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Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework

Volkan Cevher, Jonathan Mark Scarlett

The support recovery problem consists of deter- mining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as group testing, compressive sensing, and subset selection in ...

2015

Splitting the Smoothed Primal-Dual Gap: Optimal Alternating Direction Methods

Volkan Cevher, Quoc Tran Dinh

We develop rigorous alternating direction optimization methods for a prototype constrained convex optimization template, which has broad applications in computational sciences. We build upon our earlier work on the model-based gap reduction (MGR) technique ...

Tech. Report. LIONS-EPFL (2015)2015

Scalable Convex Methods for Phase Retrieval

Volkan Cevher, Alp Yurtsever, Ya-Ping Hsieh

This paper describes scalable convex optimization methods for phase retrieval. The main characteristics of these methods are the cheap per-iteration complexity and the low-memory footprint. With a variant of the original PhaseLift formulation, we first ill ...

2015

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

Model-based Sketching and Recovery with Expanders

Volkan Cevher, Luca Baldassarre, Bubacarr Bah

Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper conside ...

2014

An inexact proximal path-following algorithm for constrained convex minimization

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

Many scientific and engineering applications feature large-scale non-smooth convex minimization problems over convex sets. In this paper, we address an important instance of this broad class where we assume that the non-smooth objective is equipped with a ...

Siam Publications2014

Matrix Recipes for Hard Thresholding Methods

Volkan Cevher, Anastasios Kyrillidis

In this paper, we present and analyze a new set of low-rank recovery algorithms for linear inverse problems within the class of hard thresholding methods. We provide strategies on how to set up these algorithms via basic ingredients for different configura ...

Springer Verlag2014

Metric Learning with Rank and Sparsity Constraints

Volkan Cevher, Baran Gözcü, Bubacarr Bah

Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...

Ieee2014

Scalable sparse covariance estimation via self-concordance

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Rabeeh Karimi Mahabadi

We consider the class of convex minimization problems, composed of a self-concordant function, such as the logdet metric, a convex data fidelity term h and, a regularizing -- possibly non-smooth -- function g. This type of problems have recently attracted ...

2014

Volkan Cevher

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Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework

Splitting the Smoothed Primal-Dual Gap: Optimal Alternating Direction Methods

Scalable Convex Methods for Phase Retrieval

Convex Optimization for Big Data

Model-based Sketching and Recovery with Expanders

An inexact proximal path-following algorithm for constrained convex minimization

Matrix Recipes for Hard Thresholding Methods

Metric Learning with Rank and Sparsity Constraints

Scalable sparse covariance estimation via self-concordance

A variational approach to stable principal component pursuit

Metric Learning with Rank and Sparsity Constraints

Model-based Sketching and Recovery with Expanders

Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework

Scalable Convex Methods for Phase Retrieval

Matrix Recipes for Hard Thresholding Methods

Convex Optimization for Big Data

Splitting the Smoothed Primal-Dual Gap: Optimal Alternating Direction Methods

An inexact proximal path-following algorithm for constrained convex minimization

Scalable sparse covariance estimation via self-concordance

A variational approach to stable principal component pursuit