Publications related to Scalable sparse covariance estimation via self-concordance

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

Barrier Smoothing for Nonsmooth Convex Minimization

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

This paper proposes a smoothing technique for nonsmooth convex minimization using self-concordant barriers. To illustrate the main ideas, we compare our technique and the proximity smoothing approach (Nesterov2005) via the classical gradient method on both ...

Ieee2014

H∞ smith predictor design for time-delayed MIMO systems via convex optimization

Alireza Karimi, Achille Nicoletti

A new method for robust fixed-order H∞ controller design for uncertain time-delayed MIMO 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 linear ...

IEEE2014

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

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In recent works, compressed sensing (CS) and convex opti- mization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions [1, 2, ...

Ieee2014

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

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing system 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 a few unknown (but sparse) signals. We extend our earlier s ...

2013

Regularized Bundle Methods for Convex and Non-Convex Risks

Machine learning is most often cast as an optimization problem. Ideally, one expects a convex objective function to rely on efficient convex optimizers with nice guarantees such as no local optima. Yet, non-convexity is very frequent in practice and it may ...

2012

Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method

Colin Neil Jones

Solving a convex optimization problem within an a priori certified period of time is a challenging problem. This paper discusses the certification of Nesterov’s fast gradient method for problems with a strictly quadratic objective and a feasible set given ...

2011

Scalable sparse covariance estimation via self-concordance

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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

Barrier Smoothing for Nonsmooth Convex Minimization

H∞ smith predictor design for time-delayed MIMO systems via convex optimization

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

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

Regularized Bundle Methods for Convex and Non-Convex Risks

Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

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

Barrier Smoothing for Nonsmooth Convex Minimization

H∞ smith predictor design for time-delayed MIMO systems via convex optimization

Regularized Bundle Methods for Convex and Non-Convex Risks

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

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

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

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method