Find eigenvalues of the matrix












6














Find eigenvalues of the $(n+1) times (n+1)$-matrix



$$ left( begin {array}{cccccccc} 0&0&0&0&0&0&-n&0\ 0
&0&0&0&0&-(n-1)&0&1\ 0&0&0&0&-(n-2)&0&2&0
\ 0&0&0&ldots&0&3&0&0\ 0&0&-3&0&ldots&0
&0&0\ 0&-2&0&n-2&0&0&0&0\ -1&0&n-1&0
&0&0&0&0\ 0&n&0&0&0&0&0&0end {array} right)
$$



I have tried for some small $n$ by hand and have got that for $n=3$ the eigenvalues are $1,-1,3,-3$, for $n=4$ eigenvalues are $0,2,-2,4,-4$. So I am conjectured than for arbitrary $n$ the eigenvalues are $n, n-2, n-4, ldots, -n+2,-n.$



How to prove it?










share|cite|improve this question
























  • mostly you calculate a matrix with columns the eigenvectors, adjusting so they are all integers, and see if there is any pattern that holds up as $n$ increases
    – Will Jagy
    Dec 4 '18 at 22:40






  • 2




    Your matrix, say $M$, belongs to the so-called category of "skew centrosymmetric matrices" $JMJ=-M$ (where $J$ is the antidiagonal matrix). Doesn't know if that helps...
    – Jean Marie
    Dec 4 '18 at 23:50
















6














Find eigenvalues of the $(n+1) times (n+1)$-matrix



$$ left( begin {array}{cccccccc} 0&0&0&0&0&0&-n&0\ 0
&0&0&0&0&-(n-1)&0&1\ 0&0&0&0&-(n-2)&0&2&0
\ 0&0&0&ldots&0&3&0&0\ 0&0&-3&0&ldots&0
&0&0\ 0&-2&0&n-2&0&0&0&0\ -1&0&n-1&0
&0&0&0&0\ 0&n&0&0&0&0&0&0end {array} right)
$$



I have tried for some small $n$ by hand and have got that for $n=3$ the eigenvalues are $1,-1,3,-3$, for $n=4$ eigenvalues are $0,2,-2,4,-4$. So I am conjectured than for arbitrary $n$ the eigenvalues are $n, n-2, n-4, ldots, -n+2,-n.$



How to prove it?










share|cite|improve this question
























  • mostly you calculate a matrix with columns the eigenvectors, adjusting so they are all integers, and see if there is any pattern that holds up as $n$ increases
    – Will Jagy
    Dec 4 '18 at 22:40






  • 2




    Your matrix, say $M$, belongs to the so-called category of "skew centrosymmetric matrices" $JMJ=-M$ (where $J$ is the antidiagonal matrix). Doesn't know if that helps...
    – Jean Marie
    Dec 4 '18 at 23:50














6












6








6


0





Find eigenvalues of the $(n+1) times (n+1)$-matrix



$$ left( begin {array}{cccccccc} 0&0&0&0&0&0&-n&0\ 0
&0&0&0&0&-(n-1)&0&1\ 0&0&0&0&-(n-2)&0&2&0
\ 0&0&0&ldots&0&3&0&0\ 0&0&-3&0&ldots&0
&0&0\ 0&-2&0&n-2&0&0&0&0\ -1&0&n-1&0
&0&0&0&0\ 0&n&0&0&0&0&0&0end {array} right)
$$



I have tried for some small $n$ by hand and have got that for $n=3$ the eigenvalues are $1,-1,3,-3$, for $n=4$ eigenvalues are $0,2,-2,4,-4$. So I am conjectured than for arbitrary $n$ the eigenvalues are $n, n-2, n-4, ldots, -n+2,-n.$



How to prove it?










share|cite|improve this question















Find eigenvalues of the $(n+1) times (n+1)$-matrix



$$ left( begin {array}{cccccccc} 0&0&0&0&0&0&-n&0\ 0
&0&0&0&0&-(n-1)&0&1\ 0&0&0&0&-(n-2)&0&2&0
\ 0&0&0&ldots&0&3&0&0\ 0&0&-3&0&ldots&0
&0&0\ 0&-2&0&n-2&0&0&0&0\ -1&0&n-1&0
&0&0&0&0\ 0&n&0&0&0&0&0&0end {array} right)
$$



