Derivate of this function of matrix

In summary: I'm having trouble with applying differentiation rules because I am not sure what the matrix A is. If I expand the terms and apply chain rule, there are terms which involve differentiation of matrix wrt. a matrix, which will result in some kronecker product terms, while the size of final derivative has to be m x n.I think I am missing something.thanks,abhishek.
  • #1
abhisheksingh
4
0
Hi ,

I am stuck with the problem of finding derivative wrt. A of following function:

Def:
y - column vector of size mx1
A - matrix of size m x n
b - column vector of size n x 1
K - matrix of size n x n
y' - denotes the transpose
K^-1 - denotes inverse

f(A) = (y - Ab)' (AKA')^-1 (y-Ab)


Can anyone please help me?

thanks,
abhishek.
 
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  • #2
It looks like a straightforward application of differentiation rules -- where are you having trouble?
 
  • #3
I am not able to differentiate it. If I expand the terms and apply chain rule, there are terms which involve differentiation of matrix wrt. a matrix, which will result in some kronecker product terms, while the size of final derivative has to be m x n.

Probably I am missing something.
 
  • #4
hi Hurkyl,

can you help me in this?

thanks ,
abhishek.
 
  • #5
Where specifically do you run into the problem? Some expressions seem like there can't possibly be any difficulty. For example, the derivative of (y-Ab) is surely -(dA)b, where I've used dA to denote whatever the heck the derivative of A with respect to A is supposed to be. These sorts of derivative rules tend to be correct no matter what you're differentiating with respect to. (In fact, if they weren't, we usually wouldn't call the operation a derivative!)


Incidentally, out of curiousity, just what is the definition you're using for 'derivative with respect to A'?
 
  • #6
Here it goes:

d{(y-Ab)' (AKA')^-1 (y-Ab)}
= d{(y-Ab)'(AKA')^-1} (y-Ab) + (y-Ab)' (AKA')^-1 d{(y-Ab)}
= [d{(y-Ab)'} (AKA')^-1 + (y-Ab)' d{(AKA')^-1}] (y-Ab) - (y-Ab)' (AKA')^-1 (dA) b
= -[b' (dA)' (AKA')^-1 + (y-Ab)' d{(AKA')^-1}] (y-Ab) - (y-Ab)' (AKA')^-1 (dA) b


After this, how to evaluate d{(AKA')^-1} ? Further, how to get the closed form expression for d{f(A)}/dA ?abhishek
 
  • #7
d{(AKA')^-1}
Ah, so you don't have a rule for differentiating an inverse?

In that case, you can try implicit differentiation to work it out -- let B be a matrix function of A. Can you think of any good equations involving [itex]B^{-1}[/itex] in which you know how to differentiate everything else?


Given the symmetry under transpose of the original expression, you might be able to simplify some things. e.g. that final term is the transpose of one of the terms you get if you distribute that product. I expect those terms to actually be equal, but that does depend upon precisely what dA means.
 

1. What is the definition of a derivative of a function of matrix?

A derivative of a function of matrix is a measure of how much the output of the function changes with respect to small changes in the input matrix. It is similar to the concept of derivative in one-variable calculus, but extended to matrices.

2. How is the derivative of a function of matrix calculated?

The derivative of a function of matrix can be calculated using the standard rules of matrix calculus, such as the product rule, chain rule, and quotient rule. These rules are similar to those used in one-variable calculus, but applied to matrices.

3. What is the importance of understanding the derivative of a function of matrix?

Understanding the derivative of a function of matrix is important in many areas of science and engineering, such as physics, economics, and machine learning. It allows us to analyze how small changes in the input matrix affect the output of the function, which is crucial in making predictions and optimizing systems.

4. Can the derivative of a function of matrix be negative?

Yes, the derivative of a function of matrix can be negative. Just like in one-variable calculus, the derivative of a function of matrix can be positive, negative, or zero, depending on the behavior of the function at a particular point.

5. How is the derivative of a function of matrix used in machine learning?

The derivative of a function of matrix is used in machine learning to update the parameters of a model during the training process. By calculating the derivative of the model's loss function with respect to the parameters, we can adjust the parameters in a way that minimizes the loss and improves the model's performance.

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