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Old June 19th, 2008, 08:02 AM
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Find an orthogonal matrix P and a diagonal matrix D such that

A = PDP^(-1).

I found P but how do I find D? Is it correct to do D = PAP^(-1)? Thanks in advance.
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Old June 19th, 2008, 08:14 AM
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Originally Posted by Air View Post
Find an orthogonal matrix P and a diagonal matrix D such that

A = PDP^(-1).

I found P but how do I find D? Is it correct to do D = PAP^(-1)? Thanks in advance.
yes, you can do that.. but i think, it is D = P^{-1}AP

oh well, as long as they are inverses.. Ü
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Old June 19th, 2008, 08:15 AM
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Quote:
Originally Posted by Air View Post
Find an orthogonal matrix P and a diagonal matrix D such that

A = PDP^(-1).

I found P but how do I find D? Is it correct to do D = PAP^(-1)? Thanks in advance.
No you cannot do that I think because matrices are not commutative.
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Old June 19th, 2008, 08:31 AM
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No you cannot do that I think because matrices are not commutative.

So, how do I work out D?
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Old June 19th, 2008, 08:33 AM
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how do you get the inverse of P?
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Old June 19th, 2008, 08:35 AM
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how do you get the inverse of P?
It's just Transpose.
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Old June 19th, 2008, 08:37 AM
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yeah.. so apply the corrected equation i gave..
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Old June 19th, 2008, 08:48 AM
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Do you know how to find a matrix P such that P^{-1}AP = D?

If your familiar with that all you need to do is some basic matrix algebra

first pre multiply both sides by P , PP^{-1}AP = PD \ \ \Rightarrow IAP = PD

then post multiply by P^{-1} so IAPP^{-1} = PDP^{-1}

so IAI = PDP^{-1}

given A =  PDP^{-1}

Bobak
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Old June 19th, 2008, 11:24 AM
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Quote:
Originally Posted by Air View Post
Find an orthogonal matrix P and a diagonal matrix D such that

A = PDP^(-1).

I found P but how do I find D? Is it correct to do D = PAP^(-1)? Thanks in advance.
Although you could do D = P^{-1}AP, there is a much easier way:

In the process of finding P, you probably found the eigenvalues and eigenvectors of A. We know that if the eigenvalues of A are \lambda_1,\;\lambda_2,\;\dots,\;\lambda_n, and each eigenvalue \lambda_i corresponds to an eigenvector \textbf{p}_i such that \textbf{p}_1,\;\textbf{p}_2,\;\dots,\;\textbf{p}_n are linearly independent, then the matrix P = \left[\begin{array}{c|c|c|c}\textbf{p}_1 & \textbf{p}_2 & \cdots & \textbf{p}_n\end{array}\right] will diagonalize A, and A = PDP^{-1}. Now, it turns out that

D = \left[\begin{matrix}
\lambda_1 &       0       &        0      & \cdots & 0\\
      0       & \lambda_2 &        0      & \cdots & 0\\
      0       &       0       & \lambda_3 & \cdots & 0\\
   \vdots   &   \vdots    &     \vdots  & \ddots & \vdots\\
      0       &       0       &        0      & \cdots & \lambda_n
\end{matrix}\right]

Therefore, if you diagonalized A with P to get A = PDP^{-1}, then D will be a diagonal matrix whose diagonal entries are the eigenvalues of A, listed in the same order as you placed the eigenvectors of A into P.

Let me give a brief example:

Let A = \left[\begin{matrix}
1 & -1 & -1 \\
1 &  3 &  1 \\
-3 & 1 & -1
\end{matrix}\right]

We may easily determine the eigenvalues of A to be \lambda_1 = 2,\;\lambda_2 = -2,\;\lambda_3 = 3, and their corresponding eigenvectors:

\begin{array}{lcrl}
\lambda_1 & = & 2: & \left[\begin{matrix}-1\\0\\1\end{matrix}\right]\medskip\\
\lambda_2 & = & -2: & \left[\begin{matrix}1\\-1\\4\end{matrix}\right]\medskip\\
\lambda_3 & = & 3: & \left[\begin{matrix}-1\\1\\1\end{matrix}\right]
\end{array}

Putting these eigenvectors into the columns of P, we get

P = \left[\begin{matrix}
-1 & 1 & -1\\
 0 &-1 & 1\\
 1 & 4 & 1
\end{matrix}\right]

so A = PDP^{-1} for some diagonal D. And if you find D, you will find that it is

D = \left[\begin{matrix}
 2 & 0 & 0\\
 0 &-2 & 0\\
 0 & 0 & 3
\end{matrix}\right]

(try verifying this) and the entries along the main diagonal correspond exactly to the eigenvalues we found for A.

But, if you were to change the order of the eigenvectors in P, the entries in D will also be in different order.

For example, if we had set

P = \left[\begin{matrix}
1 & -1 & -1\\
-1 & 0 & 1\\
4 &  1 & 1
\end{matrix}\right],

then we would get

D = \left[\begin{matrix}
 -2 & 0 & 0\\
 0 & 2 & 0\\
 0 & 0 & 3
\end{matrix}\right]
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