This paper studies the long-existing idea of adding a nice smooth function to “smooth” a
nondifferentiable objective function in the context of sparse optimization, in particular, the …

This paper studies a general framework for high-order tensor SVD. We propose a new
computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to …

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to
matrix completion based on a sum of multiple orthonormal side information terms, together …

The common task in matrix completion (MC) and robust principle component analysis
(RPCA) is to recover a low-rank matrix from a given data matrix. These problems gained …

In this paper, we propose a parameterized three-operator splitting algorithm to solve
nonconvex minimization problems with the sum of three non-convex functions, where two of …

The singular value thresholding (SVT) algorithm plays an important role in the well‐known
matrix reconstruction problem, and it has many applications in computer vision and …

Abstract Box-constrained ℓ _1 ℓ 1-minimization in some cases performs remarkably better
than the classical ℓ _1 ℓ 1-minimization when appropriate box constraints are available. And …

A new first-order optimality condition for the basis pursuit denoise (BPDN) problem is
derived. This condition provides a new approach to choose the penalty parameters …

High-dimensional statistics has attracted considerable attention in recent years. To achieve
reliable estimation and uncertainty quantification, some low-dimensional structures …

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to
matrix completion based on a sum of multiple orthonormal side information terms, together …