I have tried for some small $n$ by hand and have got that for $n=3$ the eigenvalues are $1,-1,3,-3$, for $n=4$ eigenvalues are $0,2,-2,4,-4$. So I am conjectured than for arbitrary $n$ the eigenvalues are $n, n-2, n-4, ldots, -n+2,-n.$



How to prove it?







linear-algebra matrices eigenvalues-eigenvectors






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Dec 4 '18 at 22:00







Leox

















asked Dec 4 '18 at 21:39









LeoxLeox

5,2431423




5,2431423












  • mostly you calculate a matrix with columns the eigenvectors, adjusting so they are all integers, and see if there is any pattern that holds up as $n$ increases
    – Will Jagy
    Dec 4 '18 at 22:40






  • 2




    Your matrix, say $M$, belongs to the so-called category of "skew centrosymmetric matrices" $JMJ=-M$ (where $J$ is the antidiagonal matrix). Doesn't know if that helps...
    – Jean Marie
    Dec 4 '18 at 23:50


















  • mostly you calculate a matrix with columns the eigenvectors, adjusting so they are all integers, and see if there is any pattern that holds up as $n$ increases
    – Will Jagy
    Dec 4 '18 at 22:40






  • 2




    Your matrix, say $M$, belongs to the so-called category of "skew centrosymmetric matrices" $JMJ=-M$ (where $J$ is the antidiagonal matrix). Doesn't know if that helps...
    – Jean Marie
    Dec 4 '18 at 23:50
















mostly you calculate a matrix with columns the eigenvectors, adjusting so they are all integers, and see if there is any pattern that holds up as $n$ increases
– Will Jagy
Dec 4 '18 at 22:40




mostly you calculate a matrix with columns the eigenvectors, adjusting so they are all integers, and see if there is any pattern that holds up as $n$ increases
– Will Jagy
Dec 4 '18 at 22:40




2




2




Your matrix, say $M$, belongs to the so-called category of "skew centrosymmetric matrices" $JMJ=-M$ (where $J$ is the antidiagonal matrix). Doesn't know if that helps...
– Jean Marie
Dec 4 '18 at 23:50




Your matrix, say $M$, belongs to the so-called category of "skew centrosymmetric matrices" $JMJ=-M$ (where $J$ is the antidiagonal matrix). Doesn't know if that helps...
– Jean Marie
Dec 4 '18 at 23:50










2 Answers
2






active

oldest

votes


















3














Call your matrix $B_{n+1}$. Denote the Kac matrix of the same size by
$$
A_{n+1}=pmatrix{
0&n\
1&0&n-1\
&2&ddots&ddots\
& &ddots&ddots&ddots\
& & &ddots&0 &1\
& & & & n &0}.
$$

It is known that the spectrum of the Kac matrix is given by
$$
sigma(A_{n+1})={-n,,-n+2,,-n+4,ldots,,n-4,,n-2,,n}.
$$

We shall prove that $sigma(A_{n+1})=sigma(B_{n+1})$. Consequently (as the Kac matrix has distinct eigenvalues) the two matrices are similar to each other.



Here is our proof. First, $B_{n+1}^2$ is similar to $A_{n+1}^2$. In fact, if $D$ denotes the $(n+1)times(n+1)$ diagonal matrix such that $d_{kk}=(-1)^{lfloor(k-1)/2rfloor}$ (i.e. $D=operatorname{diag}(1,1,-1,-1,1,1,-1,-1,ldots)$), a straightforward computation will show that $DB_{n+1}^2D=A_{n+1}^2$.



