Finding eigenvalues of $A^{10} + A^7 + 5A$.
$begingroup$
Problem: Let $A = begin{pmatrix} 1 & 2 & -1 \ 0 & 5 & -2 \ 0 & 6 & -2 end{pmatrix}$.
1) Compute the eigenvalues of $A^{10} + A^7 + 5A$.
2) Compute $A^{10} X$ for the vector $X = begin{pmatrix} 2 \ 4 \ 7 end{pmatrix}$.
Attempt at solution: I first computed the eigenvalues of $A$. The characteristic polynomial gives begin{align*} det(A - x mathbb{I}_3) = det begin{pmatrix} 1-x & 2 & -1 \ 0 & 5-x & -2 \ 0 & 6 & -2 -x end{pmatrix} end{align*} Laplace expansion along the first column gives begin{align*} det(A - x mathbb{I}_3) = (1-x) det begin{pmatrix} 5-x & -2 \ 6 & -2-x end{pmatrix} &= (1-x) [(5-x)(-2-x)+12] \ &= (1-x)(x^2-3x+2) \ &= (x-1)^2(2-x)end{align*} So the eigenvalues are $lambda_1 = 1, lambda_2 = 1$ and $lambda_3 = -2$.
Now, I'm aware that there is a theorem which says that if the matrix $A$ has an eigenvalue $lambda$, then $A^k$ has the eigenvalue $lambda^k$ corresponding to the same eigenvector. But I'm not sure what I should do about this sum here. Can I just add the corresponding eigenvalues? So for the first eigenvalue that would give me: $ 1^{10} + 1^7 + 5(1) = 7$?
Any help would be appreciated.
Edit: Figured out part 1), but I'm still wondering what to do with 2). Should I find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix and then I can compute the powers easily?
linear-algebra vector-spaces eigenvalues-eigenvectors
$endgroup$
|
show 2 more comments
$begingroup$
Problem: Let $A = begin{pmatrix} 1 & 2 & -1 \ 0 & 5 & -2 \ 0 & 6 & -2 end{pmatrix}$.
1) Compute the eigenvalues of $A^{10} + A^7 + 5A$.
2) Compute $A^{10} X$ for the vector $X = begin{pmatrix} 2 \ 4 \ 7 end{pmatrix}$.
Attempt at solution: I first computed the eigenvalues of $A$. The characteristic polynomial gives begin{align*} det(A - x mathbb{I}_3) = det begin{pmatrix} 1-x & 2 & -1 \ 0 & 5-x & -2 \ 0 & 6 & -2 -x end{pmatrix} end{align*} Laplace expansion along the first column gives begin{align*} det(A - x mathbb{I}_3) = (1-x) det begin{pmatrix} 5-x & -2 \ 6 & -2-x end{pmatrix} &= (1-x) [(5-x)(-2-x)+12] \ &= (1-x)(x^2-3x+2) \ &= (x-1)^2(2-x)end{align*} So the eigenvalues are $lambda_1 = 1, lambda_2 = 1$ and $lambda_3 = -2$.
Now, I'm aware that there is a theorem which says that if the matrix $A$ has an eigenvalue $lambda$, then $A^k$ has the eigenvalue $lambda^k$ corresponding to the same eigenvector. But I'm not sure what I should do about this sum here. Can I just add the corresponding eigenvalues? So for the first eigenvalue that would give me: $ 1^{10} + 1^7 + 5(1) = 7$?
Any help would be appreciated.
Edit: Figured out part 1), but I'm still wondering what to do with 2). Should I find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix and then I can compute the powers easily?
linear-algebra vector-spaces eigenvalues-eigenvectors
$endgroup$
1
$begingroup$
The theorem can be further generalized to polynomial case (your guess is right and not hard to verify).
$endgroup$
– Zhanxiong
Jul 13 '15 at 21:16
2
$begingroup$
If $Ax = lambda x$ then $(A^{10} + A^7 + 5A)(x) = A^{10}x + A^7x + 5Ax = lambda^{10}x + lambda^7 x + 5 cdot lambda x = (lambda^{10} + lambda^7 + 5 cdot lambda) cdot x$.
