Online embedding of metrics
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Abstract (arXiv)
We study deterministic online embeddings of metrics spaces into normed spaces and into trees against an adaptive adversary. Main results include a polynomial lower bound on the (multiplicative) distortion of embedding into Euclidean spaces, a tight exponential upper bound on embedding into the line, and a (1+ε)-distortion embedding in ℓ_∞ of a suitably high dimension.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2023.
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Citing papers per year: 2023: 1.
1 paper.