Thus $sigma(B_{n+1}^2)=sigma(A_{n+1}^2)$. Yet, as pointed out in the answer by Jean Marie, $B_{n+1}$ is similar to $-B_{n+1}$ via the reversal matrix (the mirror image of the identity matrix from left to right). Therefore, nonzero eigenvalues of $B_{n+1}$ must occur in pairs of the form $pmlambda$. Hence we have $sigma(B_{n+1})=sigma(A_{n+1})$, because the multiplicity of each nonzero eigenvalue of $A_{n+1}^2$ is exactly $2$.






share|cite|improve this answer





















  • Very clever manipulations...
    – Jean Marie
    Dec 6 '18 at 20:52










  • @user1551 Thank you!!
    – Leox
    Dec 7 '18 at 18:46



















2














This is very far from a complete answer (but too long to fit in a comment).



Let us call $M$ (or $M_n$ if we want to stress the dependency on $n$) the given matrix.



Let us establish the following property : if $lambda$ is an eigenvalue of $M$, then $-lambda$ is as well an eigenvalue of $M$. Said otherwise, the spectrum of $M$ is symmetric with respect to $O$.



Let $J$ be the antidiagonal matrix



($J_{ij}=1 iff i+j=n+2$ and $J_{ij}=0$ otherwise).



Please note that $J^{-1}=J$.



It is not difficult to establish that



$$JMJ=-M. tag{1}$$



This is a way to express that matrices like $M$ are "skew-centrosymmetric" (there is some literature on the subject).



Let $I$ be the $n+1$ dimensional unit matrix. Subtracting $lambda I$ to LHS and RHS of (1), one can write :



$$JMJ-lambda JIJ=-M-lambda I $$



Let us left- and -right factorize by $J$ :



$$J(M - lambda I)J=-(M+lambda I).$$



Taking determinants of both sides, we get :



$$det(J)^2det(M - lambda I)=(-1)^{n+1}det(M+lambda I).$$



Thus, as $det(J)neq 0$ (recall that $J^2=I$) :



$$det(M - lambda I)=0 iff det(M-(-lambda) I)=0,$$



proving the result.






share|cite|improve this answer























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    2 Answers
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    2 Answers
    2






    active

    oldest

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    active

    oldest

    votes






    active

    oldest

    votes









    3














    Call your matrix $B_{n+1}$. Denote the Kac matrix of the same size by
    $$
    A_{n+1}=pmatrix{
    0&n\
    1&0&n-1\
    &2&ddots&ddots\
    & &ddots&ddots&ddots\
    & & &ddots&0 &1\
    & & & & n &0}.
    $$

    It is known that the spectrum of the Kac matrix is given by
    $$
    sigma(A_{n+1})={-n,,-n+2,,-n+4,ldots,,n-4,,n-2,,n}.
    $$

    We shall prove that $sigma(A_{n+1})=sigma(B_{n+1})$. Consequently (as the Kac matrix has distinct eigenvalues) the two matrices are similar to each other.



    Here is our proof. First, $B_{n+1}^2$ is similar to $A_{n+1}^2$. In fact, if $D$ denotes the $(n+1)times(n+1)$ diagonal matrix such that $d_{kk}=(-1)^{lfloor(k-1)/2rfloor}$ (i.e. $D=operatorname{diag}(1,1,-1,-1,1,1,-1,-1,ldots)$), a straightforward computation will show that $DB_{n+1}^2D=A_{n+1}^2$.



    Thus $sigma(B_{n+1}^2)=sigma(A_{n+1}^2)$. Yet, as pointed out in the answer by Jean Marie, $B_{n+1}$ is similar to $-B_{n+1}$ via the reversal matrix (the mirror image of the identity matrix from left to right). Therefore, nonzero eigenvalues of $B_{n+1}$ must occur in pairs of the form $pmlambda$. Hence we have $sigma(B_{n+1})=sigma(A_{n+1})$, because the multiplicity of each nonzero eigenvalue of $A_{n+1}^2$ is exactly $2$.






    share|cite|improve this answer





















    • Very clever manipulations...
      – Jean Marie
      Dec 6 '18 at 20:52










    • @user1551 Thank you!!
      – Leox
      Dec 7 '18 at 18:46
















    3














    Call your matrix $B_{n+1}$. Denote the Kac matrix of the same size by
    $$
    A_{n+1}=pmatrix{
    0&n\
    1&0&n-1\
    &2&ddots&ddots\
    & &ddots&ddots&ddots\
    & & &ddots&0 &1\
    & & & & n &0}.
    $$

