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Embedding between metric spaces is a very powerful algorithmic tool and has been used for finding good approximation algorithms for several problems. In ...
Dec 14, 2015 · Goemans showed that any n points x_1, \dotsc x_n in d-dimensions satisfying \ell_2^2 triangle inequalities can be embedded into \ell_{1}, with worst-case ...
Missing: ℓ22 ℓ1.
Dec 13, 2016 · Goemans showed that any n points x_1,…, x_n in d-dimensions satisfying l_2^2 triangle inequalities can be embedded into l_{1}, ...
Missing: ℓ22 | Show results with:ℓ22
Dec 14, 2015 · These results come from a semi-definite programming. (SDP) relaxation to produce solutions in the ℓ2-squared metric space, i.e., a set of ...
Missing: ℓ22 | Show results with:ℓ22
Embedding approximately low-dimensional ℓ22 metrics into ℓ1 · Approximating Sparsest Cut in Low Rank Graphs via Embeddings from Approximately Low-Dimensional ...
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Embedding approximately low-dimensional $\ell_2^2$ metrics ...
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Sep 11, 2024 · Our embedding gives an approximation algorithm for the \sparsestcut problem on low threshold-rank graphs, where earlier work was inspired by ...
Missing: ℓ22 | Show results with:ℓ22
dblp: Embedding approximately low-dimensional ℓ<sub>2<sup>2 ...
192.76.146.204 › corr › DeshpandeHV15
Amit Deshpande, Prahladh Harsha, Rakesh Venkat: Embedding approximately low-dimensional ℓ22 metrics into ℓ1. CoRR abs/1512.04170 (2015).
[PDF] Approximating Sparsest Cut in Low Rank Graphs via ...
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Approximating Sparsest Cut in Low Rank Graphs via Embeddings from Approximately Low-Dimensional ... Embedding approximately low-dimensional ℓ22 metrics into ℓ1.
In this paper we give approximation and fixed-parameter tractable (FPT) algorithms for minimum-distortion embeddings into the metric of a subdivision of some ...
Missing: ℓ1. | Show results with:ℓ1.
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