This paper develops a new class of nonconvex regularizers for low-rank matrix recovery.
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …

In this paper, we propose a new definition of tensor Schatten-p norm (t-Schatten-p norm)
based on t-SVD, and prove that this norm has similar properties to matrix Schatten-p norm …

Consider the minimization of a nonconvex differentiable function over a bounded
polyhedron. A popular primal-dual first-order method for this problem is to perform a gradient …

We propose a new method for computing dynamic mode decomposition evolution matrices,
which we use to analyze dynamical systems. Unlike the majority of existing methods, our …

Matrix Factorization (MF) has been proven to be an effective approach for the generation of
a successful recommender system. However, most current MF-based recommenders cannot …

Physical design problems, such as photonic inverse design, are typically solved using local
optimization methods. These methods often produce what appear to be good or very good …

This article proposes a novel edge collaborative compressed sensing for mechanical
vibration monitoring (MVM) to address the severe shortage of storage and computational …

When applying imitation learning techniques to fit a policy from expert demonstrations, one
can take advantage of prior stability/robustness assumptions on the expert's policy and …

We consider a non-convex constrained optimization problem, where the objective function is
weakly convex and the constraint function is either convex or weakly convex. To solve this …

We consider the problem of minimizing the sum of a smooth function and a composition of a
zero-one loss function with a linear operator, namely the zero-one composite optimization …