Compressed Sensing and its Applications

Preface.- On the Global-Local Dichotomy in Sparsity Modeling, Batenkov, Romano, Elad.- Fourier Phase Retrieval: Uniqueness and Algorithms, Bendory, Beinert, Eldar.- Compressed Sensing Approaches for Polynomial Approximation of High-Dimensional Functions, Adcock, Brugiapaglia, Webster.- Multisection in the Stochastic Block Model using Semidefinite Programming, Agarwal, Bandeira, Koiliaris, Kolla.- Recovering Signals with Unknown Sparsity in Multiple Dictionaries, Ahmad, Schniter.- Compressive Classification and the Rare Eclipse Problem, Bandeira, Mixon, Recht.- Weak Phase Retrieval, Bothelo-Andrade, Casazza, Ghoreishi, Jose, Tremain.- Cubatures on Grassmannians: Moments, Dimension Reduction, and Related Topics, Breger, Ehler, Graef, Peter.- A Randomized Tensor Train Singular Value Decomposition, Huber, Schneider, Wolf.- Versatile and Scalable Cosparse Methods for Phsyics-driven Inverse Problems, Kitic, Bensiad, Albera, Bertin, Gribonval.- Total Variation Minimization in Compressed Sensing, Felix Krahmer, Kruschel, Sandbichler.- Compressed Sensing in Hilbert Spaces, Traonmilin, Puy, Gribonval, Davies.

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• Álgebra
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Compressed Sensing;Dimensionality Reduction;Information Theory;Random Matrices;Sparse Approximation;Sparse Recovery;Fourier phase retrieval;Hilbert spaces;stochastic block model;sparse probability measures;information and communication, circuits;matrix theory