Explicit Runge-Kutta schemes for Backward Stochastic Differential Equations
For researchers in numerical analysis and stochastic computation, this provides a foundational framework for designing high-order BSDE solvers, addressing a long-standing gap.
This work extends Butcher theory to backward stochastic differential equations (BSDEs), enabling systematic derivation of order conditions for explicit Runge-Kutta schemes. The proposed schemes achieve high-order accuracy, with numerical experiments confirming theoretical results.
The Butcher theory provides a powerful tool for analyzing order conditions of Runge-Kutta schemes for ordinary differential equations (ODEs); however, such a theory has not yet been well established for backward stochastic differential equations (BSDEs) -- motivating the current work to address this gap. Specifically, we propose a new class of explicit Runge-Kutta schemes for BSDEs. These schemes admit a concise formulation that closely mirrors their ODE counterparts. Building on this formulation, we extend the Butcher theory to the proposed schemes, thereby enabling a symbolic derivation of Taylor expansions for the local truncation errors, and yielding the order conditions. Our approach preserves the elegance and generality of the original Butcher theory: it avoids stage-by-stage error expansions and provides a systematic, stage-inductive analysis, applicable to schemes with any number of stages and any target order. Numerical experiments support the theoretical results.