Quadruple Approximable by Test Data on a Subset of a Real Inner Product Space
definitionAnalysisPDEdef:approximable-test-data-hilbert-2026aThe Hilbert-space analogue of approximability by test data: a point, value, gradient and Hessian that are matched, to within any prescribed accuracy, by a test function touching the given function from above or from below at a nearby point of a subset.
In the setting of Real Hilbert Spaces: Standing Notation and Background and Real Hilbert Spaces: Series, Products, Orthonormal Bases and Differential Calculus, let be a real inner product space, with its inner product , norm and distance as fixed there, and let be the set of bounded symmetric bilinear forms on , with its norm , as fixed there. The set is open in , since every open ball of is a subset of ; accordingly the class is defined, and a has at each a gradient and a Hessian . Local maxima and local minima relative to a subset of are as fixed there, and is the absolute value of a real number .
Let , let , let , let and let . For let denote the function from to whose value at is .
1. (From above)¶ The quadruple is approximable by test data from above for on if for every positive there exist and such that has a local maximum at relative to and
2. (From below)¶ The quadruple is approximable by test data from below for on if for every positive there exist and such that has a local minimum at relative to and the four displayed inequalities of clause 1 hold.
This is the counterpart on a real inner product space of Quadruple Approximable by Test-Function Data, and differs from it in two respects: the ambient space is rather than a Euclidean space, and the set carrying is arbitrary rather than open, the test functions being of class on all of .
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