Finding the Jordan Form of a matrix…












1














I know that this type of question has been asked on here before but I am still having a hard understanding what is going on. The text that I am learning from is "Linear Algebra Done Right by Sheldon Axler" and I don't think it covers this topic very well. Does anyone have any suggestions? I am studying for my linear comp at the end of January.



Given the matrix



$$A = left(begin{matrix}0&-1&-1\-3&-1&-2\7&5&6end{matrix}right).$$



Find the Jordan form $J$ and an invertible matrix $Q$ such that $A = QJQ^{-1}$.



I know that I want to start by finding the eigenvalues of this matrix. I ended up getting det$(A - lambda I) = (lambda + 2)(lambda62 + 3lambda - 2) implies lambda = -2, frac{-3 -sqrt{17}}{2}, frac{sqrt{17} - 3}{2}$.



Now, from what I have read on this website, I know now that I want to find the null space of $A - lambda I$ for each eigenvalue. Then my invertible matrix $Q$ will have entries whose columns are these vectors. Why does one do this? Is there a "nicer" way to go about doing this other than direct computation?



This problem is from a previous linear comp at my university, so I can't imagine that they would have wanted me to the direct calculation.



Hopefully, my questions and explanation of the problem were clear. Thanks for any help in advance.










share|cite|improve this question



























    1














    I know that this type of question has been asked on here before but I am still having a hard understanding what is going on. The text that I am learning from is "Linear Algebra Done Right by Sheldon Axler" and I don't think it covers this topic very well. Does anyone have any suggestions? I am studying for my linear comp at the end of January.



    Given the matrix



    $$A = left(begin{matrix}0&-1&-1\-3&-1&-2\7&5&6end{matrix}right).$$



    Find the Jordan form $J$ and an invertible matrix $Q$ such that $A = QJQ^{-1}$.



    I know that I want to start by finding the eigenvalues of this matrix. I ended up getting det$(A - lambda I) = (lambda + 2)(lambda62 + 3lambda - 2) implies lambda = -2, frac{-3 -sqrt{17}}{2}, frac{sqrt{17} - 3}{2}$.



    Now, from what I have read on this website, I know now that I want to find the null space of $A - lambda I$ for each eigenvalue. Then my invertible matrix $Q$ will have entries whose columns are these vectors. Why does one do this? Is there a "nicer" way to go about doing this other than direct computation?



    This problem is from a previous linear comp at my university, so I can't imagine that they would have wanted me to the direct calculation.



    Hopefully, my questions and explanation of the problem were clear. Thanks for any help in advance.










    share|cite|improve this question

























      1












      1








      1







      I know that this type of question has been asked on here before but I am still having a hard understanding what is going on. The text that I am learning from is "Linear Algebra Done Right by Sheldon Axler" and I don't think it covers this topic very well. Does anyone have any suggestions? I am studying for my linear comp at the end of January.



      Given the matrix



      $$A = left(begin{matrix}0&-1&-1\-3&-1&-2\7&5&6end{matrix}right).$$



      Find the Jordan form $J$ and an invertible matrix $Q$ such that $A = QJQ^{-1}$.



      I know that I want to start by finding the eigenvalues of this matrix. I ended up getting det$(A - lambda I) = (lambda + 2)(lambda62 + 3lambda - 2) implies lambda = -2, frac{-3 -sqrt{17}}{2}, frac{sqrt{17} - 3}{2}$.



      Now, from what I have read on this website, I know now that I want to find the null space of $A - lambda I$ for each eigenvalue. Then my invertible matrix $Q$ will have entries whose columns are these vectors. Why does one do this? Is there a "nicer" way to go about doing this other than direct computation?



      This problem is from a previous linear comp at my university, so I can't imagine that they would have wanted me to the direct calculation.



      Hopefully, my questions and explanation of the problem were clear. Thanks for any help in advance.










      share|cite|improve this question













      I know that this type of question has been asked on here before but I am still having a hard understanding what is going on. The text that I am learning from is "Linear Algebra Done Right by Sheldon Axler" and I don't think it covers this topic very well. Does anyone have any suggestions? I am studying for my linear comp at the end of January.



      Given the matrix



      $$A = left(begin{matrix}0&-1&-1\-3&-1&-2\7&5&6end{matrix}right).$$



      Find the Jordan form $J$ and an invertible matrix $Q$ such that $A = QJQ^{-1}$.



      I know that I want to start by finding the eigenvalues of this matrix. I ended up getting det$(A - lambda I) = (lambda + 2)(lambda62 + 3lambda - 2) implies lambda = -2, frac{-3 -sqrt{17}}{2}, frac{sqrt{17} - 3}{2}$.



