MKL - General Polynomial Fitting function?

The General Polynomial Fitting Function in MKL

Hi,

I’m looking for a general Polynomial Fitting function in MKL, which is similar to Polyfit function from Matlab, OptiVec and Armadillo.  This function uses two set of input data X and Y to determine the coefficients a_i of a polynomial:

P_i = a₀ + a₁X_i + a₂X_i² ... a_nX_iⁿ

Where,

χ² = sum( 1/σ_i² * (P_i - Y_i)² );

Is minimized.

I have searched MKL Data Fitting Library and found that all of routines are Spline-based.  In MKL LAPACK library, I found some routines the Least Squares ones.

I understand that,

1. as the order of the polynomial increases, Spline-based methods are preferable over polynomial interpolation because its interpolation error increases
2. when the size of X and Y > the order of the polynomial increases, the Least Squares fit will be used

What I need, however,  is a general Polynomial Fitting function described above.  Is there such function and where can I find it in MKL (or IPP)?