5,282 research outputs found

    Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms

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    We present a technical survey on the state of the art approaches in data reduction and the coreset framework. These include geometric decompositions, gradient methods, random sampling, sketching and random projections. We further outline their importance for the design of streaming algorithms and give a brief overview on lower bounding techniques

    Double Bubbles Minimize

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    The classical isoperimetric inequality in R^3 states that the surface of smallest area enclosing a given volume is a sphere. We show that the least area surface enclosing two equal volumes is a double bubble, a surface made of two pieces of round spheres separated by a flat disk, meeting along a single circle at an angle of 120 degrees.Comment: 57 pages, 32 figures. Includes the complete code for a C++ program as described in the article. You can obtain this code by viewing the source of this articl

    Isoperimetric and stable sets for log-concave perturbations of Gaussian measures

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    Let Ω\Omega be an open half-space or slab in Rn+1\mathbb{R}^{n+1} endowed with a perturbation of the Gaussian measure of the form f(p):=exp(ω(p)cp2)f(p):=\exp(\omega(p)-c|p|^2), where c>0c>0 and ω\omega is a smooth concave function depending only on the signed distance from the linear hyperplane parallel to Ω\partial\Omega. In this work we follow a variational approach to show that half-spaces perpendicular to Ω\partial\Omega uniquely minimize the weighted perimeter in Ω\Omega among sets enclosing the same weighted volume. The main ingredient of the proof is the characterization of half-spaces parallel or perpendicular to Ω\partial\Omega as the unique stable sets with small singular set and null weighted capacity. Our methods also apply for Ω=Rn+1\Omega=\mathbb{R}^{n+1}, which produces in particular the classification of stable sets in Gauss space and a new proof of the Gaussian isoperimetric inequality. Finally, we use optimal transport to study the weighted minimizers when the perturbation term ω\omega is concave and possibly non-smooth.Comment: final version, to appear in Analysis and Geometry in Metric Space
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