The probabilistic superiority of stochastic symplectic methods via large deviations principles
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| _version_ | 1866910041799393280 |
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| author | Chen, Chuchu Hong, Jialin Jin, Diancong Sun, Liying |
| author_facet | Chen, Chuchu Hong, Jialin Jin, Diancong Sun, Liying |
| contents | It is well known that symplectic methods have been rigorously shown to be superior to non-symplectic ones especially in long-time computation, when applied to deterministic Hamiltonian systems. In this paper, we attempt to study the probabilistic superiority of stochastic symplectic methods by means of the theory of large deviations. We propose the concept of asymptotical preservation of numerical methods for large deviations principles associated with the exact solutions of the general stochastic Hamiltonian systems. Concerning that the linear stochastic oscillator is one of the typical stochastic Hamiltonian systems, we take it as the test equation in this paper to obtain precise results about the rate functions of large deviations principles for both exact and numerical solutions. Based on the Gärtner--Ellis theorem, we first study the large deviations principles of the mean position and the mean velocity for both the exact solution and its numerical approximations. Then, we prove that stochastic symplectic methods asymptotically preserve these two large deviations principles, but non-symplectic ones do not. This indicates that stochastic symplectic methods are able to approximate well the exponential decay speed of the "hitting probability" of the mean position and mean velocity of the stochastic oscillator. To the best of our knowledge, this is the first result about using large deviations principle to show the superiority of stochastic symplectic methods compared with non-symplectic ones in the existing literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1906_03451 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | The probabilistic superiority of stochastic symplectic methods via large deviations principles Chen, Chuchu Hong, Jialin Jin, Diancong Sun, Liying Numerical Analysis It is well known that symplectic methods have been rigorously shown to be superior to non-symplectic ones especially in long-time computation, when applied to deterministic Hamiltonian systems. In this paper, we attempt to study the probabilistic superiority of stochastic symplectic methods by means of the theory of large deviations. We propose the concept of asymptotical preservation of numerical methods for large deviations principles associated with the exact solutions of the general stochastic Hamiltonian systems. Concerning that the linear stochastic oscillator is one of the typical stochastic Hamiltonian systems, we take it as the test equation in this paper to obtain precise results about the rate functions of large deviations principles for both exact and numerical solutions. Based on the Gärtner--Ellis theorem, we first study the large deviations principles of the mean position and the mean velocity for both the exact solution and its numerical approximations. Then, we prove that stochastic symplectic methods asymptotically preserve these two large deviations principles, but non-symplectic ones do not. This indicates that stochastic symplectic methods are able to approximate well the exponential decay speed of the "hitting probability" of the mean position and mean velocity of the stochastic oscillator. To the best of our knowledge, this is the first result about using large deviations principle to show the superiority of stochastic symplectic methods compared with non-symplectic ones in the existing literature. |
| title | The probabilistic superiority of stochastic symplectic methods via large deviations principles |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/1906.03451 |