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Abstract—We study the general problem of optimizing a convex function of a matrix-valued variable subject to low-rank constraints.
Abstract: We study the general problem of optimizing a convex function of a matrix-valued variable subject to low-rank constraints.
This paper provides a novel algorithmic framework that achieves the best of both worlds: asymptotically as fast as factorization methods, while requiring no ...
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science.
In this work, we study the general problem of optimizing a convex function F(L) F ( L ) over the set of p×p p × p matrices, subject to rank constraints on L L .
May 18, 2020 · A popular approach in practice is to factorize the matrix into two compact low-rank factors, and then optimize these factors directly via simple ...
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Request PDF | On Jun 1, 2018, Mohammadreza Soltani and others published Fast Low-Rank Matrix Estimation for Ill-Conditioned Matrices | Find, read and cite ...
In this paper, we provide a novel algorithmic framework that achieves the best of both worlds: as fast as factorization methods, while requiring no spectral ...
Abstract. Low-rank matrix estimation is a canonical problem that finds numerous applications in signal pro- cessing, machine learning and imaging science.
In this paper, we describe how preconditioning should be done for noisy measurements to accelerate local convergence to minimax optimality.