The asymptotic behavior of stochastic gradient algorithms is studied. Relying on results from differential geometry (the Lojasiewicz gradient inequality), the single limit-point convergence of the algorithm iterates is demonstrated and relatively tight bounds on the convergence rate are derived....

For some backward stochastic Volterra integral equations (BSVIEs) in multi-dimensional Euclidean spaces, comparison theorems are established in a systematic way for the adapted solutions and adapted M-solutions. For completeness, comparison theorems for (forward) stochastic differential...

We consider stochastic Navier–Stokes equations in a 2D-bounded domain with the Navier with friction boundary condition. We establish the existence and the uniqueness of the solutions and study the vanishing viscosity limit. More precisely, we prove that solutions of stochastic Navier–Stokes...

In Puplinskaitė and Surgailis (2014) we introduced the notion of scaling transition for stationary random fields X on Z2 in terms of partial sums limits, or scaling limits, of X over rectangles whose sides grow at possibly different rate. The present paper establishes the existence of scaling...

We provide a new, concise proof of weak existence and uniqueness of solutions to the stochastic differential equation for the multidimensional skew Brownian motion. We also present an application to Brownian particles with skew-elastic collisions.

We give a necessary and sufficient condition for a d-dimensional Lévy process to be in the matrix normalized domain of attraction of a d-dimensional normal random vector, as t↓0. This transfers to the Lévy case classical results of Feller, Khinchin, Lévy and Hahn and Klass for random walks....

We study the problem of existence of solutions for generalized backward stochastic differential equation with two reflecting barriers (GRBSDE for short) under weaker assumptions on the data. Roughly speaking we show the existence of a maximal solution for GRBSDE when the terminal condition ξ is...

We propose a new algorithm to approximate weakly the solution of a McKean–Vlasov SDE. Based on the cubature method of Lyons and Victoir (2004), the algorithm is deterministic differing from the usual methods based on interacting particles. It can be parametrized in order to obtain a given...

Let Xj denote a fair gambler’s ruin process on Z∩[−N,N] started from X0=0, and denote by RN the number of runs of the absolute value, |Xj|, until the last visit j=LN by Xj to 0. Then, as N→∞, N−2RN converges in distribution to a density with Laplace transform: tanh(λ)/λ. In law, we...

In general, gradient estimates are very important and necessary for deriving convergence results in different geometric flows, and most of them are obtained by analytic methods. In this paper, we will apply a stochastic approach to systematically give gradient estimates for some important...