Computing minimal interpolants in C^(1,1)(mathbb{R}^d)
classification
🧮 math.NA
cs.DScs.NAmath.CA
keywords
mathbbminimalfunctiongivenmathrmnablaalgorithmscomputational
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We consider the following interpolation problem. Suppose one is given a finite set $E \subset \mathbb{R}^d$, a function $f: E \rightarrow \mathbb{R}$, and possibly the gradients of $f$ at the points of $E$. We want to interpolate the given information with a function $F \in C^{1,1}(\mathbb{R}^d)$ with the minimum possible value of $\mathrm{Lip} (\nabla F)$. We present practical, efficient algorithms for constructing an $F$ such that $\mathrm{Lip} (\nabla F)$ is minimal, or for less computational effort, within a small dimensionless constant of being minimal.
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