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I've a very trivial numerical problem where I'm currently stuck. In MATLAB the matrix Hf:

Code:

```
>> Hf
Hf =
1.0e+003 *
1.6443 1.6516 1.6583
4.8373 4.8349 4.8334
4.6385 4.6418 4.6445
-9.6014 -9.6084 -9.6154
```

Code:

```
>> [yl1 , yl3 , yl1 - yl3 ]
ans =
1.0e+006 *
0.2966 0.2972 -0.0006
0.8705 0.8703 0.0002
0.8352 0.8355 -0.0003
-1.7288 -1.7295 0.0006
```

Code:

```
>> Hf \ yl1
ans =
100.0000
75.0000
5.0000
```

Code:

```
>> Hf \ yl3
ans =
56.0412
72.5578
51.4007
```

I have much redundancy in the data, so I can ad much lines to the matrix Hf. However, it does not matter how much, the result is always the same ... unuseable.

Can anyone explain why the least squares is so terrible in this case? I'm a bit confused because least squares should be pretty robust ...

Thanks,

divB