because such polynomials can achieve good smoothing both in the central and in the near-boundary regions of a kernel, and therefore they can be confidently used in smoothing both at the internal and at the near-boundary data points of a sampled domain. In order to avoid ill-conditioning when solving the least-squares problem, "p" < "m" and "q" < "n". For a software which calculates the two-dimensional coefficients and for a database of such C's, see the section on multi-dimensional convolution coefficients, below.