Hilbert Matrix in least squares polynomial regression

When approximating the polynomial x^2+3x+2 with a least squares polynomial of degree two in [0 1], the coefficient matrix of the resultant linear system is a 3x3 Hilbert Matrix. Vector b is as follows as generated by the approximation problem. I should get an approximating polynomial almost like the original one, but the coefficients are quite afar from 2, 3 and 1. I know Hilbert Matrices are ill-conditioned. What can I do? (I am using where n is the degree of the approximating polynomial, is the function to be approximated and )

A=[1 1/2 1/3;1/2 1/3 1/4;1/3 1/4 1/5];
b=[16/3; 9/4; 97/60];
A\b
ans =
15.5000
-51.000
46.000

Answer 1

[John D'Errico]

Is A really a hilbert matrix? LOOK CAREFULLY AT WHAT YOU WRITE!!!!!!!!!

In fact, A is not even a valid 3x3 matrix. If you fail to show valid syntax, is there a way we can realistically help you? How do we know what you tried?

A=[1 1/2 1/3;1/2 1/3 1/4;1/^1/4 1/5]

Invalid use of operator.

Error in connector.internal.fevalMatlab

Error in connector.internal.fevalJSON

This is a 3x3 hilbert matrix.

hilb(3)
ans =
            1          0.5      0.33333
          0.5      0.33333         0.25
      0.33333         0.25          0.2

Is a 3x3 hilbert matrix truly ill-conditioned?

cond(hilb(3))
ans =
       524.06

No.

[Ali Kiral]

I am so sorry, I'm editing the matrix A in a few seconds..