Convex preserving scattered data interpolation using bivariate C1 cubic splines

We use bivariate C1 cubic splines to deal with convexity preserving scattered data interpolation problem. Using a necessary and sufficient condition on Bernstein–Bézier polynomials, we set the convexity-preserving interpolation problem into a quadratically constraint quadratic programming problem. We show the existence of convexity preserving interpolatory surfaces under certain conditions on the data. That is, under certain conditions on the data, there always exists a convexity preservation C1 cubic spline interpolation if the triangulation is refined sufficiently many times. We then replace the quadratical constrains by three linear constrains and formulate the problem into linearly constraint quadratic programming problems in order to be able to solve it easily. Certainly, the existence of convexity preserving interpolatory surfaces is equivalent to the feasibility of the linear constrains. We present a numerical experiment to test which of these three linear constraints performs the best.