Publications related to An Optimal Algorithm for Reconstructing Images from Binary Measurements

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the con ...

EPFL2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H-infinity performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controller ...

IEEE2018

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci

In this study, we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast ...

2018

Proximity Operators of Discrete Information Divergences

Mireille El Gheche, Giovanni Chierchia

While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the p ...

Elsevier2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H ∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the ...

2018

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

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Volkan Cevher, Quoc Tran Dinh, Alp Yurtsever, Yen-Huan Li, Ya-Ping Hsieh, Marwa El Halabi, Gergely Odor

We study a phase retrieval problem in the Poisson noise model. Motivated by the PhaseLift approach, we approximate the maximum-likelihood estimator by solving a convex program with a nuclear norm constraint. While the Frank-Wolfe algorithm, together with t ...

Ieee2016

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Volkan Cevher

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease stati ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

Abstract. The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has ...

2015

An Optimal Algorithm for Reconstructing Images from Binary Measurements

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Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Proximity Operators of Discrete Information Divergences

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

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

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Composite convex minimization involving self-concordant-like cost functions

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Proximity Operators of Discrete Information Divergences

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

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

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Composite convex minimization involving self-concordant-like cost functions