Interpolation, Schur Functions and Moment Problems 2006
DOI: 10.1007/3-7643-7547-7_4
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A Truncated Matricial Moment Problem on a Finite Interval

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Cited by 24 publications
(13 citation statements)
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“…It will turn out that the case of the matricial Hausdorff moment problem is much more difficult in comparison with the matricial problems named after Hamburger and Stieltjes. This phenomenon could be already observed in the discussion of the so-called non-degenerate case, where the Hamburger problem (see [14,41]) and the Stieltjes problem (see [15,16,18,19]) were studied considerably earlier than the Hausdorff problem (see [10,11]). The main reason for the greater complexity of the Hausdorff moment problem is caused by the fact that the localization of the measure in a prescribed compact interval of the real axis requires to satisfy simultaneously more conditions.…”
Section: Introductionmentioning
confidence: 61%
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“…It will turn out that the case of the matricial Hausdorff moment problem is much more difficult in comparison with the matricial problems named after Hamburger and Stieltjes. This phenomenon could be already observed in the discussion of the so-called non-degenerate case, where the Hamburger problem (see [14,41]) and the Stieltjes problem (see [15,16,18,19]) were studied considerably earlier than the Hausdorff problem (see [10,11]). The main reason for the greater complexity of the Hausdorff moment problem is caused by the fact that the localization of the measure in a prescribed compact interval of the real axis requires to satisfy simultaneously more conditions.…”
Section: Introductionmentioning
confidence: 61%
“…Continuing the work done in [5,10,11] A. E. Choque-Rivero [6][7][8][9] investigated further aspects of the non-degenerate truncated matricial Hausdorff moment problem. As in [5,10,11] he distinguished between the case of an odd or even number of prescribed matricial moments. The approach used in [5,10,11] is based on V. P. Potapov's method of Fundamental Matrix Inequalities.…”
Section: Introductionmentioning
confidence: 99%
“…For each z ∈ Π + , Theorem 6.3 yields \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$F(z)=\int _\mathbb {R}\frac{1}{t-z}\sigma _{F}(\mbox{d}t)$\end{document}. Then F belongs to \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\mathcal {R}_{q}(\Pi _+)$\end{document} and fulfills \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\sup _{y\in [1,+\infty )}y\Vert F(\mathrm{i}y)\Vert _{\mathrm{S}}<+\infty \ ($\end{document}see, e. g., 7, Theorem 8.7] for each y ∈ [1, +∞)).…”
Section: The Class \Documentclass{article}\usepackage{amssymb}\begin{mentioning
confidence: 99%
“…Conversely, we now consider an arbirary \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$F\in \mathcal {R}_{q}(\Pi _+)$\end{document} which satisfies \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\sup _{y\in [1,+\infty )}y\Vert F(\mathrm{i}y)\Vert _{\mathrm{S}}<+\infty$\end{document}. Then7, Theorem 8.7] yields the existence of a \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\sigma \in \mathcal {M}_\ge ^{q}(\mathbb {R})$\end{document} such that for each z ∈ Π + . Thus, Theorem 6.3 shows that \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$F\in \mathcal {R}_{q}^{[0]}(\Pi _+)$\end{document} and γ F = 0 q × q , i. e., that \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$F\in \mathcal {R}_{0,q}(\Pi _+)$\end{document}.…”
Section: The Class \Documentclass{article}\usepackage{amssymb}\begin{mentioning
confidence: 99%
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