2014
DOI: 10.1016/j.aim.2013.12.017
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Restriction estimates for space curves with respect to general measures

Abstract: Abstract. In this paper we consider adjoint restriction estimates for space curves with respect to general measures and obtain optimal estimates when the curves satisfy a finite type condition. The argument here is new in that it doesn't rely on the offspring curve method, which has been extensively used in the previous works. Our work was inspired by the recent argument due to Bourgain and Guth which was used to deduce linear restriction estimates from multilinear estimates for hypersurfaces.

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Cited by 20 publications
(50 citation statements)
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“…As mentioned in the above, for d = 2 the optimal result including the end line cases was obtained in [19] and [9]. For d ≥ 3, the sufficiency part of Theorem 1.1 follows from Theorem 1.2 below which is a special case of [20,Theorem 1.1]. In [20], the estimates with respect to general α-dimensional measure (see Definition 3.1) were obtained and those results are sharp in that there are α-dimensional measures for which the estimate fails outside of the asserted region.…”
Section: Introductionmentioning
confidence: 88%
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“…As mentioned in the above, for d = 2 the optimal result including the end line cases was obtained in [19] and [9]. For d ≥ 3, the sufficiency part of Theorem 1.1 follows from Theorem 1.2 below which is a special case of [20,Theorem 1.1]. In [20], the estimates with respect to general α-dimensional measure (see Definition 3.1) were obtained and those results are sharp in that there are α-dimensional measures for which the estimate fails outside of the asserted region.…”
Section: Introductionmentioning
confidence: 88%
“…For the purpose, we actually prove more than what we need by replacing the (d − 1)-dimensional measure σ ℓ with general α-dimensional measure. We basically follow the argument in [20]. For ν ∈ R we denote by ⌈ν⌉ the smallest integer which is not less than ν.…”
Section: Proof Of Theorem 14 and Proposition 15mentioning
confidence: 99%
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