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Multigrid methods for second order hamilton-jacobi-bellman and hamilton-jacobi-bellman-isaacs equations (Conference Paper)

机译:二阶hamilton-jacobi-bellman和hamilton-jacobi-bellman-isaacs方程的多重网格方法(会议论文)

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摘要

We propose multigrid methods for solving the discrete algebraic equations arising from the discretization of the second order Hamilton-Jacobi-Bellman (HJB) and Hamilton- Jacobi-Bellman-Isaacs (HJBI) equations. We propose a damped-relaxation method as a smoother for multigrid. In contrast with the standard policy iteration, the proposed damped-relaxation scheme is convergent for both HJB and HJBI equations. We show by local Fourier analysis that the dampedrelaxation smoother effectively reduces high frequency error. For problems with large jumps in control, we develop restriction and interpolation methods to capture the jumps on the coarse grids as well as during the coarse grid correction. We will demonstrate the effectiveness of the proposed multigrid methods for solving HJB and HJBI equations arising from option pricing as well as problems where policy iteration does not converge or converges slowly.
机译:我们提出了多网格方法来求解由二阶Hamilton-Jacobi-Bellman(HJB)和Hamilton-Jacobi-Bellman-Isaacs(HJBI)方程离散化而产生的离散代数方程。我们提出了一种阻尼松弛方法作为多网格的平滑器。与标准策略迭代相反,所提出的阻尼松弛方案对于HJB和HJBI方程都是收敛的。我们通过局部傅立叶分析表明,阻尼松弛平滑器有效地减少了高频误差。对于控制中存在较大跳跃的问题,我们开发了限制和插值方法来捕获粗网格上以及粗网格校正期间的跳跃。我们将证明所提出的多重网格方法对于解决由期权定价产生的HJB和HJBI方程以及策略迭代不能收敛或收敛缓慢的问题的有效性。

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