Why don't similar matrices have same eigenvectors and eigenvalues?











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What is wrong with this proof:
Suppose R and T are similar operators and R has eigenvalue 2 for some eigenvector $v$.
By property of similar matrices:



$R=STS^{-1}$



Therefore:



$Rv = STS^{-1}v$



$2v = STS^{-1}v$



$2S^{-1}v = TS^{-1}v$



$2S^{-1}Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



Thus $v$ is also an eigenvector of $T$ with eigenvalue 2.
Clearly this proof is incorrect, but where does it go wrong?










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  • Protip: if you're wondering why a calculation fails to be valid, and you know of a counterexample, try evaluating each step using your counterexample, and pick the first line where the two sides fail to be equal. Then you know that this step is the problem, and often the counterexample will show you why the step was problematic. You can use this technique to defeat every $-1 = 1$ fake proof out there!
    – Theo Bendit
    5 hours ago















up vote
2
down vote

favorite
1












What is wrong with this proof:
Suppose R and T are similar operators and R has eigenvalue 2 for some eigenvector $v$.
By property of similar matrices:



$R=STS^{-1}$



Therefore:



$Rv = STS^{-1}v$



$2v = STS^{-1}v$



$2S^{-1}v = TS^{-1}v$



$2S^{-1}Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



Thus $v$ is also an eigenvector of $T$ with eigenvalue 2.
Clearly this proof is incorrect, but where does it go wrong?










share|cite|improve this question







New contributor




Justin Sanders is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




















  • Protip: if you're wondering why a calculation fails to be valid, and you know of a counterexample, try evaluating each step using your counterexample, and pick the first line where the two sides fail to be equal. Then you know that this step is the problem, and often the counterexample will show you why the step was problematic. You can use this technique to defeat every $-1 = 1$ fake proof out there!
    – Theo Bendit
    5 hours ago













up vote
2
down vote

favorite
1









up vote
2
down vote

favorite
1






1





What is wrong with this proof:
Suppose R and T are similar operators and R has eigenvalue 2 for some eigenvector $v$.
By property of similar matrices:



$R=STS^{-1}$



Therefore:



$Rv = STS^{-1}v$



$2v = STS^{-1}v$



$2S^{-1}v = TS^{-1}v$



$2S^{-1}Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



Thus $v$ is also an eigenvector of $T$ with eigenvalue 2.
Clearly this proof is incorrect, but where does it go wrong?










share|cite|improve this question







New contributor




Justin Sanders is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











What is wrong with this proof:
Suppose R and T are similar operators and R has eigenvalue 2 for some eigenvector $v$.
By property of similar matrices:



$R=STS^{-1}$



Therefore:



$Rv = STS^{-1}v$



$2v = STS^{-1}v$



$2S^{-1}v = TS^{-1}v$



$2S^{-1}Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



Thus $v$ is also an eigenvector of $T$ with eigenvalue 2.
Clearly this proof is incorrect, but where does it go wrong?







linear-algebra matrices eigenvalues-eigenvectors inverse






share|cite|improve this question







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Justin Sanders is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|cite|improve this question







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Justin Sanders is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 6 hours ago









Justin Sanders

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Check out our Code of Conduct.












  • Protip: if you're wondering why a calculation fails to be valid, and you know of a counterexample, try evaluating each step using your counterexample, and pick the first line where the two sides fail to be equal. Then you know that this step is the problem, and often the counterexample will show you why the step was problematic. You can use this technique to defeat every $-1 = 1$ fake proof out there!
    – Theo Bendit
    5 hours ago


















  • Protip: if you're wondering why a calculation fails to be valid, and you know of a counterexample, try evaluating each step using your counterexample, and pick the first line where the two sides fail to be equal. Then you know that this step is the problem, and often the counterexample will show you why the step was problematic. You can use this technique to defeat every $-1 = 1$ fake proof out there!
    – Theo Bendit
    5 hours ago
















Protip: if you're wondering why a calculation fails to be valid, and you know of a counterexample, try evaluating each step using your counterexample, and pick the first line where the two sides fail to be equal. Then you know that this step is the problem, and often the counterexample will show you why the step was problematic. You can use this technique to defeat every $-1 = 1$ fake proof out there!
– Theo Bendit
5 hours ago




