论文标题
$ z $的随机Lipschitz条件的向后随机微分方程的大偏差原理
Large Deviation Principle for Backward Stochastic Differential Equations with a stochastic Lipschitz condition on $z$
论文作者
论文摘要
在本文中，抛物性部分微分方程的粘度解的概率解释是通过解决一类二次向后随机微分方程的解决方案（简称BSDE）获得的。此外，我们证明了该类别的二次BSDE解决方案的收敛性和较大的偏差原理，这与马尔可夫工艺家族有关，与往往为零的扩散系数有关。
In this paper, a probabilistic interpretation for the viscosity solution of a parabolic partial differential equation is obtained by virtue of the solution of a class of quadratic backward stochastic differential equations (BSDEs, for short). Furthermore, we prove the convergence and the large deviation principle for the solution of this class of quadratic BSDEs, which is associated with a family of Markov processes with the diffusion coefficients that tend to be zero.
