Let A = (a ij ) ∊ M n (ℝ) be an n by n symmetric stochastic matrix. For p ∊ [1, ∞) and a metric space (X, d X ), let γ(A, d p x ) be the infimum over those γ ∊ (0,∞] for which every x 1 , . . . , x n ∊ X satisfy Thus γ (A, d p x ) measures the magnitude of the nonlinear spectral gap of the matrix A with respect to the kernel d p X : X × X →[0,∞). We study pairs of metric spaces (X, d X ) and (Y, d Y ) for which there exists Ψ: (0,∞)→(0,∞) such that γ (A, d p X ) ≤Ψ (A, d p Y ) for every symmetric stochastic A ∊ Mn(ℝ) with (A, d p Y ) < ∞. When Ψ is linear a complete geometric characterization is obtained. Our estimates on nonlinear spectral gaps yield new embeddability results as well as new nonembeddability results. For example, it is shown that if n ∊ ℕ and p ∊ (2,∞) then for every f 1 , . . . , f n ∊ L p there exist x 1 , . . . , x n ∊ L 2 such that and This statement is impossible for p ∊ [1, 2), and the asymptotic dependence on p in (0.1) is sharp. We also obtain the best known lower bound on the L p distortion of Ramanujan graphs, improving over the work of Matoušek. Links to Bourgain-Milman-Wolfson type and a conjectural nonlinear Maurey-Pisier theorem are studied.
Contents
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Open AccessComparison of Metric Spectral GapsFebruary 28, 2014
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Open AccessThe Boundary at Infinity of a Rough CAT(0) SpaceApril 23, 2014
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April 24, 2014
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Open AccessGeodesics in Asymmetric Metric SpacesMay 17, 2014
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July 26, 2014
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July 26, 2014
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Open AccessInvertible Carnot GroupsSeptember 19, 2014
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November 28, 2014
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November 28, 2014
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November 28, 2014
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November 28, 2014
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Open AccessUniformly Convex Metric SpacesDecember 10, 2014