Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …

As a paradigm to recover unknown entries of a matrix from partial observations, low-rank
matrix completion (LRMC) has generated a great deal of interest. Over the years, there have …

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest
in low-rank modeling of MRI images. Existing approaches have focused on higher …

Partial separability (PS) and sparsity have been previously used to enable reconstruction of
dynamic images from undersampled (k, t)-space data. This paper presents a new method to …

Purpose This article introduces a constrained imaging method based on low‐rank and
subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). Theory …

Alternating minimization is a widely used and empirically successful heuristic for matrix
completion and related low-rank optimization problems. Theoretical guarantees for …

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 …

The matrix separation problem aims to separate a low-rank matrix and a sparse matrix from
their sum. This problem has recently attracted considerable research attention due to its …

Recovery of low-rank matrices has recently seen significant activity in many areas of science
and engineering, motivated by recent theoretical results for exact reconstruction guarantees …