On the implementation of a primal-dual algorithm for second order time-dependent mean field games with local couplings
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Abstract (arXiv)
We study a numerical approximation of a time-dependent Mean Field Game (MFG) system with local couplings. The discretization we consider stems from a variational approach described in [Briceno-Arias, Kalise, and Silva, SIAM J. Control Optim., 2017] for the stationary problem and leads to the finite difference scheme introduced by Achdou and Capuzzo-Dolcetta in [SIAM J. Numer. Anal., 48(3):1136-1162, 2010]. In order to solve the finite dimensional variational problems, in [Briceno-Arias, Kalise, and Silva, SIAM J. Control Optim., 2017] the authors implement the primal-dual algorithm introduced by Chambolle and Pock in [J. Math. Imaging Vision, 40(1):120-145, 2011], whose core consists in iteratively solving linear systems and applying a proximity operator. We apply that method to time-dependent MFG and, for large viscosity parameters, we improve the linear system solution by replacing the direct approach used in [Briceno-Arias, Kalise, and Silva, SIAM J. Control Optim., 2017] by suitable preconditioned iterative algorithms.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 1; 2025: 4.
9 papers.
- 2021Scaling up Mean Field Games with Online Mirror Descentcited by 29
- 2023Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equationscited by 10
- 2024MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Gamescited by 5
- 2021Exploration noise for learning linear-quadratic mean field gamescited by 3
- 2025Simultaneously Solving Infinitely Many LQ Mean Field Games In Hilbert Spaces: The Power of Neural Operatorscited by 3
- 2023Decoding Mean Field Games from Population and Environment Observations By Gaussian Processescited by 2
- 2025Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problemscited by 2
- 2025Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equationscited by 0
- 2025Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problemscited by 0
- 2025Simultaneously Solving Infinitely Many LQ Mean Field Games In Hilbert Spaces: The Power of Neural Operators22 Oct 2025
- 2025Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems1 May 2025
- 2025Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations17 Apr 2025
- 2025Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problems17 Feb 2025
- 2024MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games1 May 2024
- 2023Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes8 Dec 2023
- 2023Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations4 Mar 2023
- 2021Exploration noise for learning linear-quadratic mean field games2 Jul 2021
- 2021Scaling up Mean Field Games with Online Mirror Descent28 Feb 2021