2023
DOI: 10.4064/cm8796-7-2022
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A note on tempered measures

Abstract: The relation between tempered distributions and measures is analysed and clarified. While this is straightforward for positive measures, it is surprisingly subtle for signed or complex measures.

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Cited by 3 publications
(4 citation statements)
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“…It is easy to see that this is equivalent to μ:Cc(double-struckRd)double-struckC$\mu : C_{\mathsf {c}}^\infty ({\mathbb {R}}^d) \rightarrow {\mathbb {C}}$ being continuous with respect to the Schwartz topology induced by the embedding Cc(double-struckRd)sans-serifS(double-struckRd)$C_{\mathsf {c}}^\infty ({\mathbb {R}}^d) \hookrightarrow {\textsf {S}}({\mathbb {R}}^d)$ (see, e.g., [1]). Remark Let us recall that a measure μ is called (1) slowly increasing if there exists a polynomial Pdouble-struckRfalse[X1,,Xdfalse]$P \in {\mathbb {R}}[X_1,\ldots ,X_d]$ such that double-struckRd11+false|Pfalse(xfalse)false|0.33emnormald|μ|(x)badbreak<0.16em,$$\begin{equation*} \int _{{\mathbb {R}}^d} \frac{1}{1+|P(x)|}\ \mbox{d}|\mu | (x) &lt; \infty \,, \end{equation*}$$ (2) strongly tempered if false|μfalse|$|\mu |$ is tempered. A measure μ is strongly tempered if and only if it is slowly increasing [8, Theorem 2.6]. In this case, μ is tempered, see, for example, [1, p. 47] or [8, Lemma 2.3].…”
Section: Preliminariesmentioning
confidence: 99%
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“…It is easy to see that this is equivalent to μ:Cc(double-struckRd)double-struckC$\mu : C_{\mathsf {c}}^\infty ({\mathbb {R}}^d) \rightarrow {\mathbb {C}}$ being continuous with respect to the Schwartz topology induced by the embedding Cc(double-struckRd)sans-serifS(double-struckRd)$C_{\mathsf {c}}^\infty ({\mathbb {R}}^d) \hookrightarrow {\textsf {S}}({\mathbb {R}}^d)$ (see, e.g., [1]). Remark Let us recall that a measure μ is called (1) slowly increasing if there exists a polynomial Pdouble-struckRfalse[X1,,Xdfalse]$P \in {\mathbb {R}}[X_1,\ldots ,X_d]$ such that double-struckRd11+false|Pfalse(xfalse)false|0.33emnormald|μ|(x)badbreak<0.16em,$$\begin{equation*} \int _{{\mathbb {R}}^d} \frac{1}{1+|P(x)|}\ \mbox{d}|\mu | (x) &lt; \infty \,, \end{equation*}$$ (2) strongly tempered if false|μfalse|$|\mu |$ is tempered. A measure μ is strongly tempered if and only if it is slowly increasing [8, Theorem 2.6]. In this case, μ is tempered, see, for example, [1, p. 47] or [8, Lemma 2.3].…”
Section: Preliminariesmentioning
confidence: 99%
“…Remark Let us recall that a measure μ is called (1) slowly increasing if there exists a polynomial Pdouble-struckRfalse[X1,,Xdfalse]$P \in {\mathbb {R}}[X_1,\ldots ,X_d]$ such that double-struckRd11+false|Pfalse(xfalse)false|0.33emnormald|μ|(x)badbreak<0.16em,$$\begin{equation*} \int _{{\mathbb {R}}^d} \frac{1}{1+|P(x)|}\ \mbox{d}|\mu | (x) &lt; \infty \,, \end{equation*}$$ (2) strongly tempered if false|μfalse|$|\mu |$ is tempered. A measure μ is strongly tempered if and only if it is slowly increasing [8, Theorem 2.6]. In this case, μ is tempered, see, for example, [1, p. 47] or [8, Lemma 2.3]. The converse of the last claim holds only for positive measures: Any positive tempered measure is strongly tempered [27, p. 242] [13, Theorem 2.1], and there are examples of signed tempered measures, which are not strongly tempered [1, 8].…”
Section: Preliminariesmentioning
confidence: 99%
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