2010
DOI: 10.1007/s00209-010-0695-4
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Remarks on non-compact gradient Ricci solitons

Abstract: In this paper we show how techniques coming from stochastic analysis, such as stochastic completeness (in the form of the weak maximum principle at infinity), parabolicity and L p -Liouville type results for the weighted Laplacian associated to the potential may be used to obtain triviality, rigidity results, and scalar curvature estimates for gradient Ricci solitons under L p conditions on the relevant quantities.

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Cited by 156 publications
(166 citation statements)
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“…This has been already known by Ni [30] (see the proof of Corollary 1.3 in Ni [30]). As was pointed out by the referee in April 2011, it has been also known by [34] and [35].…”
Section: Moreover If There Exists a Constant M ≥ N Such Thatmentioning
confidence: 88%
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“…This has been already known by Ni [30] (see the proof of Corollary 1.3 in Ni [30]). As was pointed out by the referee in April 2011, it has been also known by [34] and [35].…”
Section: Moreover If There Exists a Constant M ≥ N Such Thatmentioning
confidence: 88%
“…See Theorem 1 in[35] or Proposition 2 in[34]. Actually, this has been proved in the proof of Corollary 1.3 in[30].…”
mentioning
confidence: 95%
“…Let u ∈ C 2 (M) be a function with u * = sup M u < +∞ and (x k ) a sequence of points on M satisfying (2.1)(i) and (2.1)(iii). It is shown in the proof of [13,Theorem 1.9…”
Section: Theorem 22 Let (M N G) Be An N-dimensional Complete Noncmentioning
confidence: 92%
“…We recall that a Riemannian manifold (M, g) is said to be stochastically complete if, for some (and hence for any) (x, t) ∈ M × (0, +∞) one has M p(x, y, t) dy = 1, where p(x, y, t) is the minimal heat kernel of the Laplace-Beltrami operator . It is well known that any Riemannian manifold satisfying the Omori-Yau maximum principle is stochastically complete [13]. In fact, stochastic completeness is equivalent to the Riemannian manifold to satisfy the weak maximum principle, that is (2.1)(i) and (2.1)(iii).…”
Section: Theorem 22 Let (M N G) Be An N-dimensional Complete Noncmentioning
confidence: 97%
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