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Anastasios Kyrillidis

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Publications associées (23)

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We study convex optimization problems that feature low-rank matrix solutions. In such scenarios, non-convex methods offer significant advantages over convex methods due to their lower space complexity as well as faster convergence speed. Moreover, many of ...

2018

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

INFORMS2018

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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

Group-Sparse Model Selection: Hardness and Relaxations

Volkan Cevher, Anastasios Kyrillidis, Luca Baldassarre, Nirav Bhan

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of ``interpretable" signals along with ...

Institute of Electrical and Electronics Engineers2016

Convex block-sparse linear regression with expanders - provably

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Luca Baldassarre, Bubacarr Bah

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-ti ...

2016

Composite Self-Concordant Minimization

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose a variable metric framework for minimizing the sum of a self-concordant function and a possibly non-smooth convex function endowed with a computable proximal operator. We theoretically establish the convergence of our framework without relying o ...

Massachusetts Institute of Technology Press2015

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Anastasios Kyrillidis

Many natural and man-made signals can be described as having a few degrees of freedom relative to their size due to natural parameterizations or constraints; examples include bandlimited signals, collections of signals observed from multiple viewpoints in ...

EPFL2014

Fixed-Rank Rayleigh Quotient Maximization by an MPSK Sequence

Anastasios Kyrillidis

Certain optimization problems in communication systems, such as limited-feedback constant-envelope beamforming or noncoherent M-ary phase-shift keying (MPSK) sequence detection, result in the maximization of a fixed-rank positive semidefinite quadratic for ...

Institute of Electrical and Electronics Engineers2014

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