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Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

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The $ L ^{ 1 } $ -Potts Functional for Robust Jump-Sparse Reconstruction

Martin Kurt Storath

We investigate the nonsmooth and nonconvex

L ^{ 1 }

-Potts functional in discrete and continuous time. We show Γ-convergence of discrete

L ^{ 1 }

-Potts functionals toward their continuous counterpart and obtain a convergence statement for the corr ...

SIAM2015

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Graph Chatbot

Chattez avec Graph Search

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

From Acoustic Room Reconstruction to SLAM

The $ L ^{ 1 } $ -Potts Functional for Robust Jump-Sparse Reconstruction

Path-following gradient-based decomposition algorithms for separable convex optimization

Complexity Certification of the Fast Alternating Minimization Algorithm for Linear Model Predictive Control

Splitting methods in control

Learning of Closed-Loop Motion Control

Image reconstruction of non-planar scenes from compressed multi-view measurements

Dynamically Selecting Composition Algorithms for Economical Composition as a Service

Online-Batch Strongly Convex Multi Kernel Learning

The $ L ^{ 1 } $ -Potts Functional for Robust Jump-Sparse Reconstruction

Complexity Certification of the Fast Alternating Minimization Algorithm for Linear Model Predictive Control

From Acoustic Room Reconstruction to SLAM

Online-Batch Strongly Convex Multi Kernel Learning

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Path-following gradient-based decomposition algorithms for separable convex optimization

Image reconstruction of non-planar scenes from compressed multi-view measurements

Splitting methods in control

Learning of Closed-Loop Motion Control

Dynamically Selecting Composition Algorithms for Economical Composition as a Service