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Download lean/docs/098.md from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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- Download file 1.15 kB
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https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/docs/098.md
- Command line
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hf download hf://datasets/roseking/openAI-math-dataset/lean/docs/098.md
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curl -L -o 098.md https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/docs/098.md
1.15 kB
Compact counterexamples to bi-Lipschitz dimension reduction
The following describes the scope of the Lean formalization related to the following accompanying paper(s):
Scope
The formalized result gives a doubling subset of real $\ell_2$, with doubling constant at most $76800$, that admits no bi-Lipschitz embedding into any finite-dimensional Euclidean space at any finite distortion. A companion gives one universal doubling constant such that every infinite-dimensional real Banach space contains a compact doubling subset with no bi-Lipschitz embedding into any finite-dimensional normed space.
Comparator links
| Result | Comparator statement |
|---|---|
| Doubling Hilbert subset without finite-dimensional embedding | DoublingHilbert.lean |
| Compact counterexamples in infinite-dimensional Banach spaces | CompactBanach.lean |