I am getting a bit rusty on some of these things, but I seem to recall
that there is a numerical advantage (speed and/or accuracy?) to
diagonalizing:
> expM <- function(X,e) { v <- La.svd(X); v$u %*% diag(v$d^e) %*% v$vt
}
> P <- matrix(c(.3,.7, .7, .3), ncol=2)
> P %*% P %*% P
[,1] [,2]
[1,] 0.468 0.532
[2,] 0.532 0.468
> expM(P,3)
[,1] [,2]
[1,] 0.468 0.532
[2,] 0.532 0.468
I think this also works for non-integer, negative, large, and complex
exponents:
> expM(P, 1.5)
[,1] [,2]
[1,] 0.3735089 0.6264911
[2,] 0.6264911 0.3735089
> expM(P, 1000)
[,1] [,2]
[1,] 0.5 0.5
[2,] 0.5 0.5
> expM(P, -3)
[,1] [,2]
[1,] -7.3125 8.3125
[2,] 8.3125 -7.3125
> expM(P, 3+.5i)
[,1] [,2]
[1,] 0.4713+0.0141531i 0.5287-0.0141531i
[2,] 0.5287-0.0141531i 0.4713+0.0141531i
>
Paul Gilbert
Doran, Harold wrote:
>Suppose I have a square matrix P
>
>P <- matrix(c(.3,.7, .7, .3), ncol=2)
>
>I know that
>
>
>
>>P * P
>>
>>
>
>Returns the element by element product, whereas
>
>
>
>>P%*%P
>>
>>
>
>Returns the matrix product.
>
>Now, P^2 also returns the element by element product. But, is there a
>slick way to write
>
>P %*% P %*% P
>
>Obviously, P^3 does not return the result I expect.
>
>Thanks,
>Harold
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
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>
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