      Now, from what I have read on this website, I know now that I want to find the null space of $A - lambda I$ for each eigenvalue. Then my invertible matrix $Q$ will have entries whose columns are these vectors. Why does one do this? Is there a "nicer" way to go about doing this other than direct computation?



      This problem is from a previous linear comp at my university, so I can't imagine that they would have wanted me to the direct calculation.



      Hopefully, my questions and explanation of the problem were clear. Thanks for any help in advance.







      linear-algebra eigenvalues-eigenvectors jordan-normal-form generalizedeigenvector






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      share|cite|improve this question











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      asked yesterday









      Taylor McMillan

      523




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          1














          The characteristic polynomial is
          $$
          p(lambda)=lambda^3-5lambda^2+8lambda-4=(lambda-1)(lambda-2)^2.
          $$

          The vectors involved in the Jordan blocks associated with eigenvalue $2$ is the column space of
          $$
          A-I = begin{pmatrix}-1 & -1 & -1 \
          -3 & -2 & -2 \
          7 & 5 & 5end{pmatrix}.
          $$



          The column space of $A-I$ is two-dimensional, which gives $mbox{dim}(mbox{ker}(A-I))=1$, and it is obvious that $mbox{ker}(A-I)$ is spanned by
          $$
          begin{pmatrix}0 \ 1 \ -1end{pmatrix}.
          $$

          (It is obvious because the last two columns of $A-I$ are identical.) Then
          begin{align}
          (A-2I)(A-I)&=begin{pmatrix}-2 & -1 & -1 \
          -3 & -3 & -2 \
          7 & 5 & 4end{pmatrix}.
          begin{pmatrix}-1 & -1 & -1 \
          -3 & -2 & -2 \
          7 & 5 & 5end{pmatrix} \
          &= begin{pmatrix}-2 & -1 & -1 \
          -2 & -1 & -1 \
          6 & 3 & 3end{pmatrix}
          end{align}

          Because $(A-2I)^2(A-I)=0$, you have
          begin{align}
          (A-2I)begin{pmatrix} -1 \ -2 \ 5end{pmatrix}&=begin{pmatrix}-1 \ -1 \ 3end{pmatrix} \
          (A-2I)begin{pmatrix}-1 \ -1 \ 3end{pmatrix} &= 0.
          end{align}

          The Jordan form is
          $$
          J = begin{pmatrix} 1 & 0 & 0 \
          0 & 2 & 1 \
          0 & 0 & 2end{pmatrix}
          $$

          and the transition matrix $Q$ is
          $$
          Q = begin{pmatrix} 0 & -1 & -1 \
          1 & -1 & -2 \
          -1 & 3 & 5
          end{pmatrix}.
          $$






          share|cite|improve this answer































            3














            Eigenvalues are $2,2,1$, which simplifies the calculation a lot.






            share|cite|improve this answer





















            • Well I better double check my work, thank you.
              – Taylor McMillan
              yesterday






            • 2




              A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
              – A. P
              yesterday










            • Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
              – Taylor McMillan
              yesterday






            • 1




              simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
              – A. P
              yesterday











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            2 Answers
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            2 Answers
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            active

            oldest

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            1














            The characteristic polynomial is
            $$
            p(lambda)=lambda^3-5lambda^2+8lambda-4=(lambda-1)(lambda-2)^2.
            $$

            The vectors involved in the Jordan blocks associated with eigenvalue $2$ is the column space of
            $$
            A-I = begin{pmatrix}-1 & -1 & -1 \
            -3 & -2 & -2 \
            7 & 5 & 5end{pmatrix}.
            $$



            The column space of $A-I$ is two-dimensional, which gives $mbox{dim}(mbox{ker}(A-I))=1$, and it is obvious that $mbox{ker}(A-I)$ is spanned by
            $$
            begin{pmatrix}0 \ 1 \ -1end{pmatrix}.
            $$

            (It is obvious because the last two columns of $A-I$ are identical.) Then
            begin{align}
            (A-2I)(A-I)&=begin{pmatrix}-2 & -1 & -1 \
            -3 & -3 & -2 \
            7 & 5 & 4end{pmatrix}.
            begin{pmatrix}-1 & -1 & -1 \
            -3 & -2 & -2 \
            7 & 5 & 5end{pmatrix} \
            &= begin{pmatrix}-2 & -1 & -1 \
            -2 & -1 & -1 \
            6 & 3 & 3end{pmatrix}
            end{align}

            Because $(A-2I)^2(A-I)=0$, you have
            begin{align}
            (A-2I)begin{pmatrix} -1 \ -2 \ 5end{pmatrix}&=begin{pmatrix}-1 \ -1 \ 3end{pmatrix} \
            (A-2I)begin{pmatrix}-1 \ -1 \ 3end{pmatrix} &= 0.
            end{align}

