Randomized Milstein algorithm for approximation of solutions of jump-diffusion SDEs
DOI10.1016/j.cam.2023.115631arXiv2212.00411OpenAlexW4387709510MaRDI QIDQ6126057
Verena Schwarz, Michaela Szölgyenyi, Paweł Przybyłowicz
Publication date: 9 April 2024
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.00411
information-based complexityLévy's area\(n\)th minimal errorjump-diffusion SDEsrandomized Milstein algorithmoptimality of algorithms
Analysis of algorithms and problem complexity (68Q25) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Numerical solutions to stochastic differential and integral equations (65C30)
Cites Work
- Unnamed Item
- Unnamed Item
- Optimal global approximation of stochastic differential equations with additive Poisson noise
- Optimal global approximation of SDEs with time-irregular coefficients in asymptotic setting
- Numerical solution of stochastic differential equations with jumps in finance
- Two quadrature rules for stochastic Itô-integrals with fractional Sobolev regularity
- Theory of stochastic differential equations with jumps and applications.
- Randomized derivative-free Milstein algorithm for efficient approximation of solutions of SDEs under noisy information
- Existence, uniqueness, and approximation of solutions of jump-diffusion SDEs with discontinuous drift
- Existence and uniqueness of solutions of SDEs with discontinuous drift and finite activity jumps
- Minimal asymptotic error for one-point approximation of SDEs with time-irregular coefficients
- A randomized Milstein method for stochastic differential equations with non-differentiable drift coefficients
- Optimal pointwise approximation of SDE's from inexact information
- Strong approximation of solutions of stochastic differential equations with time-irregular coefficients via randomized Euler algorithm
- Conditional expectations associated with stochastic processes
- Complexity of stochastic integration in Sobolev classes
- Efficient Approximation of SDEs Driven by Countably Dimensional Wiener Process and Poisson Random Measure
- Nonlinear Lebesgue and Itô integration problems of high complexity
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