On applying the maximum volume principle to a basis selection problem in multivariate polynomial interpolation
This provides a practical criterion for basis selection in multivariate polynomial interpolation, which is important for numerical analysis and approximation theory.
The paper investigates the maximum volume principle for selecting polynomial basis functions to ensure well-defined and accurate multivariate polynomial interpolation. It proves that the Lebesgue constant is bounded by the reciprocal of the volume of the Vandermonde matrix, and numerical examples show the approach works well.
The maximum volume principle is investigated as a means to solve the following problem: Given a set of arbitrary interpolation nodes, how to choose a set of polynomial basis functions for which the Lagrange interpolation problem is well-defined with reasonable interpolation error? The interpolation error is controlled by the Lebesgue constant of multivariate polynomial interpolation and it is proven that the Lebesgue constant can effectively be bounded by the reciprocals of the volume (i.e., determinant in modulus) and the minimal singular value of the multidimensional Vandermonde matrix associated with the interpolation problem. This suggests that a large volume of the Vandermonde system can be used as an indicator of accuracy and stability of the resulting interpolating polynomial. Numerical examples demonstrate that the approach outlined in this paper works remarkably well in practical computations.