    It is known that the spectrum of the Kac matrix is given by
    $$
    sigma(A_{n+1})={-n,,-n+2,,-n+4,ldots,,n-4,,n-2,,n}.
    $$

    We shall prove that $sigma(A_{n+1})=sigma(B_{n+1})$. Consequently (as the Kac matrix has distinct eigenvalues) the two matrices are similar to each other.



    Here is our proof. First, $B_{n+1}^2$ is similar to $A_{n+1}^2$. In fact, if $D$ denotes the $(n+1)times(n+1)$ diagonal matrix such that $d_{kk}=(-1)^{lfloor(k-1)/2rfloor}$ (i.e. $D=operatorname{diag}(1,1,-1,-1,1,1,-1,-1,ldots)$), a straightforward computation will show that $DB_{n+1}^2D=A_{n+1}^2$.



    Thus $sigma(B_{n+1}^2)=sigma(A_{n+1}^2)$. Yet, as pointed out in the answer by Jean Marie, $B_{n+1}$ is similar to $-B_{n+1}$ via the reversal matrix (the mirror image of the identity matrix from left to right). Therefore, nonzero eigenvalues of $B_{n+1}$ must occur in pairs of the form $pmlambda$. Hence we have $sigma(B_{n+1})=sigma(A_{n+1})$, because the multiplicity of each nonzero eigenvalue of $A_{n+1}^2$ is exactly $2$.






    share|cite|improve this answer





















    • Very clever manipulations...
      – Jean Marie
      Dec 6 '18 at 20:52










    • @user1551 Thank you!!
      – Leox
      Dec 7 '18 at 18:46














    3












    3








    3






    Call your matrix $B_{n+1}$. Denote the Kac matrix of the same size by
    $$
    A_{n+1}=pmatrix{
    0&n\
    1&0&n-1\
    &2&ddots&ddots\
    & &ddots&ddots&ddots\
    & & &ddots&0 &1\
    & & & & n &0}.
    $$

    It is known that the spectrum of the Kac matrix is given by
    $$
    sigma(A_{n+1})={-n,,-n+2,,-n+4,ldots,,n-4,,n-2,,n}.
    $$

    We shall prove that $sigma(A_{n+1})=sigma(B_{n+1})$. Consequently (as the Kac matrix has distinct eigenvalues) the two matrices are similar to each other.



    Here is our proof. First, $B_{n+1}^2$ is similar to $A_{n+1}^2$. In fact, if $D$ denotes the $(n+1)times(n+1)$ diagonal matrix such that $d_{kk}=(-1)^{lfloor(k-1)/2rfloor}$ (i.e. $D=operatorname{diag}(1,1,-1,-1,1,1,-1,-1,ldots)$), a straightforward computation will show that $DB_{n+1}^2D=A_{n+1}^2$.



    Thus $sigma(B_{n+1}^2)=sigma(A_{n+1}^2)$. Yet, as pointed out in the answer by Jean Marie, $B_{n+1}$ is similar to $-B_{n+1}$ via the reversal matrix (the mirror image of the identity matrix from left to right). Therefore, nonzero eigenvalues of $B_{n+1}$ must occur in pairs of the form $pmlambda$. Hence we have $sigma(B_{n+1})=sigma(A_{n+1})$, because the multiplicity of each nonzero eigenvalue of $A_{n+1}^2$ is exactly $2$.






    share|cite|improve this answer












    Call your matrix $B_{n+1}$. Denote the Kac matrix of the same size by
    $$
    A_{n+1}=pmatrix{
    0&n\
    1&0&n-1\
    &2&ddots&ddots\
    & &ddots&ddots&ddots\
    & & &ddots&0 &1\
    & & & & n &0}.
    $$

    It is known that the spectrum of the Kac matrix is given by
    $$
    sigma(A_{n+1})={-n,,-n+2,,-n+4,ldots,,n-4,,n-2,,n}.
    $$

    We shall prove that $sigma(A_{n+1})=sigma(B_{n+1})$. Consequently (as the Kac matrix has distinct eigenvalues) the two matrices are similar to each other.



