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dc.contributor.authorDinh Dũng-
dc.date.accessioned2016-05-27T01:51:29Z-
dc.date.available2016-05-27T01:51:29Z-
dc.date.issued2012-
dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/10983-
dc.description.abstractWe study optimal algorithms in adaptive continuous sampling recovery of smooth functions defined on the unit d-cube Id≔[0,1]d. Functions to be recovered are in Besov space . The recovery error is measured in the quasi-norm ‖⋅‖q of . For a set A⊂Lq, we define a sampling algorithm of recovery with the free choice of sample points and recovering functions from A as follows. For each , we choose n sample points which define n sampled values of f. Based on these sample points and sampled values, we choose a function from A for recovering f. The choice of n sample points and a recovering function from A for each defines an n-sampling algorithm . We suggest a new approach to investigate the optimal adaptive sampling recovery by in the sense of continuous non-linear n-widths which is related to n-term approximation. If Φ={φk}k∈K is a family of functions in Lq, let Σn(Φ) be the non-linear set of linear combinations of n free terms from Φ. Denote by G the set of all families Φ such that the intersection of Φ with any finite dimensional subspace in Lq is a finite set, and by the set of all continuous mappings from into Lq. We define the quantity For 0<p,q,θ≤∞ and α>d/p, we prove the asymptotic orderen_US
dc.publisherJournal of Approximation Theoryen_US
dc.subjectAdaptive sampling recovery; Continuous n-sampling algorithm; B-spline quasi-interpolant representation; Besov spaceen_US
dc.titleContinuous algorithms in adaptive sampling recoveryen_US
dc.typeBooken_US
dc.typeBook chapteren_US
dc.typeDataseten_US
Appears in Collections:ITI - Papers


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  • Full metadata record
    DC FieldValueLanguage
    dc.contributor.authorDinh Dũng-
    dc.date.accessioned2016-05-27T01:51:29Z-
    dc.date.available2016-05-27T01:51:29Z-
    dc.date.issued2012-
    dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/10983-
    dc.description.abstractWe study optimal algorithms in adaptive continuous sampling recovery of smooth functions defined on the unit d-cube Id≔[0,1]d. Functions to be recovered are in Besov space . The recovery error is measured in the quasi-norm ‖⋅‖q of . For a set A⊂Lq, we define a sampling algorithm of recovery with the free choice of sample points and recovering functions from A as follows. For each , we choose n sample points which define n sampled values of f. Based on these sample points and sampled values, we choose a function from A for recovering f. The choice of n sample points and a recovering function from A for each defines an n-sampling algorithm . We suggest a new approach to investigate the optimal adaptive sampling recovery by in the sense of continuous non-linear n-widths which is related to n-term approximation. If Φ={φk}k∈K is a family of functions in Lq, let Σn(Φ) be the non-linear set of linear combinations of n free terms from Φ. Denote by G the set of all families Φ such that the intersection of Φ with any finite dimensional subspace in Lq is a finite set, and by the set of all continuous mappings from into Lq. We define the quantity For 0<p,q,θ≤∞ and α>d/p, we prove the asymptotic orderen_US
    dc.publisherJournal of Approximation Theoryen_US
    dc.subjectAdaptive sampling recovery; Continuous n-sampling algorithm; B-spline quasi-interpolant representation; Besov spaceen_US
    dc.titleContinuous algorithms in adaptive sampling recoveryen_US
    dc.typeBooken_US
    dc.typeBook chapteren_US
    dc.typeDataseten_US
    Appears in Collections:ITI - Papers


    Thumbnail
  • (25)Continuous algorithms in adaptive sampling recovery.pdf
    • Size : 298,4 kB

    • Format : Adobe PDF

    • View : 
    • Download :