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Autori principali: Tang, Shanjian, Zhang, Huilin
Natura: Preprint
Pubblicazione: 2021
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Accesso online:https://arxiv.org/abs/2109.05282
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author Tang, Shanjian
Zhang, Huilin
author_facet Tang, Shanjian
Zhang, Huilin
contents The paper concerns classical solution of path-dependent partial differential equations (PPDEs) with coefficients depending on both variables of path and path-valued measure, which are crucial to understanding large-scale mean-field interacting systems in a non-Markovian setting. We construct classical solutions of the PPDEs via solution of the forward and backward stochastic differential equations. To accommodate the intricacies introduced by the appearance of the path in the coefficients, we develop a novel technique known as the ``parameter frozen'' approach to the PPDEs.
format Preprint
id arxiv_https___arxiv_org_abs_2109_05282
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Classical solution of path-dependent mean-field semilinear PDEs
Tang, Shanjian
Zhang, Huilin
Probability
Optimization and Control
60G22, 60H10, 34C29
The paper concerns classical solution of path-dependent partial differential equations (PPDEs) with coefficients depending on both variables of path and path-valued measure, which are crucial to understanding large-scale mean-field interacting systems in a non-Markovian setting. We construct classical solutions of the PPDEs via solution of the forward and backward stochastic differential equations. To accommodate the intricacies introduced by the appearance of the path in the coefficients, we develop a novel technique known as the ``parameter frozen'' approach to the PPDEs.
title Classical solution of path-dependent mean-field semilinear PDEs
topic Probability
Optimization and Control
60G22, 60H10, 34C29
url https://arxiv.org/abs/2109.05282