Finding eigenvalues of $A^{10} + A^7 + 5A$.












0












$begingroup$


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?










share|cite|improve this question











$endgroup$








  • 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
















0












$begingroup$


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?










share|cite|improve this question











$endgroup$








  • 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














0












0








0


1



$begingroup$


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?










share|cite|improve this question











$endgroup$




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






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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














  • 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










3 Answers
3






active

oldest

votes


















2












$begingroup$

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 $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$






share|cite|improve this answer











$endgroup$













  • $begingroup$
    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



















1












$begingroup$

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 $P(A)$.






share|cite|improve this answer









$endgroup$





















    1












    $begingroup$

    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 $A^{10}+A^{7}+5A$.






    share|cite|improve this answer









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      3 Answers
      3






      active

      oldest

      votes








      3 Answers
      3






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      2












      $begingroup$

      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 $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$






      share|cite|improve this answer











      $endgroup$













      • $begingroup$
        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
















      2












      $begingroup$

      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 $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$






      share|cite|improve this answer











      $endgroup$













      • $begingroup$
        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














      2












      2








      2





      $begingroup$

      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 $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$






      share|cite|improve this answer











      $endgroup$



      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 $$A^{10}X = A^{10}(3cdot v_1 + 4cdot v_2 + 1cdot v_3) = 3cdot A^{10}v_1 + 4cdot A^{10} v_2 + A^{10}v_3$$ $$ = 3cdot lambda_2^{10} cdot v_1 + 4cdot lambda_1^{10}cdot v_2 + lambda_1^{10}cdot v_3 = 3cdot 2^{10} cdot v_1 + 4cdot 1^{10}cdot v_2 + 1^{10}cdot v_3$$







      share|cite|improve this answer














      share|cite|improve this answer



      share|cite|improve this answer








      edited Dec 2 '18 at 16:20









      winnie33

      32




      32










      answered Jul 13 '15 at 22:28









      KrijnKrijn

      1,146924




      1,146924












      • $begingroup$
        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


















      • $begingroup$
        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
















      $begingroup$
      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











      1












      $begingroup$

      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 $P(A)$.






      share|cite|improve this answer









      $endgroup$


















        1












        $begingroup$

        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 $P(A)$.






        share|cite|improve this answer









        $endgroup$
















          1












          1








          1





          $begingroup$

          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 $P(A)$.






          share|cite|improve this answer









          $endgroup$



          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 $P(A)$.







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Jul 13 '15 at 21:23









          ajotatxeajotatxe

          53.7k23890




          53.7k23890























              1












              $begingroup$

              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 $A^{10}+A^{7}+5A$.






              share|cite|improve this answer









              $endgroup$


















                1












                $begingroup$

                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 $A^{10}+A^{7}+5A$.






                share|cite|improve this answer









                $endgroup$
















                  1












                  1








                  1





                  $begingroup$

                  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 $A^{10}+A^{7}+5A$.






                  share|cite|improve this answer









                  $endgroup$



                  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 $A^{10}+A^{7}+5A$.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Jul 13 '15 at 21:23









                  aghaagha

                  8,99641533




                  8,99641533






























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