Protip: if you're wondering why a calculation fails to be valid, and you know of a counterexample, try evaluating each step using your counterexample, and pick the first line where the two sides fail to be equal. Then you know that this step is the problem, and often the counterexample will show you why the step was problematic. You can use this technique to defeat every $-1 = 1$ fake proof out there!
– Theo Bendit
5 hours ago










3 Answers
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up vote
2
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When you went from
$$
2S^{-1}v=TS^{-1}v
$$

to
$$
2S^{-1}Sv=Tv
$$

you completely ignored the rules of matrix multiplcation; you cannot arbitrarily commute matrices in a product! It should have read as follows:
$$
2S^{-1}vS=TS^{-1}vS
$$

Also not that it is not even clear that this is well defined as written, since $v$ is probably a column vector.






share|cite|improve this answer








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    up vote
    2
    down vote













    It's wrong when you jump from $2S^{-1}v=TS^{-1}v$ to $2S^{-1}Sv=Tv$.






    share|cite|improve this answer




























      up vote
      2
      down vote













      Since the error in our OP Justin Sanders' argument has been thoroughly vetted in other answers, here I will directly address the title question.



      Similar matrices do have the same eigenvalues, to wit:



      if



      $B = PAP^{-1}, tag 1$



      then



      $B - lambda I = PAP^{-1} - lambda I = PAP^{-1} - lambda PIP^{-1} = P(A - lambda I)P^{-1}, tag 2$



      whence



      $det(B - lambda I) = det(P(A - lambda I)P^{-1}) = det(P) det(A - lambda I) det (P^{-1}) = det(A - lambda I), tag 3$



      since $det(P^{-1}) = (det(P))^{-1}; tag 4$



      thus $A$ and $B$, having the same characteristic polynomials, also share the roots of these polynomials, i.e., their eigenvalues.



      However, similar matrices do not in general share eigenvectors; if



      $B vec v = lambda vec v, tag 5$



      then



      $PAP^{-1} vec v = lambda vec v, tag 6$



      or



      $AP^{-1} vec v = lambda P^{-1} vec v, tag 7$



      that is, $P^{-1} vec v$ is an eigenvector of $A$ corresponding to $lambda$; indeed, if $lambda$ is a root of (3) of multiplicity one, then $P^{-1} vec v$ is, up to a scale factor, the eigenvector of $A$ associated with $lambda$. Since we can in general choose $P$ so that $P^{-1} vec v ne vec v$, the eigenvectors will not be shared 'twixt' $A$ and $B$.



      So while the eigenvalues are similarity invariant, the eigenvectors transform according to $vec v mapsto P^{-1} vec v$.






      share|cite|improve this answer





















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        3 Answers
        3






        active

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        3 Answers
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        up vote
        2
        down vote













        When you went from
        $$
        2S^{-1}v=TS^{-1}v
        $$

        to
        $$
        2S^{-1}Sv=Tv
        $$

        you completely ignored the rules of matrix multiplcation; you cannot arbitrarily commute matrices in a product! It should have read as follows:
        $$
        2S^{-1}vS=TS^{-1}vS
        $$

        Also not that it is not even clear that this is well defined as written, since $v$ is probably a column vector.






        share|cite|improve this answer








        New contributor




        GenericMathMan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
        Check out our Code of Conduct.






















          up vote
          2
          down vote













          When you went from
          $$
          2S^{-1}v=TS^{-1}v
          $$

          to
          $$
          2S^{-1}Sv=Tv
          $$

          you completely ignored the rules of matrix multiplcation; you cannot arbitrarily commute matrices in a product! It should have read as follows:
          $$
          2S^{-1}vS=TS^{-1}vS
          $$

          Also not that it is not even clear that this is well defined as written, since $v$ is probably a column vector.






          share|cite|improve this answer








          New contributor




          GenericMathMan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.




















            up vote
            2
            down vote










            up vote
            2
            down vote









            When you went from
            $$
            2S^{-1}v=TS^{-1}v
            $$

            to
            $$
            2S^{-1}Sv=Tv
            $$

            you completely ignored the rules of matrix multiplcation; you cannot arbitrarily commute matrices in a product! It should have read as follows:
            $$
            2S^{-1}vS=TS^{-1}vS
            $$

            Also not that it is not even clear that this is well defined as written, since $v$ is probably a column vector.






            share|cite|improve this answer








            New contributor




            GenericMathMan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.









