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Symmetry
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
DBLP
Article . 2023
Data sources: DBLP
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Markov Moment Problems on Special Closed Subsets of Rn

Authors: Cristian Octav Olteanu;

Markov Moment Problems on Special Closed Subsets of Rn

Abstract

First, this paper provides characterizing the existence and uniqueness of the linear operator solution T for large classes of full Markov moment problems on closed subsets F of Rn. One uses approximation by special nonnegative polynomials. The case when F is compact is studied. Then the cases when F=Rn and F=R+n are under attention. Here, the main findings consist in proving and applying the density of special polynomials, which are sums of squares, in the positive cone of Lν1(Rn), and respectively of Lν1(R+n), for a large class of measures ν. One solves the important difficulty created by the fact that on Rn, n≥2, there exist nonnegative polynomials which are not expressible in terms of sums of squares. This is the second aim of the paper. On the other hand, two types of symmetry are outlined. Both these symmetry properties appear naturally from the thematic mentioned above. This is the third aim of the paper. They lead to new statements, illustrated in corollaries, and supported by a few examples.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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