Publications associées à 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

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

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

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

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

Barrier Smoothing for Nonsmooth Convex Minimization

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

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

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

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

Regularized Bundle Methods for Convex and Non-Convex Risks