Overlapping Domain Decomposition Methods for Noncoercive Hamilton-Jacobi-Bellman Equation

This study presents an approach to demonstrate the monotonic and geometric convergence of the non-coercive Hamilton-Jacobi-Bellman problem with Dirichlet boundary conditions using the finite difference method. The approach combines overlapping domain decomposition with a sequence of lower and upper solutions to characterize the solution in a discrete setting. Numerical experiments are conducted to validate the methodology and illustrate the consistency of the theoretical findings.

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Publication Details

Journal
European Journal of Pure and Applied Mathematics
Published
2025-05-01
DOI
https://doi.org/10.29020/nybg.ejpam.v18i2.6070
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Overlapping Domain Decomposition Methods for Noncoercive Hamilton-Jacobi-Bellman Equation

Mohamed Haiour, Sofiane Madi, Samar Chebbah
European Journal of Pure and Applied Mathematics
Model Reduction and Neural Networks
article

Overlapping Domain Decomposition Methods for Noncoercive Hamilton-Jacobi-Bellman Equation

Mohamed Haiour, Sofiane Madi, Samar Chebbah
article en

Abstract

This study presents an approach to demonstrate the monotonic and geometric convergence of the non-coercive Hamilton-Jacobi-Bellman problem with Dirichlet boundary conditions using the finite difference method. The approach combines overlapping domain decomposition with a sequence of lower and upper solutions to characterize the solution in a discrete setting. Numerical experiments are conducted to validate the methodology and illustrate the consistency of the theoretical findings.

European Journal of Pure and Applied MathematicsVol. 18(2)
Badji Mokhtar-Annaba University (DZ), University of Batna 1 (DZ), École Normale Supérieure de Constantine (DZ)
Openalex Percentile: Top 93%
Model Reduction and Neural Networks
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