$endgroup$
– Krijn
Jul 13 '15 at 21:19
$begingroup$
You should re-check your algebra, the eigenvalues are $2,1,1$
$endgroup$
– John McGee
Jul 13 '15 at 21:20
$begingroup$
Ah right, thanks. I wrote down the wrong minor. Also, for the second question, should I first find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix, and then compute $A^{10}$?
$endgroup$
– Kamil
Jul 13 '15 at 21:23
$begingroup$
It might be easier to find the eigenvectors and write $X$ as a linear combination of these eigenvectors. Then $A^{10}X$ should be easy to calculate.
$endgroup$
– Krijn
Jul 13 '15 at 21:25
|
show 2 more comments
$begingroup$
Problem: Let $A = begin{pmatrix} 1 & 2 & -1 \ 0 & 5 & -2 \ 0 & 6 & -2 end{pmatrix}$.
1) Compute the eigenvalues of $A^{10} + A^7 + 5A$.
2) Compute $A^{10} X$ for the vector $X = begin{pmatrix} 2 \ 4 \ 7 end{pmatrix}$.
Attempt at solution: I first computed the eigenvalues of $A$. The characteristic polynomial gives begin{align*} det(A - x mathbb{I}_3) = det begin{pmatrix} 1-x & 2 & -1 \ 0 & 5-x & -2 \ 0 & 6 & -2 -x end{pmatrix} end{align*} Laplace expansion along the first column gives begin{align*} det(A - x mathbb{I}_3) = (1-x) det begin{pmatrix} 5-x & -2 \ 6 & -2-x end{pmatrix} &= (1-x) [(5-x)(-2-x)+12] \ &= (1-x)(x^2-3x+2) \ &= (x-1)^2(2-x)end{align*} So the eigenvalues are $lambda_1 = 1, lambda_2 = 1$ and $lambda_3 = -2$.
Now, I'm aware that there is a theorem which says that if the matrix $A$ has an eigenvalue $lambda$, then $A^k$ has the eigenvalue $lambda^k$ corresponding to the same eigenvector. But I'm not sure what I should do about this sum here. Can I just add the corresponding eigenvalues? So for the first eigenvalue that would give me: $ 1^{10} + 1^7 + 5(1) = 7$?
Any help would be appreciated.
Edit: Figured out part 1), but I'm still wondering what to do with 2). Should I find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix and then I can compute the powers easily?
linear-algebra vector-spaces eigenvalues-eigenvectors
$endgroup$
Problem: Let $A = begin{pmatrix} 1 & 2 & -1 \ 0 & 5 & -2 \ 0 & 6 & -2 end{pmatrix}$.
1) Compute the eigenvalues of $A^{10} + A^7 + 5A$.
2) Compute $A^{10} X$ for the vector $X = begin{pmatrix} 2 \ 4 \ 7 end{pmatrix}$.
Attempt at solution: I first computed the eigenvalues of $A$. The characteristic polynomial gives begin{align*} det(A - x mathbb{I}_3) = det begin{pmatrix} 1-x & 2 & -1 \ 0 & 5-x & -2 \ 0 & 6 & -2 -x end{pmatrix} end{align*} Laplace expansion along the first column gives begin{align*} det(A - x mathbb{I}_3) = (1-x) det begin{pmatrix} 5-x & -2 \ 6 & -2-x end{pmatrix} &= (1-x) [(5-x)(-2-x)+12] \ &= (1-x)(x^2-3x+2) \ &= (x-1)^2(2-x)end{align*} So the eigenvalues are $lambda_1 = 1, lambda_2 = 1$ and $lambda_3 = -2$.
Now, I'm aware that there is a theorem which says that if the matrix $A$ has an eigenvalue $lambda$, then $A^k$ has the eigenvalue $lambda^k$ corresponding to the same eigenvector. But I'm not sure what I should do about this sum here. Can I just add the corresponding eigenvalues? So for the first eigenvalue that would give me: $ 1^{10} + 1^7 + 5(1) = 7$?
