Publications associées à Matrix Recipes for Hard Thresholding Methods

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Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Matrix Recipes for Hard Thresholding Methods

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Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

Coordinate-Descent Diffusion Learning by Networked Agents

The complexity of error metrics

Coordinate-descent adaptation over networks

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

A Comment on “Computational Complexity of Stochastic Programming Problems”

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

On solving LPN using BKW and variants Implementation and Analysis

Tight Bounds for Asynchronous Renaming

Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

Coordinate-descent adaptation over networks

Tight Bounds for Asynchronous Renaming

A Comment on “Computational Complexity of Stochastic Programming Problems”

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Coordinate-Descent Diffusion Learning by Networked Agents

The complexity of error metrics

On solving LPN using BKW and variants Implementation and Analysis