Publications related to Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

We consider design of sparse controllers for a stochastic linear system with infinite horizon quadratic objective. We formulate the non-sparse optimal solution through a semidefinite program for the second order moments of the states and inputs. Given that ...

University of Minnesota2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Comparing genomes with rearrangements and segmental duplications

Bernard Moret, Mingfu Shao

Motivation: Large-scale evolutionary events such as genomic rearrange. ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational pro ...

Oxford University Press2015

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 Gamma-convergence of discrete L-1-Potts functionals toward their continuous counterpart and obtain a convergence statement for the corresponding minimi ...

Siam Publications2015

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing framework in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on multiple unknown (but sparse) signals and formulate the ...

Institute of Electrical and Electronics Engineers2014

Sensing the real world

Juri Ranieri

A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phe ...

EPFL2014

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

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Michaël Unser, Emrah Bostan, Stamatios Lefkimmiatis, Emmanuel Augustin Julien Froustey

We develop a variational algorithm for reconstructing phase objects from a series of bright field micrographs. Our mathematical model is based on the transport-of-intensity equation (TIE), which links the phase of a complex field to the axial derivative of ...

Ieee2014

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Michaël Unser, Emrah Bostan, Stamatios Lefkimmiatis, Emmanuel Augustin Julien Froustey

We develop a variational algorithm for reconstructing phase objects from a series of bright field micrographs. Our mathematical model is based on the transport-of-intensity equation (TIE), which links the phase of a complex field to the axial derivative of ...

IEEE2014

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

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‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso

Comparing genomes with rearrangements and segmental duplications

The L-1-Potts Functional For Robust Jump-Sparse Reconstruction

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Sensing the real world

PURIFY: a new approach to radio-interferometric imaging

Jump-Sparse and Sparse Recovery Using Potts Functionals

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Jump-Sparse and Sparse Recovery Using Potts Functionals

Comparing genomes with rearrangements and segmental duplications

Estimation Error of the Constrained Lasso

Sensing the real world

The L-1-Potts Functional For Robust Jump-Sparse Reconstruction

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

PURIFY: a new approach to radio-interferometric imaging