Abstract.
We present an adaptive multilevel Monte Carlo (MLMC) method for
weak approximations of solutions to Itô stochastic
differential equations (SDE). The work [Oper. Res. 56 (2008), 607–617]
proposed and analyzed an MLMC method based on a hierarchy of uniform time
discretizations and control variates to reduce the computational
effort required by a single level Euler–Maruyama Monte
Carlo method from
Funding source: Royal Institute of Technology in Stockholm
Award Identifier / Grant number: Dahlquist fellowship
Funding source: Department of Scientific Computing in Florida State University
Funding source: University of Austin Subcontract
Award Identifier / Grant number: 024550
Funding source: VR project
Award Identifier / Grant number: “Effektiva numeriska metoder för stokastiska differentialekvationer med tillämpningar”
Funding source: Center for Industrial and Applied Mathematics (CIAM) at the Royal Institute of Technology
Funding source: King Abdullah University of Science and Technology (KAUST)
The authors would like to thank Mike Giles and the two anonymous reviewers for valuable comments.
© 2014 by Walter de Gruyter Berlin/Boston