangwoon Yun +1 more
TL;DR: A (block) coordinate gradient descent method for solving this class of nonsmooth separable problems and establishes global convergence and, under a local Lipschitzian error bound assumption, linear convergence for this method.