Publications related to On Matching Pursuit and Coordinate Descent

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Michaël Unser, Demetri Psaltis, Ulugbek Kamilov, Alexandre Goy, Joowon Lim

Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally non-convex, the solution it obt ...

Ieee2017

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

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dominique Bonvin, René Uwe Schneider, Predrag Milosavljevic

The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...

2016

Structure Tensor Total Variation

Michaël Unser, Stamatios Lefkimmiatis

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

SIAM2015

On Convergence Of The Maximum Block Improvement Method

André Uschmajew

The MBI (maximum block improvement) method is a greedy approach to solving optimization problems where the decision variables can be grouped into a finite number of blocks. Assuming that optimizing over one block of variables while fixing all others is rel ...

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

Geometric Variational Models for Inverse Problems in Imaging

Virginia Estellers Casas

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

DynaProg for Scala

Thierry Coppey

Dynamic programming is an algorithmic technique to solve problems that follow the Bellman’s principle: optimal solutions depends on optimal sub-problem solutions. The core idea behind dynamic programming is to memoize intermediate results into matrices to ...

2013

Coordination and Sampling in Distributed Constraint Optimization

Brammert Ottens

The Distributed Constraint Optimization (DCOP) framework can be used to model a wide range of optimization problems that are inherently distributed. A distributed optimization problem can be viewed as a problem distributed over a set of agents, where agent ...

EPFL2012

A survey and comparison of contemporary algorithms for computing the matrix geometric mean

In this paper we present a survey of various algorithms for computing matrix geometric means and derive new second-order optimization algorithms to compute the Karcher mean. These new algorithms are constructed using the standard definition of the Riemanni ...

2012

On Matching Pursuit and Coordinate Descent

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Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

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

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Structure Tensor Total Variation

On Convergence Of The Maximum Block Improvement Method

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Geometric Variational Models for Inverse Problems in Imaging

DynaProg for Scala

Coordination and Sampling in Distributed Constraint Optimization

A survey and comparison of contemporary algorithms for computing the matrix geometric mean

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

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

DynaProg for Scala

On Convergence Of The Maximum Block Improvement Method

A survey and comparison of contemporary algorithms for computing the matrix geometric mean

Structure Tensor Total Variation

Coordination and Sampling in Distributed Constraint Optimization

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Geometric Variational Models for Inverse Problems in Imaging