    Here is our proof. First, $B_{n+1}^2$ is similar to $A_{n+1}^2$. In fact, if $D$ denotes the $(n+1)times(n+1)$ diagonal matrix such that $d_{kk}=(-1)^{lfloor(k-1)/2rfloor}$ (i.e. $D=operatorname{diag}(1,1,-1,-1,1,1,-1,-1,ldots)$), a straightforward computation will show that $DB_{n+1}^2D=A_{n+1}^2$.



    Thus $sigma(B_{n+1}^2)=sigma(A_{n+1}^2)$. Yet, as pointed out in the answer by Jean Marie, $B_{n+1}$ is similar to $-B_{n+1}$ via the reversal matrix (the mirror image of the identity matrix from left to right). Therefore, nonzero eigenvalues of $B_{n+1}$ must occur in pairs of the form $pmlambda$. Hence we have $sigma(B_{n+1})=sigma(A_{n+1})$, because the multiplicity of each nonzero eigenvalue of $A_{n+1}^2$ is exactly $2$.







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Dec 6 '18 at 20:10









    user1551user1551

    71.8k566125




    71.8k566125












    • Very clever manipulations...
      – Jean Marie
      Dec 6 '18 at 20:52










    • @user1551 Thank you!!
      – Leox
      Dec 7 '18 at 18:46


















    • Very clever manipulations...
      – Jean Marie
      Dec 6 '18 at 20:52










    • @user1551 Thank you!!
      – Leox
      Dec 7 '18 at 18:46
















    Very clever manipulations...
    – Jean Marie
    Dec 6 '18 at 20:52




    Very clever manipulations...
    – Jean Marie
    Dec 6 '18 at 20:52












    @user1551 Thank you!!
    – Leox
    Dec 7 '18 at 18:46




    @user1551 Thank you!!
    – Leox
    Dec 7 '18 at 18:46











    2














    This is very far from a complete answer (but too long to fit in a comment).



    Let us call $M$ (or $M_n$ if we want to stress the dependency on $n$) the given matrix.



    Let us establish the following property : if $lambda$ is an eigenvalue of $M$, then $-lambda$ is as well an eigenvalue of $M$. Said otherwise, the spectrum of $M$ is symmetric with respect to $O$.



    Let $J$ be the antidiagonal matrix



    ($J_{ij}=1 iff i+j=n+2$ and $J_{ij}=0$ otherwise).



    Please note that $J^{-1}=J$.



    It is not difficult to establish that



    $$JMJ=-M. tag{1}$$



    This is a way to express that matrices like $M$ are "skew-centrosymmetric" (there is some literature on the subject).



    Let $I$ be the $n+1$ dimensional unit matrix. Subtracting $lambda I$ to LHS and RHS of (1), one can write :



    $$JMJ-lambda JIJ=-M-lambda I $$



    Let us left- and -right factorize by $J$ :



    $$J(M - lambda I)J=-(M+lambda I).$$



    Taking determinants of both sides, we get :



    $$det(J)^2det(M - lambda I)=(-1)^{n+1}det(M+lambda I).$$



    Thus, as $det(J)neq 0$ (recall that $J^2=I$) :



    $$det(M - lambda I)=0 iff det(M-(-lambda) I)=0,$$



    proving the result.






    share|cite|improve this answer




























      2














      This is very far from a complete answer (but too long to fit in a comment).



      Let us call $M$ (or $M_n$ if we want to stress the dependency on $n$) the given matrix.



      Let us establish the following property : if $lambda$ is an eigenvalue of $M$, then $-lambda$ is as well an eigenvalue of $M$. Said otherwise, the spectrum of $M$ is symmetric with respect to $O$.



      Let $J$ be the antidiagonal matrix



      ($J_{ij}=1 iff i+j=n+2$ and $J_{ij}=0$ otherwise).



