2020
DOI: 10.1007/s12220-020-00559-z
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Representations for the Bloch Type Semi-norm of Fréchet Differentiable Mappings

Abstract: In this paper we give some results concerning Fréchet differentiable mappings between domains in normed spaces with controlled growth. The results are mainly motivated by Pavlović's equality for the Bloch semi-norm of continuously differentiable mappings in the Bloch class on the unit ball of the Euclidean space as well as the very recent Jocić's generalization of this result.

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Cited by 5 publications
(12 citation statements)
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“…(see [10] for a similar result in a general setting, i.e., for Fréchet differentiable mappings between vector spaces with norm). In the sequel we will consider a generalization of the Pavlović result for holomorphic functions on the manifold M .…”
Section: Distance Decreasing Property Of the Real Part And The Modulusmentioning
confidence: 78%
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“…(see [10] for a similar result in a general setting, i.e., for Fréchet differentiable mappings between vector spaces with norm). In the sequel we will consider a generalization of the Pavlović result for holomorphic functions on the manifold M .…”
Section: Distance Decreasing Property Of the Real Part And The Modulusmentioning
confidence: 78%
“…|ζ−η| = ω(ζ) (for this equality in a general setting, i.e., for vector spaces with norm, we refer to [10]), from the last inequality we obtain (2.2).…”
Section: Distance Decreasing Property Of the Real Part And The Modulusmentioning
confidence: 94%
“…Here is the slight improvement on [14, Theorem 1.5], which we will prove in Subsection 5.10. We let λ1(X)$\lambda _1(X)$ denote the smallest nonzero eigenvalue of the Laplacian acting on L2$L^2$ functions on X$X$.…”
Section: Introductionmentioning
confidence: 81%
“…Remark Theorem 1.6 is slightly stronger than [14, Theorem 1.5] as we now explain. In their result, they first choose ε$\varepsilon$ and then choose g$g$ subject to the inequality gfalse(1+εfalse)/ε$g \geqslant (1+\epsilon )/\epsilon$, or equivalently ε1g1$\varepsilon \geqslant \frac{1}{g-1}$.…”
Section: Introductionmentioning
confidence: 86%
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