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    Korovkin-type results and doubly stochastic transformations over Euclidean Jordan algebras

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    2209.13303.pdf (227.0Kb)
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    https://arxiv.org/abs/2209.13303
    Permanent Link
    https://doi.org/10.48550/arXiv.2209.13303
    http://hdl.handle.net/11603/26219
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    • UMBC Faculty Collection
    • UMBC Mathematics and Statistics Department
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    Author/Creator
    Gowda, Muddappa
    Date
    2022-09-27
    Type of Work
    19 pages
    Text
    journal articles
    preprints
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Abstract
    A well-known theorem of Korovkin asserts that if {Tk} is a sequence of positive linear transformations on C[a, b] such that Tk(h) → h (in the sup-norm on C[a, b]) for all h ∈ {1, φ, φ2}, where φ(t) = t on [a, b], then Tk(h) → h for all h ∈ C[a, b]. In particular, if T is a positive linear transformation on C[a, b] such that T (h) = h for all h ∈ {1, φ, φ2}, then T is the Identity transformation. In this paper, we present some analogs of these results over Euclidean Jordan algebras. We show that if T is a positive linear transformation on a Euclidean Jordan algebra V such that T (h) = h for all h ∈ {e, p, p2}, where e is the unit element in V and p is an element of V with distinct eigenvalues, then T = T ∗ = I (the Identity transformation) on the span of the Jordan frame corresponding to the spectral decomposition of p; consequently, if a positive linear transformation coincides with the Identity transformation on a Jordan frame, then it is doubly stochastic. We also present sequential and weak-majorization versions.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.