| Author |
Comment/Response |
Patrik
|
 10/02/11 1:13pm
Hello. I am posed a linear optimazation problem in the form of Ax = y, where I need to find the best solution x for the matrix-equation.
The properties of the matrix A is that it's fat (more coulmns than rows), also there is no guarantee that the columnspace of A is linearly independent(that is, A may not have full rank).
Trouble is, when A doesnt not have full rank, there is a set of non-uniqe solutions x and I need to pick the best one out of these. This is my problem.
If A does have full rank I can just
A := PseudoInverse[~some matrix elements];
y := {~some matrix elements};
A.y
and evaluate (this is the least norm solution). Then I am guaranteed the least norm solution.
So, is there some extension to this so I can handle it when A does not nessecarily have full rank? I was told Matlab has this, tho I'm using Mathematica, so here I am.
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