            The Jordan form is
            $$
            J = begin{pmatrix} 1 & 0 & 0 \
            0 & 2 & 1 \
            0 & 0 & 2end{pmatrix}
            $$

            and the transition matrix $Q$ is
            $$
            Q = begin{pmatrix} 0 & -1 & -1 \
            1 & -1 & -2 \
            -1 & 3 & 5
            end{pmatrix}.
            $$






            share|cite|improve this answer




























              1














              The characteristic polynomial is
              $$
              p(lambda)=lambda^3-5lambda^2+8lambda-4=(lambda-1)(lambda-2)^2.
              $$

              The vectors involved in the Jordan blocks associated with eigenvalue $2$ is the column space of
              $$
              A-I = begin{pmatrix}-1 & -1 & -1 \
              -3 & -2 & -2 \
              7 & 5 & 5end{pmatrix}.
              $$



              The column space of $A-I$ is two-dimensional, which gives $mbox{dim}(mbox{ker}(A-I))=1$, and it is obvious that $mbox{ker}(A-I)$ is spanned by
              $$
              begin{pmatrix}0 \ 1 \ -1end{pmatrix}.
              $$

              (It is obvious because the last two columns of $A-I$ are identical.) Then
              begin{align}
              (A-2I)(A-I)&=begin{pmatrix}-2 & -1 & -1 \
              -3 & -3 & -2 \
              7 & 5 & 4end{pmatrix}.
              begin{pmatrix}-1 & -1 & -1 \
              -3 & -2 & -2 \
              7 & 5 & 5end{pmatrix} \
              &= begin{pmatrix}-2 & -1 & -1 \
              -2 & -1 & -1 \
              6 & 3 & 3end{pmatrix}
              end{align}

              Because $(A-2I)^2(A-I)=0$, you have
              begin{align}
              (A-2I)begin{pmatrix} -1 \ -2 \ 5end{pmatrix}&=begin{pmatrix}-1 \ -1 \ 3end{pmatrix} \
              (A-2I)begin{pmatrix}-1 \ -1 \ 3end{pmatrix} &= 0.
              end{align}

              The Jordan form is
              $$
              J = begin{pmatrix} 1 & 0 & 0 \
              0 & 2 & 1 \
              0 & 0 & 2end{pmatrix}
              $$

              and the transition matrix $Q$ is
              $$
              Q = begin{pmatrix} 0 & -1 & -1 \
              1 & -1 & -2 \
              -1 & 3 & 5
              end{pmatrix}.
              $$






              share|cite|improve this answer


























                1












                1








                1






                The characteristic polynomial is
                $$
                p(lambda)=lambda^3-5lambda^2+8lambda-4=(lambda-1)(lambda-2)^2.
                $$

                The vectors involved in the Jordan blocks associated with eigenvalue $2$ is the column space of
                $$
                A-I = begin{pmatrix}-1 & -1 & -1 \
                -3 & -2 & -2 \
                7 & 5 & 5end{pmatrix}.
                $$



                The column space of $A-I$ is two-dimensional, which gives $mbox{dim}(mbox{ker}(A-I))=1$, and it is obvious that $mbox{ker}(A-I)$ is spanned by
                $$
                begin{pmatrix}0 \ 1 \ -1end{pmatrix}.
                $$

                (It is obvious because the last two columns of $A-I$ are identical.) Then
                begin{align}
                (A-2I)(A-I)&=begin{pmatrix}-2 & -1 & -1 \
                -3 & -3 & -2 \
                7 & 5 & 4end{pmatrix}.
                begin{pmatrix}-1 & -1 & -1 \
                -3 & -2 & -2 \
                7 & 5 & 5end{pmatrix} \
                &= begin{pmatrix}-2 & -1 & -1 \
                -2 & -1 & -1 \
                6 & 3 & 3end{pmatrix}
                end{align}

                Because $(A-2I)^2(A-I)=0$, you have
                begin{align}
                (A-2I)begin{pmatrix} -1 \ -2 \ 5end{pmatrix}&=begin{pmatrix}-1 \ -1 \ 3end{pmatrix} \
                (A-2I)begin{pmatrix}-1 \ -1 \ 3end{pmatrix} &= 0.
                end{align}

                The Jordan form is
                $$
                J = begin{pmatrix} 1 & 0 & 0 \
                0 & 2 & 1 \
                0 & 0 & 2end{pmatrix}
                $$

                and the transition matrix $Q$ is
                $$
                Q = begin{pmatrix} 0 & -1 & -1 \
                1 & -1 & -2 \
                -1 & 3 & 5
                end{pmatrix}.
                $$






                share|cite|improve this answer














                The characteristic polynomial is
                $$
                p(lambda)=lambda^3-5lambda^2+8lambda-4=(lambda-1)(lambda-2)^2.
                $$