      Please note that $J^{-1}=J$.



      It is not difficult to establish that



      $$JMJ=-M. tag{1}$$



      This is a way to express that matrices like $M$ are "skew-centrosymmetric" (there is some literature on the subject).



      Let $I$ be the $n+1$ dimensional unit matrix. Subtracting $lambda I$ to LHS and RHS of (1), one can write :



      $$JMJ-lambda JIJ=-M-lambda I $$



      Let us left- and -right factorize by $J$ :



      $$J(M - lambda I)J=-(M+lambda I).$$



      Taking determinants of both sides, we get :



      $$det(J)^2det(M - lambda I)=(-1)^{n+1}det(M+lambda I).$$



      Thus, as $det(J)neq 0$ (recall that $J^2=I$) :



      $$det(M - lambda I)=0 iff det(M-(-lambda) I)=0,$$



      proving the result.






      share|cite|improve this answer


























        2












        2








        2






        This is very far from a complete answer (but too long to fit in a comment).



        Let us call $M$ (or $M_n$ if we want to stress the dependency on $n$) the given matrix.



        Let us establish the following property : if $lambda$ is an eigenvalue of $M$, then $-lambda$ is as well an eigenvalue of $M$. Said otherwise, the spectrum of $M$ is symmetric with respect to $O$.



        Let $J$ be the antidiagonal matrix



        ($J_{ij}=1 iff i+j=n+2$ and $J_{ij}=0$ otherwise).



        Please note that $J^{-1}=J$.



        It is not difficult to establish that



        $$JMJ=-M. tag{1}$$



        This is a way to express that matrices like $M$ are "skew-centrosymmetric" (there is some literature on the subject).



        Let $I$ be the $n+1$ dimensional unit matrix. Subtracting $lambda I$ to LHS and RHS of (1), one can write :



        $$JMJ-lambda JIJ=-M-lambda I $$



        Let us left- and -right factorize by $J$ :



        $$J(M - lambda I)J=-(M+lambda I).$$



        Taking determinants of both sides, we get :



        $$det(J)^2det(M - lambda I)=(-1)^{n+1}det(M+lambda I).$$



        Thus, as $det(J)neq 0$ (recall that $J^2=I$) :



        $$det(M - lambda I)=0 iff det(M-(-lambda) I)=0,$$



        proving the result.






        share|cite|improve this answer














        This is very far from a complete answer (but too long to fit in a comment).



        Let us call $M$ (or $M_n$ if we want to stress the dependency on $n$) the given matrix.



        Let us establish the following property : if $lambda$ is an eigenvalue of $M$, then $-lambda$ is as well an eigenvalue of $M$. Said otherwise, the spectrum of $M$ is symmetric with respect to $O$.



        Let $J$ be the antidiagonal matrix



        ($J_{ij}=1 iff i+j=n+2$ and $J_{ij}=0$ otherwise).



        Please note that $J^{-1}=J$.



        It is not difficult to establish that



        $$JMJ=-M. tag{1}$$



        This is a way to express that matrices like $M$ are "skew-centrosymmetric" (there is some literature on the subject).



        Let $I$ be the $n+1$ dimensional unit matrix. Subtracting $lambda I$ to LHS and RHS of (1), one can write :



        $$JMJ-lambda JIJ=-M-lambda I $$



        Let us left- and -right factorize by $J$ :



        $$J(M - lambda I)J=-(M+lambda I).$$



        Taking determinants of both sides, we get :



        $$det(J)^2det(M - lambda I)=(-1)^{n+1}det(M+lambda I).$$



        Thus, as $det(J)neq 0$ (recall that $J^2=I$) :



        $$det(M - lambda I)=0 iff det(M-(-lambda) I)=0,$$



        proving the result.







        share|cite|improve this answer














        share|cite|improve this answer



        share|cite|improve this answer








        edited Dec 6 '18 at 20:41

























        answered Dec 5 '18 at 20:38









        Jean MarieJean Marie

        28.9k41949




        28.9k41949






























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