Abstract
This paper deals with interpolation and approximation satisfying constraints. We consider approximation by conditionally positive definite functions in norms which are associated with the conditionally positive definite functions. The theory of reproducing kernels is used to transform the approximation problems to quadratic optimization problems. Then we can give the existence, characterization and uniqueness results for the solutions. The methods of optimization theory can be used in order to determine solutions.
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Strauss, H. Approximation with Constraints by Conditionally Positive Definite Functions. Numerical Algorithms 30, 185–198 (2002). https://doi.org/10.1023/A:1016023310994
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DOI: https://doi.org/10.1023/A:1016023310994