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

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2














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:



$Rv = STS^-1v$



$2v = STS^-1v$



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



$2S^-1Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



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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  • 1




    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
    Dec 17 at 23:46















2














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:



$Rv = STS^-1v$



$2v = STS^-1v$



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



$2S^-1Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



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

















  • 1




    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
    Dec 17 at 23:46













2












2








2


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:



$Rv = STS^-1v$



$2v = STS^-1v$



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



$2S^-1Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



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













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:



$Rv = STS^-1v$



$2v = STS^-1v$



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



$2S^-1Sv = Tv$



$2Iv = Tv$



$Tv = 2v$



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






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asked Dec 17 at 23:25









Justin Sanders

111




111







  • 1




    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
    Dec 17 at 23:46












  • 1




    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
    Dec 17 at 23:46







1




1




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
Dec 17 at 23:46




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
Dec 17 at 23:46










3 Answers
3






active

oldest

votes


















4














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

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

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

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




























    3














    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




























      2














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






      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









        4














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

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

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

        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

























          4














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

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

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

          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























            4












            4








            4






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

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

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

            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












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

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

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

            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












            share|cite|improve this answer



            share|cite|improve this answer










            answered Dec 17 at 23:28









            GenericMathMan

            511




            511





















                3














                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

























                  3














                  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























                    3












                    3








                    3






                    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 Dec 18 at 0:09









                    Robert Lewis

                    43.6k22863




                    43.6k22863





















                        2














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






                        share|cite|improve this answer

























                          2














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






                          share|cite|improve this answer























                            2












                            2








                            2






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






                            share|cite|improve this answer












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







                            share|cite|improve this answer












                            share|cite|improve this answer



                            share|cite|improve this answer










                            answered Dec 17 at 23:29









                            José Carlos Santos

                            149k22119221




                            149k22119221



























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