Multivariate moment problems: Geometry and indeterminateness
Author(s) -
Mihai Putinar,
Claus Scheiderer
Publication year - 2009
Publication title -
annali scuola normale superiore - classe di scienze
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.444
H-Index - 37
eISSN - 2036-2145
pISSN - 0391-173X
DOI - 10.2422/2036-2145.2006.2.01
Subject(s) - mathematics , sequence (biology) , moment (physics) , multivariate statistics , univariate , generalization , uniqueness , morphism , measure (data warehouse) , dimension (graph theory) , lebesgue measure , moment problem , sequence space , lebesgue integration , discrete mathematics , pure mathematics , mathematical analysis , computer science , statistics , banach space , physics , classical mechanics , genetics , database , principle of maximum entropy , biology
The most accurate determinateness criteria for the multivariate mo- ment problem require the density of polynomials in a weighted Lebesgue space of a generic representing measure. We propose a relaxation of such a criterion to the approximation of a single function, and based on this condition we analyze the impact of the geometry of the support on the uniqueness of the representing mea- sure. In particular we show that a multivariate moment sequence is determinate if its support has dimension one and is virtually compact; a generalization to higher dimensions is also given. Among the one-dimensional sets which are not virtually compact, we show that at least a large subclass supports indeterminate moment sequences. Moreover, we prove that the determinateness of a moment sequence is implied by the same condition (in general easier to verify) of the push-forward sequence via finite morphisms.
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