Publications related to Stochastic Frank-Wolfe for Composite Convex Minimization

Scampi: a robust approximate message-passing framework for compressive imaging

Florent Gérard Krzakala, Jean François Emmanuel Barbier

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

The article examines in some detail the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the stand ...

2016

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

Robert Gerhard Jérôme Luce

Nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the input data matrix belong to the convex cone generated by only a few of its columns. The provably most robust met ...

MATHICSE2016

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

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

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In recent works, sparse models and convex optimization techniques have been applied to radio-interferometric (RI) imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. In this talk, I will review our latest contribut ...

2015

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

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Alireza Karimi

In this paper, a new algorithm is proposed for the design of a family of controllers to be used within an adaptive switching control scheme. The resulting switching controller is able to attenuate the effects of disturbances having uncertain and possibly t ...

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

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Pierre Vandergheynst, Mahdad Hosseini Kamal

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...

Springer-Verlag Berlin2015

Stochastic Frank-Wolfe for Composite Convex Minimization

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Scampi: a robust approximate message-passing framework for compressive imaging

On the influence of momentum acceleration on online learning

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

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

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

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

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

On the influence of momentum acceleration on online learning

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

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

Composite convex minimization involving self-concordant-like cost functions

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

Scampi: a robust approximate message-passing framework for compressive imaging

Composite convex minimization involving self-concordant-like cost functions

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging