Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929679448932352 |
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| author | Matoušek, J. Brandner, M. Duník, J. Punčochář, I. |
| author_facet | Matoušek, J. Brandner, M. Duník, J. Punčochář, I. |
| contents | This paper deals with the state estimation of stochastic systems and examines the possible employment of tensor decompositions in grid-based filtering routines, in particular, the tensor-train decomposition. The aim is to show that these techniques can lead to a massive reduction in both the computational and storage complexity of grid-based filtering algorithms without considerable tradeoffs in accuracy. This claim is supported by an algorithm descriptions and numerical illustrations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality Matoušek, J. Brandner, M. Duník, J. Punčochář, I. Signal Processing This paper deals with the state estimation of stochastic systems and examines the possible employment of tensor decompositions in grid-based filtering routines, in particular, the tensor-train decomposition. The aim is to show that these techniques can lead to a massive reduction in both the computational and storage complexity of grid-based filtering algorithms without considerable tradeoffs in accuracy. This claim is supported by an algorithm descriptions and numerical illustrations. |
| title | Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.07942 |