72 citations · 542 across the 65 of their papers we have counts for
7 papers · 1 filter
Convex block-sparse linear regression with expanders -- provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad +3
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…
Linear Inverse Problems with Norm and Sparsity Constraints
Volkan Cevher, Sina Jafarpour, Anastasios Kyrillidis
We describe two nonconventional algorithms for linear regression, called GAME and CLASH. The salient characteristics of these approaches is that they exploit the convex -ba…
Structured Sparsity: Discrete and Convex approaches
Anastasios Kyrillidis, Luca Baldassarre, Marwa El-Halabi +2
Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance…
Fixed-rank Rayleigh Quotient Maximization by an PSK Sequence
Anastasios Kyrillidis, George N. Karystinos
Certain optimization problems in communication systems, such as limited-feedback constant-envelope beamforming or noncoherent -ary phase-shift keying (PSK) sequence detection…
Sublinear Time, Approximate Model-based Sparse Recovery For All
Anastasios Kyrillidis, Volkan Cevher
We describe a probabilistic, {\it sublinear} runtime, measurement-optimal system for model-based sparse recovery problems through dimensionality reducing, {\em dense} random matric…
Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction
Anastasios Kyrillidis, Volkan Cevher
We propose Matrix ALPS for recovering a sparse plus low-rank decomposition of a matrix given its corrupted and incomplete linear measurements. Our approach is a first-order project…