Convergence Analysis of Iterative Deep Learning Algorithms for Fully Nonlinear BSPDEs in Non-Markovian Utility Maximization.

Ma J, Wu H, Zheng HH

DOI: 10.1007/s10915-026-03416-3
Published: 2026
Container: Journal of scientific computing
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1007/s10915-026-03416-3,
  title = {Convergence Analysis of Iterative Deep Learning Algorithms for Fully Nonlinear BSPDEs in Non-Markovian Utility Maximization.},
  author = {Ma J and Wu H and Zheng HH},
  year = {2026},
  journal = {Journal of scientific computing},
  doi = {10.1007/s10915-026-03416-3},
  url = {https://doi.org/10.1007/s10915-026-03416-3}
}

RIS

TY  - JOUR
TI  - Convergence Analysis of Iterative Deep Learning Algorithms for Fully Nonlinear BSPDEs in Non-Markovian Utility Maximization.
AU  - Ma J
AU  - Wu H
AU  - Zheng HH
PY  - 2026
JO  - Journal of scientific computing
DO  - 10.1007/s10915-026-03416-3
UR  - https://doi.org/10.1007/s10915-026-03416-3
ER  -

APA

J, M., H, W., & HH, Z. (2026). Convergence Analysis of Iterative Deep Learning Algorithms for Fully Nonlinear BSPDEs in Non-Markovian Utility Maximization.. Journal of scientific computing. https://doi.org/10.1007/s10915-026-03416-3

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pubmed · retrieved 2026-09-25T09:05:56.244Z