Spline interpolation matches prescribed function values at distinct sites by an element of a finite-dimensional spline space. In a B-spline basis its existence and uniqueness for all data are equivalent to invertibility of the B-spline collocation matrix. Distinct ordered sites satisfying the Schoenberg–Whitney theorem give this condition.
For a fixed spline space and admissible interpolation sites, this linear operator sends a continuous function to its unique interpolating spline. Here is the vector of sampled values and is the B-spline collocation matrix. It reproduces every spline in its range. Its sensitivity to perturbing the data is quantified by the B-spline interpolation operator norm.
For unit-spaced order-three B-splines sampled at support midpoints , the B-spline collocation matrix has diagonal and neighboring entries . Conjugating by alternating diagonal signs reduces its inverse absolute values to the nonnegative inverse of . With , the absolute row sums are . This follows by solving with . The maximum occurs at the middle row or rows and tends to two as grows. Nonnegativity and subpartition of unity of the B-splines transfer this bound to the spline interpolation operator.
For distinct interpolation sites and an invertible B-spline collocation matrix, the interpolant is , where samples data and . Sampling and basis synthesis have operator norms at most one in the supremum norm. For the reverse inequality, realize the signs of a maximal absolute row sum of as values of a continuous function of norm one, and apply uniform-norm stability of a B-spline basis. This yields both displayed bounds.
For the degree-two Cardinal B-spline basis on knots , sampling at gives the upper-bidiagonal B-spline collocation matrix , where is the one-step upper shift. Since , and its maximum absolute row sum is . The B-spline interpolation operator norm estimate with the order-three stability constant gives the displayed linear upper and lower bounds. The lower bound proves failure of uniform boundedness.
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