Any help would be appreciated.
Edit: Figured out part 1), but I'm still wondering what to do with 2). Should I find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix and then I can compute the powers easily?
linear-algebra vector-spaces eigenvalues-eigenvectors
linear-algebra vector-spaces eigenvalues-eigenvectors
edited Dec 15 '15 at 17:49
Rising Star
292112
292112
asked Jul 13 '15 at 21:14
KamilKamil
1,99821545
1,99821545
1
$begingroup$
The theorem can be further generalized to polynomial case (your guess is right and not hard to verify).
$endgroup$
– Zhanxiong
Jul 13 '15 at 21:16
2
$begingroup$
If $Ax = lambda x$ then $(A^{10} + A^7 + 5A)(x) = A^{10}x + A^7x + 5Ax = lambda^{10}x + lambda^7 x + 5 cdot lambda x = (lambda^{10} + lambda^7 + 5 cdot lambda) cdot x$.
$endgroup$
– Krijn
Jul 13 '15 at 21:19
$begingroup$
You should re-check your algebra, the eigenvalues are $2,1,1$
$endgroup$
– John McGee
Jul 13 '15 at 21:20
$begingroup$
Ah right, thanks. I wrote down the wrong minor. Also, for the second question, should I first find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix, and then compute $A^{10}$?
$endgroup$
– Kamil
Jul 13 '15 at 21:23
$begingroup$
It might be easier to find the eigenvectors and write $X$ as a linear combination of these eigenvectors. Then $A^{10}X$ should be easy to calculate.
$endgroup$
– Krijn
Jul 13 '15 at 21:25
|
show 2 more comments
1
$begingroup$
The theorem can be further generalized to polynomial case (your guess is right and not hard to verify).
$endgroup$
– Zhanxiong
Jul 13 '15 at 21:16
2
$begingroup$
If $Ax = lambda x$ then $(A^{10} + A^7 + 5A)(x) = A^{10}x + A^7x + 5Ax = lambda^{10}x + lambda^7 x + 5 cdot lambda x = (lambda^{10} + lambda^7 + 5 cdot lambda) cdot x$.
$endgroup$
– Krijn
Jul 13 '15 at 21:19
$begingroup$
You should re-check your algebra, the eigenvalues are $2,1,1$
$endgroup$
– John McGee
Jul 13 '15 at 21:20
$begingroup$
Ah right, thanks. I wrote down the wrong minor. Also, for the second question, should I first find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix, and then compute $A^{10}$?
$endgroup$
– Kamil
Jul 13 '15 at 21:23
$begingroup$
It might be easier to find the eigenvectors and write $X$ as a linear combination of these eigenvectors. Then $A^{10}X$ should be easy to calculate.
$endgroup$
– Krijn
Jul 13 '15 at 21:25
1
1
$begingroup$
The theorem can be further generalized to polynomial case (your guess is right and not hard to verify).
$endgroup$
– Zhanxiong
Jul 13 '15 at 21:16
$begingroup$
The theorem can be further generalized to polynomial case (your guess is right and not hard to verify).
$endgroup$
– Zhanxiong
Jul 13 '15 at 21:16
2
2
$begingroup$
If $Ax = lambda x$ then $(A^{10} + A^7 + 5A)(x) = A^{10}x + A^7x + 5Ax = lambda^{10}x + lambda^7 x + 5 cdot lambda x = (lambda^{10} + lambda^7 + 5 cdot lambda) cdot x$.
$endgroup$
– Krijn
Jul 13 '15 at 21:19
$begingroup$
If $Ax = lambda x$ then $(A^{10} + A^7 + 5A)(x) = A^{10}x + A^7x + 5Ax = lambda^{10}x + lambda^7 x + 5 cdot lambda x = (lambda^{10} + lambda^7 + 5 cdot lambda) cdot x$.
