Exploiting the Structure in Tensor Decompositions for Matrix Multiplication
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917518632812544 |
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| author | Kauers, Manuel Moosbauer, Jakob Wood, Isaac |
| author_facet | Kauers, Manuel Moosbauer, Jakob Wood, Isaac |
| contents | We present a new algorithm for fast matrix multiplication using tensor decompositions which have special features. Thanks to these features we obtain exponents lower than what the rank of the tensor decomposition suggests. In particular for $6\times 6$ matrix multiplication we reduce the exponent of the recent algorithm by Moosbauer and Poole from $2.8075$ to $2.8019$, while retaining a reasonable leading coefficient. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11041 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploiting the Structure in Tensor Decompositions for Matrix Multiplication Kauers, Manuel Moosbauer, Jakob Wood, Isaac Symbolic Computation We present a new algorithm for fast matrix multiplication using tensor decompositions which have special features. Thanks to these features we obtain exponents lower than what the rank of the tensor decomposition suggests. In particular for $6\times 6$ matrix multiplication we reduce the exponent of the recent algorithm by Moosbauer and Poole from $2.8075$ to $2.8019$, while retaining a reasonable leading coefficient. |
| title | Exploiting the Structure in Tensor Decompositions for Matrix Multiplication |
| topic | Symbolic Computation |
| url | https://arxiv.org/abs/2602.11041 |