no code implementations • 12 Apr 2022 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we mainly focus on the numerical solution of high-dimensional stochastic optimal control problem driven by fully-coupled forward-backward stochastic differential equations (FBSDEs in short) through deep learning.
no code implementations • 4 Nov 2021 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we mainly focus on solving high-dimensional stochastic Hamiltonian systems with boundary condition, which is essentially a Forward Backward Stochastic Differential Equation (FBSDE in short), and propose a novel method from the view of the stochastic control.
no code implementations • 11 Feb 2021 • Qiang Han, Shaolin Ji
Novel multi-step predictor-corrector numerical schemes have been derived for approximating decoupled forward-backward stochastic differential equations (FBSDEs).
Numerical Analysis Numerical Analysis
no code implementations • 2 Feb 2021 • Shaolin Ji, Rundong Xu
Based on the stochastic maximum principle for the partially coupled forward-backward stochastic control system (FBSCS for short), a modified method of successive approximations (MSA for short) is established for stochastic recursive optimal control problems.
Optimization and Control Probability 93E20, 60H10, 60H30, 49M05
1 code implementation • 5 Jul 2020 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning.
no code implementations • 11 Jul 2019 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
Recently, the deep learning method has been used for solving forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs).
no code implementations • 16 May 2017 • Shaolin Ji, Hanqing Jin, Xiaomin Shi
This paper studies the continuous time mean-variance portfolio selection problem with one kind of non-linear wealth dynamics.