$endgroup$
– Krijn
Jul 13 '15 at 21:19
$begingroup$
You should re-check your algebra, the eigenvalues are $2,1,1$
$endgroup$
– John McGee
Jul 13 '15 at 21:20
$begingroup$
You should re-check your algebra, the eigenvalues are $2,1,1$
$endgroup$
– John McGee
Jul 13 '15 at 21:20
$begingroup$
Ah right, thanks. I wrote down the wrong minor. Also, for the second question, should I first find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix, and then compute $A^{10}$?
$endgroup$
– Kamil
Jul 13 '15 at 21:23
$begingroup$
Ah right, thanks. I wrote down the wrong minor. Also, for the second question, should I first find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix, and then compute $A^{10}$?
$endgroup$
– Kamil
Jul 13 '15 at 21:23
$begingroup$
It might be easier to find the eigenvectors and write $X$ as a linear combination of these eigenvectors. Then $A^{10}X$ should be easy to calculate.
$endgroup$
– Krijn
Jul 13 '15 at 21:25
$begingroup$
It might be easier to find the eigenvectors and write $X$ as a linear combination of these eigenvectors. Then $A^{10}X$ should be easy to calculate.
$endgroup$
– Krijn
Jul 13 '15 at 21:25
|
show 2 more comments
3 Answers
3
active
oldest
votes
$begingroup$
For the first question, observe that
$$(A^{10}+A^{7}+5A)x=(lambda^{10}+lambda^7+5lambda)x$$ as I have shown in the comments.
For the second question, you observed correctly that we have three eigenvectors, for the eigenvalues $2$, $1$ and $1$, respectively
$$v_1 = begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix}, v_2 = begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix}, v_3= begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$$
We see that $X = begin{pmatrix} 2\ 4 \ 7 \ end{pmatrix} = 3 cdot begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix} + 4 cdot begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix} + 1 cdot begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$
With this we can calculate $A^{10}X$ quite easily, because $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$
$endgroup$
$begingroup$
I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
$endgroup$
– Kamil
Jul 13 '15 at 22:35
$begingroup$
If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
$endgroup$
– Krijn
Jul 13 '15 at 22:41
$begingroup$
Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
$endgroup$
– Kamil
Jul 13 '15 at 22:53
$begingroup$
That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
$endgroup$
– Krijn
Jul 13 '15 at 22:54
add a comment |
$begingroup$
If $P(X)=sum_{k=0}^na_kX^k$ is a polynomial and a matrix $A$ has en eigenvalue $lambda$ with eigenvector $v$, we have:
$$P(A)(v)=sum_{k=0}^na_kA^kv=sum_{k=0}^na_klambda^kv=P(lambda)v$$
Thus, $P(lambda)$ is an eigenvalue of $P(A)$.
$endgroup$
add a comment |
$begingroup$
By definition of eigenvalue:
$$Av=lambda v$$
for $vneq 0$ and corresponding eigenvector. So:
$$A^{2}v=A (Av)=A(lambda v)=lambda Av=lambda lambda v=lambda^2 v$$
The same way we can show that:
$$A^k v=lambda^k v$$
So:
$$A^{10}v=lambda^{10}v$$
$$A^{7}v=lambda^{7}v$$
$$5A^{1}v=5lambda^{1}v$$
If we add these three equations side by side we have:
$$(A^{10}+A^{7}+5A)v=(lambda^{10}+lambda^7+5lambda)v$$
So if $lambda$ is eigenvalue of $A$, then $lambda^{10}+lambda^7+5lambda$ is eigenvalue of $A^{10}+A^{7}+5A$.
$endgroup$
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
For the first question, observe that
$$(A^{10}+A^{7}+5A)x=(lambda^{10}+lambda^7+5lambda)x$$ as I have shown in the comments.
