Machine Learning Architectures for Price Formation Models
Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min–max characterization of the optimal control and price variables. Our main theoretical contribution is the development o...
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| Veröffentlicht in: | Applied mathematics & optimization 2023-08, Vol.88 (1), p.23, Article 23 |
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| container_title | Applied mathematics & optimization |
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| creator | Gomes, Diogo Gutierrez, Julian Laurière, Mathieu |
| description | Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min–max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models. |
| doi_str_mv | 10.1007/s00245-023-10002-8 |
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| subjects | Applied mathematics Calculus of Variations and Optimal Control Optimization Control Control theory Estimates Lagrange multiplier Machine learning Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Numerical and Computational Physics Optimal control Optimization Simulation Systems Theory Theoretical Training |
| title | Machine Learning Architectures for Price Formation Models |
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