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### Comparison of LAPACK and Jasymca Routines

We calculate the 4-th degree regression polynomial for the following x,y-data:
```>> x=[1:6],y=x+1
x = [ 1  2  3  4  5  6 ]
y = [ 2  3  4  5  6  7 ]
>> polyfit(x,y,4)
p =
5.1958E-14   -9.6634E-13  -2.4727E-12  1   1
```
The coefficients `p(1),p(2),p(3)` should vanish since `x` and `y` represent a perfect straight line. This is an unstable problem, and it can be easily extended to make Jasymca completely fail. In our second attempt we use the Lapack-routine `linlstsq`:
```>> x=[1:6],y=x+1;
>> l=length(x);n=4;
>> X=(x'*ones(1,n+1)).^(ones(l,1)*(n:-1:0))
>> linlstsq(X,y')
ans =
-1.6288E-18
-7.0249E-17
1.0653E-15
1
1
```
The coefficients `p(1),p(2),p(3)` are now significantly smaller. This particular problem can be solved exactly using Jasymca-routines and exact numbers, which avoids any rounding errors:
```>> x=rat([1:6]);y=x+1;
>> polyfit(x,y,4)
p =
0  0  0  1  1
```
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Helmut Dersch
2009-03-15