Publications associées à Combinatorial Penalties: Which structures are preserved by convex relaxations?

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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

Combinatorial Penalties: Which structures are preserved by convex relaxations?

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Designing Gabor windows using convex optimization

A Data-Driven Approach to Power Converter Control via Convex Optimization

Yield prediction in parallel homogeneous assembly

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

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Learning with tensors: a framework based on convex optimization and spectral regularization

Convex Optimization for Big Data

Convex computation of the region of attraction of polynomial control systems

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

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

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Yield prediction in parallel homogeneous assembly

A Data-Driven Approach to Power Converter Control via Convex Optimization

Convex Optimization for Big Data

Convex computation of the region of attraction of polynomial control systems

Designing Gabor windows using convex optimization

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

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Learning with tensors: a framework based on convex optimization and spectral regularization

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