Perspectives on characteristics based curse-of-dimensionality-free numerical approaches for solving Hamilton-Jacobi equations
Not on the map. Why?
Abstract (arXiv)
This paper extends the considerations of the works [1, 2] regarding curse-of-dimensionality-free numerical approaches to solve certain types of Hamilton-Jacobi equations arising in optimal control problems, differential games and elsewhere. A rigorous formulation and justification for the extended Hopf-Lax formula of [2] is provided together with novel theoretical and practical discussions including useful recommendations. By using the method of characteristics, the solutions of some problem classes under convexity/concavity conditions on Hamiltonians (in particular, the solutions of Hamilton-Jacobi-Bellman equations in optimal control problems) are evaluated separately at different initial positions. This allows for the avoidance of the curse of dimensionality, as well as for choosing arbitrary computational regions. The corresponding feedback control strategies are obtained at selected positions without approximating the partial derivatives of the solutions. The results of numerical simulations demonstrate the high potential of the proposed techniques. It is also pointed out that, despite the indicated advantages, the related approaches still have a limited range of applicability, and their extensions to Hamilton-Jacobi-Isaacs equations in zero-sum two-player differential games are currently developed only for sufficiently narrow classes of control systems. That is why further extensions are worth investigating.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
7 papers.
- 2022SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problemscited by 9
- 2021Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEscited by 5
- 2023Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problemscited by 5
- 2024Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learningcited by 5
- 2025Neural Implicit Solution Formula for Efficiently Solving Hamilton-Jacobi Equationscited by 4
- 2024HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative modelscited by 2
- 2026Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEscited by 0
- 2026Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs22 Jun 2026
- 2025Neural Implicit Solution Formula for Efficiently Solving Hamilton-Jacobi Equations31 Jan 2025
- 2024HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models15 Sep 2024
- 2024Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning16 Jun 2024
- 2023Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems22 Mar 2023
- 2022SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems14 Jan 2022
- 2021Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs7 May 2021