Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations

DOI10.1016/j.amc.2022.127119OpenAlexW3180389317WikidataQ115361056 ScholiaQ115361056MaRDI QIDQ2141183

Long Teng

Publication date: 23 May 2022

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.06673

zbMATH Keywords

regressionhigh-dimensional problembackward stochastic differential equations (BSDEs)XGBoost

Mathematics Subject Classification ID

Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)

Related Items (2)

Numerical methods for backward stochastic differential equations: a survey ⋮ Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations

Uses Software

• XGBoost
• LightGBM
• CatBoost

Cites Work

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