multi variate damping least square solver

[NPI_Chris]

Hello all,

I am hoping to get some advice and a starting point for a challenging problem I am working on.

I have a 33x37 matrix that i need to minimize using a damping least square approach.  in order to achieve the minimization I need to use a set of 50 Matrices (33x37, representing adjustment impacts) that are grouped in 12 separate catagories with different starting points and degrees of freedom, and none of the adjustment matrices are truly independent of each other (they all interact with each other to some extent.)

For the output I need the minimized matrix and the amount of each vector applied to achieve the minimized solution.

To hopefully clarify the inputs and outputs I have included an example vi with a dummy set of data.

I am hoping that someone can give me an idea of where to start, can this be accomplished using the labview libraries or will this have to use scripting via a mathscript node, or matlab?  If it can be done in different ways which method is the most efficient?

I hope this is enough information, but if you need more details please let me know, although giving exact details can be difficult in this case due to IP considerations.

As always thank you in advance for any help/suggestions you may be able to offer.

Chris

[DSPGuy]

Chris,

It is not clear exactly what it is you are trying to minimize.  The 33x37 matrix holds the values to be minimized, but what is the function that you are trying to minimize?

-Jim

[NPI_Chris]

Other then using a least square method I dont have a function defined yet (trying to figure out what function to use), but essentially I am trying to find out how much of each of the adjustment matrices need to be applied to minimize the original matrices..

I hope that explains what i am trying to do a little better

[DSPGuy]

Chris,

When you say "much of each of the adjustment matrices need to be applied" do you mean some sort of matrix multiplication?  Some scaling and addition?  Post when you have a function in mind for this.

-Jim