            When you went from
            $$
            2S^{-1}v=TS^{-1}v
            $$

            to
            $$
            2S^{-1}Sv=Tv
            $$

            you completely ignored the rules of matrix multiplcation; you cannot arbitrarily commute matrices in a product! It should have read as follows:
            $$
            2S^{-1}vS=TS^{-1}vS
            $$

            Also not that it is not even clear that this is well defined as written, since $v$ is probably a column vector.







            share|cite|improve this answer








            New contributor




            GenericMathMan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.









            share|cite|improve this answer



            share|cite|improve this answer






            New contributor




            GenericMathMan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.









            answered 6 hours ago









            GenericMathMan

            311




            311




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            New contributor





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                up vote
                2
                down vote













                It's wrong when you jump from $2S^{-1}v=TS^{-1}v$ to $2S^{-1}Sv=Tv$.






                share|cite|improve this answer

























                  up vote
                  2
                  down vote













                  It's wrong when you jump from $2S^{-1}v=TS^{-1}v$ to $2S^{-1}Sv=Tv$.






                  share|cite|improve this answer























                    up vote
                    2
                    down vote










                    up vote
                    2
                    down vote









                    It's wrong when you jump from $2S^{-1}v=TS^{-1}v$ to $2S^{-1}Sv=Tv$.






                    share|cite|improve this answer












                    It's wrong when you jump from $2S^{-1}v=TS^{-1}v$ to $2S^{-1}Sv=Tv$.







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered 6 hours ago









                    José Carlos Santos

                    147k22117218




                    147k22117218






















                        up vote
                        2
                        down vote













                        Since the error in our OP Justin Sanders' argument has been thoroughly vetted in other answers, here I will directly address the title question.



                        Similar matrices do have the same eigenvalues, to wit:



                        if



                        $B = PAP^{-1}, tag 1$



                        then



                        $B - lambda I = PAP^{-1} - lambda I = PAP^{-1} - lambda PIP^{-1} = P(A - lambda I)P^{-1}, tag 2$



                        whence



                        $det(B - lambda I) = det(P(A - lambda I)P^{-1}) = det(P) det(A - lambda I) det (P^{-1}) = det(A - lambda I), tag 3$



                        since $det(P^{-1}) = (det(P))^{-1}; tag 4$



                        thus $A$ and $B$, having the same characteristic polynomials, also share the roots of these polynomials, i.e., their eigenvalues.



                        However, similar matrices do not in general share eigenvectors; if



                        $B vec v = lambda vec v, tag 5$



                        then



                        $PAP^{-1} vec v = lambda vec v, tag 6$



                        or



                        $AP^{-1} vec v = lambda P^{-1} vec v, tag 7$



                        that is, $P^{-1} vec v$ is an eigenvector of $A$ corresponding to $lambda$; indeed, if $lambda$ is a root of (3) of multiplicity one, then $P^{-1} vec v$ is, up to a scale factor, the eigenvector of $A$ associated with $lambda$. Since we can in general choose $P$ so that $P^{-1} vec v ne vec v$, the eigenvectors will not be shared 'twixt' $A$ and $B$.



                        So while the eigenvalues are similarity invariant, the eigenvectors transform according to $vec v mapsto P^{-1} vec v$.






                        share|cite|improve this answer

























                          up vote
                          2
                          down vote













                          Since the error in our OP Justin Sanders' argument has been thoroughly vetted in other answers, here I will directly address the title question.



                          Similar matrices do have the same eigenvalues, to wit:



                          if



                          $B = PAP^{-1}, tag 1$



                          then



                          $B - lambda I = PAP^{-1} - lambda I = PAP^{-1} - lambda PIP^{-1} = P(A - lambda I)P^{-1}, tag 2$



                          whence



                          $det(B - lambda I) = det(P(A - lambda I)P^{-1}) = det(P) det(A - lambda I) det (P^{-1}) = det(A - lambda I), tag 3$



                          since $det(P^{-1}) = (det(P))^{-1}; tag 4$



                          thus $A$ and $B$, having the same characteristic polynomials, also share the roots of these polynomials, i.e., their eigenvalues.



