Publications related to Efficient Greedy Coordinate Descent for Composite Problems

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coord ...

2017

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

PURIFY: a new approach to radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a m ...

Wiley-Blackwell2014

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Alireza Karimi

Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input-output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, ...

Pergamon-Elsevier Science Ltd2014

An Equivalence between the Lasso and Support Vector Machines

Martin Jaggi

We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are eq ...

Chapman and Hall/CRC2014

On L2-Regularization for Virtual Reference Feedback Tuning

Alireza Karimi

The Virtual Reference Feedback Tuning (VRFT) approach is a design method that allow optimal feedback control laws to be derived from input-output (I/O) data only, without need of a model of the process. A drawback of this methods is that, in its standard f ...

2013

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupt ...

Ieee-Inst Electrical Electronics Engineers Inc2013

An Iterative Linear Expansion of Thresholds for l1-based Image Restoration

Thierry Blu, Hanjie Pan

This paper proposes a novel algorithmic framework to solve image restoration problems under sparsity assumptions. As usual, the reconstructed image is the minimum of an objective functional that consists of a data fidelity term and an l1 regularization. Ho ...

Institute of Electrical and Electronics Engineers2013

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...

Institute of Electrical and Electronics Engineers2012

Efficient Greedy Coordinate Descent for Composite Problems

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Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

PURIFY: a new approach to radio-interferometric imaging

Enhancing statistical performance of data-driven controller tuning via L2-regularization

An Equivalence between the Lasso and Support Vector Machines

On L2-Regularization for Virtual Reference Feedback Tuning

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

An Iterative Linear Expansion of Thresholds for l1-based Image Restoration

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

PURIFY: a new approach to radio-interferometric imaging

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

On L2-Regularization for Virtual Reference Feedback Tuning

Enhancing statistical performance of data-driven controller tuning via L2-regularization

An Equivalence between the Lasso and Support Vector Machines

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

An Iterative Linear Expansion of Thresholds for l1-based Image Restoration

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization