Publications related to A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines the notions of smoothing and homotopy under the CGM framework, and provably achieves the optimal O(1/sqrt(k)) convergenc ...

ICML2018

A Data-Driven Approach to Power Converter Control via Convex Optimization

Alireza Karimi, Achille Nicoletti, Michele Martino

A new model reference data-driven approach is presented for synthesizing controllers for the CERN power converter control system. This method uses the frequency re- sponse function (FRF) of a system in order to avoid the problem of unmodeled dynamics assoc ...

2017

Faster Coordinate Descent via Adaptive Importance Sampling

Volkan Cevher, Martin Jaggi, Dmytro Perekrestenko

Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our ...

PMLR2017

A single-phase, proximal path-following framework

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose a new proximal, path-following framework for a class of---possibly non-smooth---constrained convex problems. We consider settings where the non-smooth part is endowed with a proximity operator, and the constraint set is equipped with a self-conc ...

INFORMS2017

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

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

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

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

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 Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

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A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

A Data-Driven Approach to Power Converter Control via Convex Optimization

Faster Coordinate Descent via Adaptive Importance Sampling

A single-phase, proximal path-following framework

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

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

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

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

Composite convex minimization involving self-concordant-like cost functions

Faster Coordinate Descent via Adaptive Importance Sampling

Composite convex minimization involving self-concordant-like cost functions

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

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

A Data-Driven Approach to Power Converter Control via Convex Optimization

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

A single-phase, proximal path-following framework

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

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