For the second question, you observed correctly that we have three eigenvectors, for the eigenvalues $2$, $1$ and $1$, respectively
$$v_1 = begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix}, v_2 = begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix}, v_3= begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$$
We see that $X = begin{pmatrix} 2\ 4 \ 7 \ end{pmatrix} = 3 cdot begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix} + 4 cdot begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix} + 1 cdot begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$
With this we can calculate $A^{10}X$ quite easily, because $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$
$endgroup$
$begingroup$
I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
$endgroup$
– Kamil
Jul 13 '15 at 22:35
$begingroup$
If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
$endgroup$
– Krijn
Jul 13 '15 at 22:41
$begingroup$
Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
$endgroup$
– Kamil
Jul 13 '15 at 22:53
$begingroup$
That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
$endgroup$
– Krijn
Jul 13 '15 at 22:54
add a comment |
$begingroup$
For the first question, observe that
$$(A^{10}+A^{7}+5A)x=(lambda^{10}+lambda^7+5lambda)x$$ as I have shown in the comments.
For the second question, you observed correctly that we have three eigenvectors, for the eigenvalues $2$, $1$ and $1$, respectively
$$v_1 = begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix}, v_2 = begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix}, v_3= begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$$
We see that $X = begin{pmatrix} 2\ 4 \ 7 \ end{pmatrix} = 3 cdot begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix} + 4 cdot begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix} + 1 cdot begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$
With this we can calculate $A^{10}X$ quite easily, because $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$
$endgroup$
$begingroup$
I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
$endgroup$
– Kamil
Jul 13 '15 at 22:35
$begingroup$
If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
$endgroup$
– Krijn
Jul 13 '15 at 22:41
$begingroup$
Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
$endgroup$
– Kamil
Jul 13 '15 at 22:53
$begingroup$
That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
$endgroup$
– Krijn
Jul 13 '15 at 22:54
add a comment |
$begingroup$
For the first question, observe that
$$(A^{10}+A^{7}+5A)x=(lambda^{10}+lambda^7+5lambda)x$$ as I have shown in the comments.
For the second question, you observed correctly that we have three eigenvectors, for the eigenvalues $2$, $1$ and $1$, respectively
$$v_1 = begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix}, v_2 = begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix}, v_3= begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$$
We see that $X = begin{pmatrix} 2\ 4 \ 7 \ end{pmatrix} = 3 cdot begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix} + 4 cdot begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix} + 1 cdot begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$
With this we can calculate $A^{10}X$ quite easily, because $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$
$endgroup$
For the first question, observe that
$$(A^{10}+A^{7}+5A)x=(lambda^{10}+lambda^7+5lambda)x$$ as I have shown in the comments.
For the second question, you observed correctly that we have three eigenvectors, for the eigenvalues $2$, $1$ and $1$, respectively
$$v_1 = begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix}, v_2 = begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix}, v_3= begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$$
We see that $X = begin{pmatrix} 2\ 4 \ 7 \ end{pmatrix} = 3 cdot begin{pmatrix} frac{1}{3}\ frac{2}{3} \ 1 \ end{pmatrix} + 4 cdot begin{pmatrix} 0\ frac{1}{2} \ 1 \ end{pmatrix} + 1 cdot begin{pmatrix} 1\ 0 \ 0 \ end{pmatrix}$
With this we can calculate $A^{10}X$ quite easily, because $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$
edited Dec 2 '18 at 16:20
winnie33
32
32
answered Jul 13 '15 at 22:28
KrijnKrijn
1,146924
1,146924
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I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
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– Kamil
Jul 13 '15 at 22:35
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If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
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– Krijn
Jul 13 '15 at 22:41
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Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
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– Kamil
Jul 13 '15 at 22:53
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That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
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– Krijn
Jul 13 '15 at 22:54
add a comment |
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I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
$endgroup$
– Kamil
Jul 13 '15 at 22:35
$begingroup$
If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
$endgroup$
– Krijn
Jul 13 '15 at 22:41
$begingroup$
Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
$endgroup$
– Kamil
Jul 13 '15 at 22:53
$begingroup$
That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
$endgroup$
– Krijn
Jul 13 '15 at 22:54
$begingroup$
I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
$endgroup$
– Kamil
Jul 13 '15 at 22:35
$begingroup$
I don't understand how $v_3$ can be a different eigenvector from $v_2$, when the eigenvalue is $1$ in both cases? How did you compute $v_3$?
