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

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Alireza Karimi, Achille Nicoletti, Zlatko Emedi

In this paper, a new method for designing robust fixed-order

\mathcal{H}_{\infty}

discrete-time controllers is presented. The controller structure is a two-degree of freedom polynomial controller of the

RST

type. A data-driven approach is implemented f ...

IEEE2015

Robust Smith Predictor Design for Time-Delay Systems with H∞ Performance

Alireza Karimi, Achille Nicoletti, Vinicius De Oliveira

A new method for robust fixed-order H∞ controller design for uncertain time-delay systems is presented. It is shown that the H∞ robust performance condition can be represented by a set of convex constraints with respect to the parameters of a linearly para ...

Springer2015

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

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

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 heretofor ...

2015

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Jean-Philippe Thiran, Yves Wiaux, Erick Jorge Canales Rodriguez, Alessandro Daducci, Anna Auria Rasclosa, Tim Bjørn Dyrby

Mapping the local microstructure properties of the tissue in the brain is crucial to understand any pathological condition from a biological perspective. In recent years, microstructure-imaging techniques aimed at extracting such precious information by us ...

2015

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

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If the acquired data is to be sent to a far-away base station, collaborative beamforming performed by the sensors may help to distribute the communication load amon ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Convex Optimization for Big Data

Volkan Cevher

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniqu ...

Institute of Electrical and Electronics Engineers2014

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Anastasios Kyrillidis

Many natural and man-made signals can be described as having a few degrees of freedom relative to their size due to natural parameterizations or constraints; examples include bandlimited signals, collections of signals observed from multiple viewpoints in ...

EPFL2014

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

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014

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

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A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Robust Smith Predictor Design for Time-Delay Systems with H∞ Performance

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

PURIFY: a new approach to radio-interferometric imaging

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Convex Optimization for Big Data

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

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

Robust Smith Predictor Design for Time-Delay Systems with H∞ Performance

PURIFY: a new approach to radio-interferometric imaging

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Convex Optimization for Big Data

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

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Composite convex minimization involving self-concordant-like cost functions

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Composite convex minimization involving self-concordant-like cost functions