In this paper, we extend the generalized approximate message passing (G-AMP) approach,
originally proposed for high-dimensional generalized-linear regression in the context of …

We consider the non-square matrix sensing problem, under restricted isometry property
(RIP) assumptions. We focus on the non-convex formulation, where any rank-r matrix $ X∈ …

We study the recovery of Hermitian low rank matrices X∈ C n× n from undersampled
measurements via nuclear norm minimization. We consider the particular scenario where …

We study the minimization of a convex function f (X) over the set of n\times n positive semi-
definite matrices, but when the problem is recast as\min_U g (U):= f (UU^⊤), with …

We establish theoretical recovery guarantees of a family of Riemannian optimization
algorithms for low rank matrix recovery, which is about recovering an m*n rank r matrix from …

Matrices of low rank can be uniquely determined from fewer linear measurements, or
entries, than the total number of entries in the matrix. Moreover, there is a growing literature …

Matrix completion involves recovering a matrix from a subset of its entries by utilizing
interdependency between the entries, typically through low rank structure. Despite matrix …

We introduce the conjugate gradient iterative hard thresholding (CGIHT) family of algorithms
for the efficient solution of constrained underdetermined linear systems of equations arising …

A rank-r matrix X∈R^m*n can be written as a product UV^⊤, where U∈R^m*r and
V∈R^n*r. One could exploit this observation in optimization: eg, consider the minimization …

A fundamental limitation of the inverse acoustic problem is determined by the size of the
array and the microphone density. A solution to achieve large array and/or high microphone …