Surface fitting using convex tensor-product splines
B Jüttler - Journal of computational and applied mathematics, 1997 - Elsevier
Journal of computational and applied mathematics, 1997•Elsevier
A construction of linear sufficient convexity conditions for polynomial tensor-product spline
functions is presented. As the main new feature of this construction, the obtained conditions
are asymptotically necessary: increasing the number of linear inequalities in a suitable
manner adapts them to any finite set of strongly convex spline surfaces. Based on the linear
constraints we formulate least-squares approximation of scattered data by spline surfaces as
a quadratic programming problem.
functions is presented. As the main new feature of this construction, the obtained conditions
are asymptotically necessary: increasing the number of linear inequalities in a suitable
manner adapts them to any finite set of strongly convex spline surfaces. Based on the linear
constraints we formulate least-squares approximation of scattered data by spline surfaces as
a quadratic programming problem.
A construction of linear sufficient convexity conditions for polynomial tensor-product spline functions is presented. As the main new feature of this construction, the obtained conditions are asymptotically necessary: increasing the number of linear inequalities in a suitable manner adapts them to any finite set of strongly convex spline surfaces. Based on the linear constraints we formulate least-squares approximation of scattered data by spline surfaces as a quadratic programming problem.
Elsevier