Mean field type control with species dependent dynamics via structured tensor optimization
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| Title: | Mean field type control with species dependent dynamics via structured tensor optimization |
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| Authors: | Ringh, Axel, 1989, Haasler, Isabel, Chen, Yongxin, Karlsson, Johan |
| Source: | IEEE Control Systems Letters. 7:2898-2903 |
| Subject Terms: | Computational methods, Tensors, Robot kinematics, Mathematical models, Fluid flow systems, Games, Sociology, Large-scale systems, Statistics, Optimization, Stochastic optimal control, Costs |
| Description: | In this work we consider mean field type control problems with multiple species that have different dynamics. We formulate the discretized problem using a new type of entropy-regularized multimarginal optimal transport problems where the cost is a decomposable structured tensor. A novel algorithm for solving such problems is derived, using this structure and leveraging recent results in entropy-regularized optimal transport. The algorithm is then demonstrated on a numerical example in robot coordination problem for search and rescue, where three different types of robots are used to cover a given area at minimal cost. |
| File Description: | electronic |
| Access URL: | https://research.chalmers.se/publication/536614 https://research.chalmers.se/publication/536614/file/536614_Fulltext.pdf |
| Database: | SwePub |
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Nájsť tento článok vo Web of Science | Abstract: | In this work we consider mean field type control problems with multiple species that have different dynamics. We formulate the discretized problem using a new type of entropy-regularized multimarginal optimal transport problems where the cost is a decomposable structured tensor. A novel algorithm for solving such problems is derived, using this structure and leveraging recent results in entropy-regularized optimal transport. The algorithm is then demonstrated on a numerical example in robot coordination problem for search and rescue, where three different types of robots are used to cover a given area at minimal cost. |
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| ISSN: | 24751456 |
| DOI: | 10.1109/LCSYS.2023.3289050 |