$endgroup$
– Kamil
Jul 13 '15 at 22:35
$begingroup$
If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
$endgroup$
– Krijn
Jul 13 '15 at 22:41
$begingroup$
If an eigenvalue $lambda$ has multiplicity $> 1$, such as $lambda = 1$ in our case, it can happen that there are two linearly independent eigenvectors $v$, $w$ such that $Av = lambda v$ and $Aw = lambda w$. There is an easy test for the amount of linearly independent eigenvectors that belong to a certain eigenvalue, namely: The amount of linearly independent eigenvalues for a certain eigenvalue $lambda$ is equal to the dimension of the kernel of $A - lambda I$. In our case, the dimension of the kernel of $A - 1cdot I$ is equal to $2$, therefore, there are two eigenvectors.
$endgroup$
– Krijn
Jul 13 '15 at 22:41
$begingroup$
Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
$endgroup$
– Kamil
Jul 13 '15 at 22:53
$begingroup$
Thanks, that was very clear. I will look for a proof of this theorem. Also, is it correct that two distinct eigenvalues can never have the same eigenvector?
$endgroup$
– Kamil
Jul 13 '15 at 22:53
$begingroup$
That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
$endgroup$
– Krijn
Jul 13 '15 at 22:54
$begingroup$
That is indeed correct and very easy to proof, because if $Av = lambda_1 v$ and $Av = lambda_2 v$ then of course $lambda_1 v = lambda_2 v Rightarrow lambda_1 = lambda_2$.
$endgroup$
– Krijn
Jul 13 '15 at 22:54
add a comment |
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If $P(X)=sum_{k=0}^na_kX^k$ is a polynomial and a matrix $A$ has en eigenvalue $lambda$ with eigenvector $v$, we have:
$$P(A)(v)=sum_{k=0}^na_kA^kv=sum_{k=0}^na_klambda^kv=P(lambda)v$$
Thus, $P(lambda)$ is an eigenvalue of $P(A)$.
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add a comment |
$begingroup$
If $P(X)=sum_{k=0}^na_kX^k$ is a polynomial and a matrix $A$ has en eigenvalue $lambda$ with eigenvector $v$, we have:
$$P(A)(v)=sum_{k=0}^na_kA^kv=sum_{k=0}^na_klambda^kv=P(lambda)v$$
Thus, $P(lambda)$ is an eigenvalue of $P(A)$.
$endgroup$
add a comment |
$begingroup$
If $P(X)=sum_{k=0}^na_kX^k$ is a polynomial and a matrix $A$ has en eigenvalue $lambda$ with eigenvector $v$, we have:
$$P(A)(v)=sum_{k=0}^na_kA^kv=sum_{k=0}^na_klambda^kv=P(lambda)v$$
Thus, $P(lambda)$ is an eigenvalue of $P(A)$.
$endgroup$
If $P(X)=sum_{k=0}^na_kX^k$ is a polynomial and a matrix $A$ has en eigenvalue $lambda$ with eigenvector $v$, we have:
$$P(A)(v)=sum_{k=0}^na_kA^kv=sum_{k=0}^na_klambda^kv=P(lambda)v$$
Thus, $P(lambda)$ is an eigenvalue of $P(A)$.
answered Jul 13 '15 at 21:23
ajotatxeajotatxe
53.7k23890
53.7k23890
add a comment |
add a comment |
$begingroup$
By definition of eigenvalue:
$$Av=lambda v$$
for $vneq 0$ and corresponding eigenvector. So:
$$A^{2}v=A (Av)=A(lambda v)=lambda Av=lambda lambda v=lambda^2 v$$
The same way we can show that:
$$A^k v=lambda^k v$$
So:
$$A^{10}v=lambda^{10}v$$
$$A^{7}v=lambda^{7}v$$
$$5A^{1}v=5lambda^{1}v$$
If we add these three equations side by side we have:
$$(A^{10}+A^{7}+5A)v=(lambda^{10}+lambda^7+5lambda)v$$
So if $lambda$ is eigenvalue of $A$, then $lambda^{10}+lambda^7+5lambda$ is eigenvalue of $A^{10}+A^{7}+5A$.
