Can all invertible matrices be diagonalized?

In summary, the conversation discusses using the definition of a single variable function to define a matrix function, specifically for the calculation of the Error function of a matrix, nth power of a matrix, and exponential of a matrix. The method involves using diagonalization and eigenvectors. The conversation also mentions the possibility of not being able to diagonalize all matrices and recommends a book on the subject.
  • #1
vvgobre
4
0
For a matrix [X] ,

Is there anyway to calculate the Error function of matrix or Erf[X] ?

Any possible solution to above will highly appreciated! :)
 
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  • #2
The general way to use the definition of a single variable function f(x) to define a matrix function f(X) is to take a power series for f(x) (like the McLaurin series) and substitute the matrix X for the variable x in it. Of course, this only makes sense if the power series in the matrix converges to some matrix.
 
  • #3
Thank you Stephen. :)

Recently i learn that if you have a matrix [A]

by diagonalization as A= V D V^-1 :D is diagonal matrix , V is eigenvector
can be use to calculate nth power (A^n) of matrix A as

A^(1/2) = V . D^(1/2) . V-1

source:http://en.wikipedia.org/wiki/Square_root_of_a_matrix

So basically i am using FORTRAN and i need to write three routine for
Erf[matrix], exp[matrix], [matrix]^a where a is real.

So Is it mathematically "true" ? if i generalize aforementioned algorithm to my three "needs" as

1) Erf[A] = V . Erf[D] . V-1

2) exp[A] = V . exp[D] . V-1

3) [A]^a = V . [D]^a . V-1 ; a is real number

?
 
  • #4
Yes, those are correct. Of course, you can't diagonalize all matrices. Were you writing your algorithm to apply only to those that can be diagonalized?

By the way, a modern book on this subject is "Functions Of Matrices" by Nicholas Higham published by SIAM.
 
  • #5
Thank you Stephen for prompt reply and reference too.

Yes i do check whether the matrix is invertible or not. :smile:
 
  • #6
Some invertible matrices are not diagonalizable. And some diagonalizable matrices are not invertible. (Zero is a legitimate eigenvalue.)
 

What is the error function of a matrix?

The error function of a matrix is a mathematical function that measures the difference between the observed values of a matrix and the values predicted by a mathematical model. It is often used in statistical analysis and machine learning to evaluate the accuracy of a model.

How is the error function calculated for a matrix?

The error function is typically calculated by taking the sum of the squared differences between the observed and predicted values of the matrix. This is known as the mean squared error (MSE) and is a common measure of error in regression and classification tasks.

Can the error function of a matrix be negative?

No, the error function of a matrix cannot be negative. This is because it is calculated using squared differences, which will always result in a positive value. A negative error would indicate that the predicted values are closer to the observed values than they actually are, which is not possible.

How can the error function be minimized?

The goal of any model is to minimize the error function, as this indicates a better fit to the data. This can be achieved by adjusting the parameters of the model and re-evaluating the error function until it reaches a minimum value. This process is known as optimization and is a key component of model training.

Is the error function the only measure of model performance?

No, the error function is not the only measure of model performance. Other metrics such as accuracy, precision, and recall may also be used depending on the specific problem and goals of the model. It is important to consider multiple measures of performance when evaluating a model.

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