Abstract
Implicit and explicit characterizations of the solutions to the following constrained best interpolation problem
are presented. Here,T is a densely-defined, closed, linear mapping from a Hilbert spaceX to a Hilbert spaceY, A: X→Z is a continuous, linear mapping withZ a locally, convex linear topological space,C is a closed, convex set in the domain domT ofT, andd∈AC. For the case in whichC is a closed, convex cone, it is shown that the constrained best interpolation problem can generally be solved by finding the saddle points of a saddle function on the whole space, and, if the explicit characterization is applicable, then solving this problem is equivalent to solving an unconstrained minimization problem for a convex function.
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Supported by a research assistantship from the National Science Foundation under Grant No. DMS-9000053.
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Zhao, K. Best interpolation in seminorm with convex constraints. Numer Algor 9, 141–156 (1995). https://doi.org/10.1007/BF02143931
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DOI: https://doi.org/10.1007/BF02143931