{"ID":2884576,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06052","arxiv_id":"2508.06052","title":"Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.06052","url_pdf":"https://arxiv.org/pdf/2508.06052v1","authors":"[\"Haruto Nakashima\",\"Siddhartha Ganguly\",\"Kenji Kashima\"]","published":"2025-08-08T06:21:21Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false}