We consider the multidimensional generalised stochastic Burgers equation in the space-periodic setting: $ \partial \mathbf{u}/\partial t+$ $(\nabla…

A problem of great interest in optimization is to minimize a sum of two closed, proper, and convex functions where one is smooth and the other has a …

Schwarz methods are attractive parallel solvers for large scale linear systems obtained when partial differential equations are discretized. For hybr…

Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insig…

Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar operations than the classical algorithm, have been c…

We consider the classical stochastic multi-armed bandit problem with a constraint on the total cost incurred by switching between actions. We prove m…

Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in da…

Inexact alternating direction multiplier methods (ADMMs) are developed for solving general separable convex optimization problems with a linear const…

Local iterated function systems are an important generalisation of the standard (global) iterated function systems (IFSs). For a particular class of …

In this paper, we study numerical methods for the solution of partial differential equations on evolving surfaces. The evolving hypersurface in $\Bbb…

The swimming trajectories of self-propelled organisms or synthetic devices in a viscous fluid can be altered by hydrodynamic interactions with nearby…

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Kenne…

Modeling and identification of high-dimensional stochastic processes is ubiquitous in many fields. In particular, there is a growing interest in mode…

We study array imaging of a sparse scene of point-like sources or scatterers in a homogeneous medium. For source imaging the sensors in the array are…

The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with expos…

A weak turbulence theory is derived for magnetohydrodynamics under rapid rotation and in the presence of a large-scale magnetic field. The angular ve…

The problem of finding sparse solutions to underdetermined systems of linear equations arises in several applications (e.g. signal and image processi…

The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and …

The stable principal component pursuit (SPCP) problem is a non-smooth convex optimization problem, the solution of which has been shown both in theor…

For many natural and engineered systems, a central function or design goal is the synchronization of one or more rhythmic or oscillating processes to…

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