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Ricci curvature, entropy and optimal transport

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This chapter comprises the lecture notes on the interplay between optimal transport and Riemannian geometry. On a Riemannian manifold, the convexity of entropy along optimal transport in the space of probability measures characterizes lower bounds of the Ricci curvature. We then discuss geometric properties of general metric measure spaces satisfying this convexity condition. Introduction This chapter is extended notes based on the author's lecture series at the summer school at Université Joseph Fourier, Grenoble: “Optimal Transportation: Theory and Applications.” The aim of these five lectures (corresponding to Sections 7.3–7.7) was to review the recent impressive development on the interplay between optimal transport theory and Riemannian geometry. Ricci curvature and entropy are the key ingredients. See [Lo2] for a survey in the same spirit with a slightly different selection of topics. Optimal transport theory is concerned with the behavior of transport between two probability measures in a metric space. We say that such transport is optimal if it minimizes a certain cost function typically defined from the distance of the metric space. Optimal transport naturally inherits the geometric structure of the underlying space; in particular Ricci curvature plays a crucial role for describing optimal transport in Riemannian manifolds. In fact, optimal transport is always performed along geodesics, and we obtain Jacobi fields as their variational vector fields. The behavior of these Jacobi fields is controlled by the Ricci curvature as is usual in comparison geometry. In this way, a lower Ricci curvature bound turns out to be equivalent to a certain convexity property of entropy in the space of probability measures.

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... We conclude this brief review with a statement of the famous Bishop-Gromov Comparison Theorem. This is well known and can be found in any book on comparison geometry; we refer to the notes in [29] for an excellent introduction to the basic theory at an elementary level: Theorem 4 (Bishop-Gromov). Let (M, g) a d -dimensional Riemannian manifold such that the scalar curvature is bounded below by −K for some K > 0. For any point x ∈ M , let B r (x) denote the metric ball around x in M with radius r > 0. For any 0 < r < R, we have ...
... Proofs of both results are available in the third section of [29]. ...
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Preprint
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Thesis
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... See [St2, Proposition 2.1, Theorem 2.3] (and[Vi, Theorem 30.7],[Oh3, Theorem 6.1] as well for the case of N = ∞) for the precise statements of the Brunn-Minkowski inequality and the Bishop-Gromov volume comparison. ...
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Chapter
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We define the coarse Ricci curvature of metric spaces in terms of how much small balls are closer (in Wasserstein transportation distance) than their centers are. This definition naturally extends to any Markov chain on a metric space. For a Riemannian manifold this gives back, after scaling, the value of Ricci curvature of a tangent vector. Examples of positively curved spaces for this definition include the discrete cube and discrete versions of the Ornstein–Uhlenbeck process. Moreover this generalization is consistent with the Bakry–Émery Ricci curvature for Brownian motion with a drift on a Riemannian manifold.Positive Ricci curvature is shown to imply a spectral gap, a Lévy–Gromov–like Gaussian concentration theorem and a kind of modified logarithmic Sobolev inequality. The bounds obtained are sharp in a variety of examples.
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We introduce and study rough (approximate) lower curvature bounds for discrete spaces and for graphs. This notion agrees with the one introduced in [J. Lott, C. Villani, Ricci curvature for metric-measure spaces via optimal transport, Ann. of Math. 169 (2009), in press] and [K.T. Sturm, On the geometry of metric measure spaces. I, Acta Math. 196 (2006) 65–131], in the sense that the metric measure space which is approximated by a sequence of discrete spaces with rough curvature ⩾K will have curvature ⩾K in the sense of [J. Lott, C. Villani, Ricci curvature for metric-measure spaces via optimal transport, Ann. of Math. 169 (2009), in press; K.T. Sturm, On the geometry of metric measure spaces. I, Acta Math. 196 (2006) 65–131]. Moreover, in the converse direction, discretizations of metric measure spaces with curvature ⩾K will have rough curvature ⩾K. We apply our results to concrete examples of homogeneous planar graphs.
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310 ZHONGMIN Page 6. both arguments in [C1] and [GP] were carried out using Toponogov's comparison theorem. However, we can show that, in a manifold