The discrete optimal transport (OT) problem, which offers an effective computational tool for
comparing two discrete probability distributions, has recently attracted much attention and …

Stochastic optimisation algorithms are the de facto standard for machine learning with large
amounts of data. Handling only a subset of available data in each optimisation step …

In this paper, we propose a novel Anderson's acceleration method to solve nonlinear
equations, which does\emph {not} require a restart strategy to achieve numerical stability …

We extend the framework of the Karush–Kuhn–Tucker (KKT) limiter from second-order
nonlinear scalar equations to complex systems of equations and construct time-implicit high …

We provide systematic studies of the variational properties of decomposable functions which
are compositions of an outer support function and an inner smooth mapping under certain …

Matrix optimization has various applications in finance, statistics, and engineering, etc. In
this paper, we derive the Lagrangian dual of the matrix optimization problem with sparse …