Consider the approximation of Navier-Stokes equations for a Newtonian fluid by Euler type systems with relaxation. This requires to decompose the second-order derivative terms of the velocity into first-order terms. We use Hurwitz-Radon matrices for this decomposition. We prove the convergence of the approximate systems to the Navier-Stokes equations locally in time for smooth initial data and globally in time for initial data near constant equilibrium states.

Online optimization where the cost function is gradually revealed has a lot of applications in machine learning. Recently, there has been interest in distributed online optimization due to the widespread wireless connection and the emergence of internet of things. This talk is focused on the distributed online optimization problem over a multi-agent network subject to local set constraints and coupled inequality constraints, which has a large number of applications in practice, such as wireless sensor networks, power systems and plug-in electric vehicles. We present a primal-dual algorithm which overcomes the limitations of existing works where balanced graph and boundedness of certain algorithm parameter are assumed which are inappropriate.

We will discuss recent developments in the representation theory of Lie algebras of polynomial vector fields on algebraic varieties (based on joint results with Y.Billih, J.Nilssen and A.Zaidan). In particular, we focus on the classification of irreducible representations of the Lie algebra of vector fields on a torus.

We introduce a recent (on-going) work joint with Fagui Li on minimal hypersurfaces in unit spheres, where some integral inequalities and their equality characterizations are obtained.

We present some work on analysis of elliptic PDEs of non-local types. And we give a brief introduction on: some basic maximum principles, existence and uniqueness, classi_cations and Liouville type theorems, Bocher type theorems and the related maximum principles for singular solutions. The focus is on the ideas and related basic estimates behind these research work. We will also discuss some basic estimates for linear equations.

I will survey some recent progress on differential calculus of quivers with potentials and give some applications of it to algebraic geometry and representation theory.

We propose an inexact proximal augmented Lagrangian framework with explicit inner problem termination rule for composite convex optimization problems. We consider arbitrary linearly convergent inner solver including in particular stochastic algorithms, making the resulting framework more scalable facing the ever-increasing problem dimension. Each subproblem is solved inexactly with an explicit and self-adaptive stopping criterion, without requiring to set an a priori target accuracy. When the primal and dual domain

In this talk, we present a novel nonlocal nonlinear coarse grid approximation using a machine learning algorithm. Multiscale models for complex nonlinear systems require nonlocal multicontinua approaches. These rigorous techniques require complex local computations, which involve solving local problems in oversampled regions subject to constraints. The solutions of these local problems can be replaced by solving original problem on a coarse (oversampled) region for many input parameters (boundary and source terms) and computing effective properties derived by nonlinear nonlocal multicontinua approaches. The effective properties depend on many variables (oversampled region and the number of continua), thus their calculations require some type of machine learning techniques.