Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912527184560128 |
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| author | Nakashima, Haruto Ganguly, Siddhartha Kashima, Kenji |
| author_facet | Nakashima, Haruto Ganguly, Siddhartha Kashima, Kenji |
| contents | We tackle the data-driven chance-constrained density steering problem using the Gromov-Wasserstein metric. The underlying dynamical system is an unknown linear controlled recursion, with the assumption that sufficiently rich input-output data from pre-operational experiments are available. The initial state is modeled as a Gaussian mixture, while the terminal state is required to match a specified Gaussian distribution. We reformulate the resulting optimal control problem as a difference-of-convex program and show that it can be efficiently and tractably solved using the DC algorithm. Numerical results validate our approach through various data-driven schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance Nakashima, Haruto Ganguly, Siddhartha Kashima, Kenji Optimization and Control Machine Learning Systems and Control We tackle the data-driven chance-constrained density steering problem using the Gromov-Wasserstein metric. The underlying dynamical system is an unknown linear controlled recursion, with the assumption that sufficiently rich input-output data from pre-operational experiments are available. The initial state is modeled as a Gaussian mixture, while the terminal state is required to match a specified Gaussian distribution. We reformulate the resulting optimal control problem as a difference-of-convex program and show that it can be efficiently and tractably solved using the DC algorithm. Numerical results validate our approach through various data-driven schemes. |
| title | Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance |
| topic | Optimization and Control Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2508.06052 |