Publications associées à Monopulse Beam Synthesis Using a Sparse Single Layer of Weights

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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

Monopulse Beam Synthesis Using a Sparse Single Layer of Weights

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Global Identification of Kinetic Parameters via the Extent-based Incremental Approach

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

Multilinear Low-Rank Tensors on Graphs & Applications

Fast Robust PCA on Graphs

A new mathematical representation of demand using choice-based optimization method

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

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

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Dictionary learning over large distributed models via dual-ADMM strategies

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

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

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

Global Identification of Kinetic Parameters via the Extent-based Incremental Approach

Multilinear Low-Rank Tensors on Graphs & Applications

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Fast Robust PCA on Graphs

A new mathematical representation of demand using choice-based optimization method

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Dictionary learning over large distributed models via dual-ADMM strategies