                          However, similar matrices do not in general share eigenvectors; if



                          $B vec v = lambda vec v, tag 5$



                          then



                          $PAP^{-1} vec v = lambda vec v, tag 6$



                          or



                          $AP^{-1} vec v = lambda P^{-1} vec v, tag 7$



                          that is, $P^{-1} vec v$ is an eigenvector of $A$ corresponding to $lambda$; indeed, if $lambda$ is a root of (3) of multiplicity one, then $P^{-1} vec v$ is, up to a scale factor, the eigenvector of $A$ associated with $lambda$. Since we can in general choose $P$ so that $P^{-1} vec v ne vec v$, the eigenvectors will not be shared 'twixt' $A$ and $B$.



                          So while the eigenvalues are similarity invariant, the eigenvectors transform according to $vec v mapsto P^{-1} vec v$.






                          share|cite|improve this answer























                            up vote
                            2
                            down vote










                            up vote
                            2
                            down vote









                            Since the error in our OP Justin Sanders' argument has been thoroughly vetted in other answers, here I will directly address the title question.



                            Similar matrices do have the same eigenvalues, to wit:



                            if



                            $B = PAP^{-1}, tag 1$



                            then



                            $B - lambda I = PAP^{-1} - lambda I = PAP^{-1} - lambda PIP^{-1} = P(A - lambda I)P^{-1}, tag 2$



                            whence



                            $det(B - lambda I) = det(P(A - lambda I)P^{-1}) = det(P) det(A - lambda I) det (P^{-1}) = det(A - lambda I), tag 3$



                            since $det(P^{-1}) = (det(P))^{-1}; tag 4$



                            thus $A$ and $B$, having the same characteristic polynomials, also share the roots of these polynomials, i.e., their eigenvalues.



                            However, similar matrices do not in general share eigenvectors; if



                            $B vec v = lambda vec v, tag 5$



                            then



                            $PAP^{-1} vec v = lambda vec v, tag 6$



                            or



                            $AP^{-1} vec v = lambda P^{-1} vec v, tag 7$



                            that is, $P^{-1} vec v$ is an eigenvector of $A$ corresponding to $lambda$; indeed, if $lambda$ is a root of (3) of multiplicity one, then $P^{-1} vec v$ is, up to a scale factor, the eigenvector of $A$ associated with $lambda$. Since we can in general choose $P$ so that $P^{-1} vec v ne vec v$, the eigenvectors will not be shared 'twixt' $A$ and $B$.



                            So while the eigenvalues are similarity invariant, the eigenvectors transform according to $vec v mapsto P^{-1} vec v$.






                            share|cite|improve this answer












                            Since the error in our OP Justin Sanders' argument has been thoroughly vetted in other answers, here I will directly address the title question.



                            Similar matrices do have the same eigenvalues, to wit:



                            if



                            $B = PAP^{-1}, tag 1$



                            then



                            $B - lambda I = PAP^{-1} - lambda I = PAP^{-1} - lambda PIP^{-1} = P(A - lambda I)P^{-1}, tag 2$



                            whence



                            $det(B - lambda I) = det(P(A - lambda I)P^{-1}) = det(P) det(A - lambda I) det (P^{-1}) = det(A - lambda I), tag 3$



                            since $det(P^{-1}) = (det(P))^{-1}; tag 4$



                            thus $A$ and $B$, having the same characteristic polynomials, also share the roots of these polynomials, i.e., their eigenvalues.



                            However, similar matrices do not in general share eigenvectors; if



                            $B vec v = lambda vec v, tag 5$



                            then



                            $PAP^{-1} vec v = lambda vec v, tag 6$



                            or



                            $AP^{-1} vec v = lambda P^{-1} vec v, tag 7$



                            that is, $P^{-1} vec v$ is an eigenvector of $A$ corresponding to $lambda$; indeed, if $lambda$ is a root of (3) of multiplicity one, then $P^{-1} vec v$ is, up to a scale factor, the eigenvector of $A$ associated with $lambda$. Since we can in general choose $P$ so that $P^{-1} vec v ne vec v$, the eigenvectors will not be shared 'twixt' $A$ and $B$.



                            So while the eigenvalues are similarity invariant, the eigenvectors transform according to $vec v mapsto P^{-1} vec v$.







                            share|cite|improve this answer












                            share|cite|improve this answer



                            share|cite|improve this answer










                            answered 5 hours ago









                            Robert Lewis

                            42.9k22863




                            42.9k22863






















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