$endgroup$
add a comment |
$begingroup$
By definition of eigenvalue:
$$Av=lambda v$$
for $vneq 0$ and corresponding eigenvector. So:
$$A^{2}v=A (Av)=A(lambda v)=lambda Av=lambda lambda v=lambda^2 v$$
The same way we can show that:
$$A^k v=lambda^k v$$
So:
$$A^{10}v=lambda^{10}v$$
$$A^{7}v=lambda^{7}v$$
$$5A^{1}v=5lambda^{1}v$$
If we add these three equations side by side we have:
$$(A^{10}+A^{7}+5A)v=(lambda^{10}+lambda^7+5lambda)v$$
So if $lambda$ is eigenvalue of $A$, then $lambda^{10}+lambda^7+5lambda$ is eigenvalue of $A^{10}+A^{7}+5A$.
$endgroup$
add a comment |
$begingroup$
By definition of eigenvalue:
$$Av=lambda v$$
for $vneq 0$ and corresponding eigenvector. So:
$$A^{2}v=A (Av)=A(lambda v)=lambda Av=lambda lambda v=lambda^2 v$$
The same way we can show that:
$$A^k v=lambda^k v$$
So:
$$A^{10}v=lambda^{10}v$$
$$A^{7}v=lambda^{7}v$$
$$5A^{1}v=5lambda^{1}v$$
If we add these three equations side by side we have:
$$(A^{10}+A^{7}+5A)v=(lambda^{10}+lambda^7+5lambda)v$$
So if $lambda$ is eigenvalue of $A$, then $lambda^{10}+lambda^7+5lambda$ is eigenvalue of $A^{10}+A^{7}+5A$.
$endgroup$
By definition of eigenvalue:
$$Av=lambda v$$
for $vneq 0$ and corresponding eigenvector. So:
$$A^{2}v=A (Av)=A(lambda v)=lambda Av=lambda lambda v=lambda^2 v$$
The same way we can show that:
$$A^k v=lambda^k v$$
So:
$$A^{10}v=lambda^{10}v$$
$$A^{7}v=lambda^{7}v$$
$$5A^{1}v=5lambda^{1}v$$
If we add these three equations side by side we have:
$$(A^{10}+A^{7}+5A)v=(lambda^{10}+lambda^7+5lambda)v$$
So if $lambda$ is eigenvalue of $A$, then $lambda^{10}+lambda^7+5lambda$ is eigenvalue of $A^{10}+A^{7}+5A$.
answered Jul 13 '15 at 21:23
aghaagha
8,99641533
8,99641533
add a comment |
add a comment |
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1
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The theorem can be further generalized to polynomial case (your guess is right and not hard to verify).
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– Zhanxiong
Jul 13 '15 at 21:16
2
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If $Ax = lambda x$ then $(A^{10} + A^7 + 5A)(x) = A^{10}x + A^7x + 5Ax = lambda^{10}x + lambda^7 x + 5 cdot lambda x = (lambda^{10} + lambda^7 + 5 cdot lambda) cdot x$.
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– Krijn
Jul 13 '15 at 21:19
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You should re-check your algebra, the eigenvalues are $2,1,1$
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– John McGee
Jul 13 '15 at 21:20
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Ah right, thanks. I wrote down the wrong minor. Also, for the second question, should I first find an invertible matrix $P$ such that $P^{-1} A P$ is a diagonal matrix, and then compute $A^{10}$?
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– Kamil
Jul 13 '15 at 21:23
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It might be easier to find the eigenvectors and write $X$ as a linear combination of these eigenvectors. Then $A^{10}X$ should be easy to calculate.
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– Krijn
Jul 13 '15 at 21:25