                The vectors involved in the Jordan blocks associated with eigenvalue $2$ is the column space of
                $$
                A-I = begin{pmatrix}-1 & -1 & -1 \
                -3 & -2 & -2 \
                7 & 5 & 5end{pmatrix}.
                $$



                The column space of $A-I$ is two-dimensional, which gives $mbox{dim}(mbox{ker}(A-I))=1$, and it is obvious that $mbox{ker}(A-I)$ is spanned by
                $$
                begin{pmatrix}0 \ 1 \ -1end{pmatrix}.
                $$

                (It is obvious because the last two columns of $A-I$ are identical.) Then
                begin{align}
                (A-2I)(A-I)&=begin{pmatrix}-2 & -1 & -1 \
                -3 & -3 & -2 \
                7 & 5 & 4end{pmatrix}.
                begin{pmatrix}-1 & -1 & -1 \
                -3 & -2 & -2 \
                7 & 5 & 5end{pmatrix} \
                &= begin{pmatrix}-2 & -1 & -1 \
                -2 & -1 & -1 \
                6 & 3 & 3end{pmatrix}
                end{align}

                Because $(A-2I)^2(A-I)=0$, you have
                begin{align}
                (A-2I)begin{pmatrix} -1 \ -2 \ 5end{pmatrix}&=begin{pmatrix}-1 \ -1 \ 3end{pmatrix} \
                (A-2I)begin{pmatrix}-1 \ -1 \ 3end{pmatrix} &= 0.
                end{align}

                The Jordan form is
                $$
                J = begin{pmatrix} 1 & 0 & 0 \
                0 & 2 & 1 \
                0 & 0 & 2end{pmatrix}
                $$

                and the transition matrix $Q$ is
                $$
                Q = begin{pmatrix} 0 & -1 & -1 \
                1 & -1 & -2 \
                -1 & 3 & 5
                end{pmatrix}.
                $$







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited yesterday

























                answered yesterday









                DisintegratingByParts

                58.7k42579




                58.7k42579























                    3














                    Eigenvalues are $2,2,1$, which simplifies the calculation a lot.






                    share|cite|improve this answer





















                    • Well I better double check my work, thank you.
                      – Taylor McMillan
                      yesterday






                    • 2




                      A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
                      – A. P
                      yesterday










                    • Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
                      – Taylor McMillan
                      yesterday






                    • 1




                      simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
                      – A. P
                      yesterday
















                    3














                    Eigenvalues are $2,2,1$, which simplifies the calculation a lot.






                    share|cite|improve this answer





















                    • Well I better double check my work, thank you.
                      – Taylor McMillan
                      yesterday






                    • 2




                      A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
                      – A. P
                      yesterday










                    • Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
                      – Taylor McMillan
                      yesterday






                    • 1




                      simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
                      – A. P
                      yesterday














                    3












                    3








                    3






                    Eigenvalues are $2,2,1$, which simplifies the calculation a lot.






                    share|cite|improve this answer












                    Eigenvalues are $2,2,1$, which simplifies the calculation a lot.







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered yesterday









                    A.Γ.

                    22.6k32656




                    22.6k32656












                    • Well I better double check my work, thank you.
                      – Taylor McMillan
                      yesterday






                    • 2




                      A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
                      – A. P
                      yesterday










                    • Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
                      – Taylor McMillan
                      yesterday






                    • 1




                      simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
                      – A. P
                      yesterday


















                    • Well I better double check my work, thank you.
                      – Taylor McMillan
                      yesterday






                    • 2




                      A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
                      – A. P
                      yesterday










                    • Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
                      – Taylor McMillan
                      yesterday






                    • 1




                      simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
                      – A. P
                      yesterday
















                    Well I better double check my work, thank you.
                    – Taylor McMillan
                    yesterday




                    Well I better double check my work, thank you.
                    – Taylor McMillan
                    yesterday




                    2




                    2




                    A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
                    – A. P
                    yesterday




                    A simple way to check if you Eigenvalues $lambda_j ,jin {1...n}$ for a $ntimes n$ Matrix are correct is to check if $sum_{j=1}^nlambda_j=trace(A)$ and if $prod_{j=1}^nlambda_j=det(A)$
                    – A. P
                    yesterday












                    Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
                    – Taylor McMillan
                    yesterday




                    Thanks @A.P. Also, how do I handle finding the null space for the eigenvalue 2 that has multiplicity 2?
                    – Taylor McMillan
                    yesterday




                    1




                    1




                    simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
                    – A. P
                    yesterday




                    simply use the same algorithm as for calculating the Nullspace for the value 1. In this case you will notice that you only get one Vektor, therefore the dimension of the Nullspace for $lamda=2$ is 1.
                    – A. P
                    yesterday


















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