Hi all,
I want to solve an equation system Ax=b with least squares.
In my program I do the following:
VectorXd x(54);
MatrixXd A(44, 54);
VectorXd b(44);
//Fill A, x and b with values...
x = A.jacobiSvd(ComputeFullU | ComputeFullV).solve(b);
double relative_error = (A*x - b).norm() / b.norm();

I always get real big values in my x vector and my relative error is around a million... Am I doing something wrong?
I know that in the solution vector should be values around -2.0 to 2.0 and with matlab I get a nice x vector with just a little error.
Problem is I cant use Matlab in my program.

Here is my input data:
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b=[0.0132;-0.7984;0.3375;0.4238;-0.5040;0.1298;-0.4237;-0.3907;-0.0312;-0.1939;-0.7702;0.4020;0.1066;0.4995;-0.0042;-1.3118;-1.5623;-4.3572;-1.2877;-2.1275;-2.4368;0.8677;-0.9215;0.7576;1.2523;-1.2252;0.7880;0.0534;0.8329;2.3970;0.3824;-0.4462;1.6129;-0.2670;-0.3284;1.3252;0.1670;0.4413;0.8238;0.0847;0.4926;1.1158;0.0000;0.0000];
x=A\b

I hope it's ok to post such big data here...

Thanks in advance for any hints!
Have a nice Weekend,
Martin

I don't have time to look at your precise example. Sometime ColPivHouseholderQr works better, worth trying it.

Thank you, it worked perfectly! I didnt know that this function also could be used for least squared solutions, I thought the equation system had to be definitely solvable for this. Thanks for your help!