We study a class of semi-implicit Taylor-type numerical methods that are easy to implement and designed to solve multidimensional stochastic differential equations driven by a general rough noise, e.g. a fractional Brownian motion. In the multiplicative noise case, the equation is understood as a rough differential equation in the sense of T. Lyons. We focus on equations for which the drift coefficient may be unbounded and satisfies a one-sided Lipschitz condition only. We prove well-posedness of the methods, provide a full analysis, and deduce their convergence rate. Numerical experiments show that our schemes are particularly useful in the case of stiff rough stochastic differential equations driven by a fractional Brownian motion
This thesis consists of three independent chapters in the theme of rough path theory. Introduced in ...
We investigate the pathwise well-posedness of stochastic evolution equations perturbed by multiplica...
We build a hybrid theory of rough stochastic analysis which seamlessly combines the advantages of bo...
We study a class of semi-implicit Taylor-type numerical methods that are easy to implement and desig...
We study Runge-Kutta methods for rough differential equations which can be used to calculate solutio...
Discrete approximations to solutions of stochastic differential equations are well-known to converge...
In this paper we discuss implicit Taylor methods for stiff Ito stochastic differential equations. Ba...
International audienceThis paper is devoted to the study of numerical approximation schemes for a cl...
AbstractThe paper considers the derivation of families of semi-implicit schemes of weak order N=3.0 ...
The main motivation behind writing this thesis was to construct numerical methods to approximate sol...
A fully implicit integration method for stochastic differential equations with significant multipl...
Multiscale differential equations arise in the modeling of many important problems in the science an...
In this paper a family of fully implicit Milstein methods are introduced for solving stiff stochasti...
AbstractThis article introduces the splitting method to systems driven by rough paths. The focus is ...
Abstract. Discrete approximations to solutions of stochastic differential equations are well-known t...
This thesis consists of three independent chapters in the theme of rough path theory. Introduced in ...
We investigate the pathwise well-posedness of stochastic evolution equations perturbed by multiplica...
We build a hybrid theory of rough stochastic analysis which seamlessly combines the advantages of bo...
We study a class of semi-implicit Taylor-type numerical methods that are easy to implement and desig...
We study Runge-Kutta methods for rough differential equations which can be used to calculate solutio...
Discrete approximations to solutions of stochastic differential equations are well-known to converge...
In this paper we discuss implicit Taylor methods for stiff Ito stochastic differential equations. Ba...
International audienceThis paper is devoted to the study of numerical approximation schemes for a cl...
AbstractThe paper considers the derivation of families of semi-implicit schemes of weak order N=3.0 ...
The main motivation behind writing this thesis was to construct numerical methods to approximate sol...
A fully implicit integration method for stochastic differential equations with significant multipl...
Multiscale differential equations arise in the modeling of many important problems in the science an...
In this paper a family of fully implicit Milstein methods are introduced for solving stiff stochasti...
AbstractThis article introduces the splitting method to systems driven by rough paths. The focus is ...
Abstract. Discrete approximations to solutions of stochastic differential equations are well-known t...
This thesis consists of three independent chapters in the theme of rough path theory. Introduced in ...
We investigate the pathwise well-posedness of stochastic evolution equations perturbed by multiplica...
We build a hybrid theory of rough stochastic analysis which seamlessly combines the advantages of bo...