Linear independent eigenvectors and eigenvalues











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I have T as a linear transformation from V to V over the field F, V has dimension n. T has the maximum number n distinct eigenvalues, then show that there exists a basis of V consisting of eigenvectors.




I know that if I let $v_1,...,v_r$ be eigenvectors belonging to distinct eigenvalues, then those vectors are linearly independent. Can I make a basis from these linearly independent vector and prove that it spans V?



Also, what will the matrix of T be in respect to this basis?



Thank you for any input!










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    If there are $n$ linearly independent vectors in $n$-dimensional space, then they must form a basis. To see what $T$ looks like, consider what $T x_k$ looks like in the basis of eigenvectors.
    – copper.hat
    Nov 20 '13 at 3:19












  • @Akaichan Do you still need help with this or is Copper.Hat's comment enough.
    – Git Gud
    Nov 25 '13 at 21:41















up vote
0
down vote

favorite













I have T as a linear transformation from V to V over the field F, V has dimension n. T has the maximum number n distinct eigenvalues, then show that there exists a basis of V consisting of eigenvectors.




I know that if I let $v_1,...,v_r$ be eigenvectors belonging to distinct eigenvalues, then those vectors are linearly independent. Can I make a basis from these linearly independent vector and prove that it spans V?



Also, what will the matrix of T be in respect to this basis?



Thank you for any input!










share|cite|improve this question




















  • 1




    If there are $n$ linearly independent vectors in $n$-dimensional space, then they must form a basis. To see what $T$ looks like, consider what $T x_k$ looks like in the basis of eigenvectors.
    – copper.hat
    Nov 20 '13 at 3:19












  • @Akaichan Do you still need help with this or is Copper.Hat's comment enough.
    – Git Gud
    Nov 25 '13 at 21:41













up vote
0
down vote

favorite









up vote
0
down vote

favorite












I have T as a linear transformation from V to V over the field F, V has dimension n. T has the maximum number n distinct eigenvalues, then show that there exists a basis of V consisting of eigenvectors.




I know that if I let $v_1,...,v_r$ be eigenvectors belonging to distinct eigenvalues, then those vectors are linearly independent. Can I make a basis from these linearly independent vector and prove that it spans V?



Also, what will the matrix of T be in respect to this basis?



Thank you for any input!










share|cite|improve this question
















I have T as a linear transformation from V to V over the field F, V has dimension n. T has the maximum number n distinct eigenvalues, then show that there exists a basis of V consisting of eigenvectors.




I know that if I let $v_1,...,v_r$ be eigenvectors belonging to distinct eigenvalues, then those vectors are linearly independent. Can I make a basis from these linearly independent vector and prove that it spans V?



Also, what will the matrix of T be in respect to this basis?



Thank you for any input!







linear-algebra matrices transformation eigenvalues-eigenvectors






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edited Nov 20 '13 at 4:44









Mhenni Benghorbal

43k63574




43k63574










asked Nov 20 '13 at 3:10









Akaichan

1,47621836




1,47621836








  • 1




    If there are $n$ linearly independent vectors in $n$-dimensional space, then they must form a basis. To see what $T$ looks like, consider what $T x_k$ looks like in the basis of eigenvectors.
    – copper.hat
    Nov 20 '13 at 3:19












  • @Akaichan Do you still need help with this or is Copper.Hat's comment enough.
    – Git Gud
    Nov 25 '13 at 21:41














  • 1




    If there are $n$ linearly independent vectors in $n$-dimensional space, then they must form a basis. To see what $T$ looks like, consider what $T x_k$ looks like in the basis of eigenvectors.
    – copper.hat
    Nov 20 '13 at 3:19












  • @Akaichan Do you still need help with this or is Copper.Hat's comment enough.
    – Git Gud
    Nov 25 '13 at 21:41








1




1




If there are $n$ linearly independent vectors in $n$-dimensional space, then they must form a basis. To see what $T$ looks like, consider what $T x_k$ looks like in the basis of eigenvectors.
– copper.hat
Nov 20 '13 at 3:19






If there are $n$ linearly independent vectors in $n$-dimensional space, then they must form a basis. To see what $T$ looks like, consider what $T x_k$ looks like in the basis of eigenvectors.
– copper.hat
Nov 20 '13 at 3:19














@Akaichan Do you still need help with this or is Copper.Hat's comment enough.
– Git Gud
Nov 25 '13 at 21:41




@Akaichan Do you still need help with this or is Copper.Hat's comment enough.
– Git Gud
Nov 25 '13 at 21:41










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By definition, a basis for $V$ is a linearly-independent set of vectors in $V$ that spans the space $V,$ and the dimension of a finite-dimensional vector space is the number of elements in a basis for $V,$ so we know that any basis for $V$ must contain exactly $n$ linearly-independent vectors. We also know (see Theorem 5 on page 45 of Hoffman and Kunze's Linear Algebra) that every linearly-independent subset of $V$ is part of a basis for $V.$ You already know that the $n$ eigenvectors are linearly independent, so it follows that they form a basis for $V.$



To find the matrix of $T$ with respect your ordered basis $mathscr B$ of eigenvectors, we use the fact that the ith column of that matrix is given by $[Tv_i]_{mathscr B}$ where $[ cdot ]_{mathscr B}$ denotes the coordinate matrix with respect to $mathscr B.$ We therefore compute
$$[Tv_i]_{mathscr B} = [lambda_i v_i]_{mathscr B} = begin{bmatrix} 0\ vdots\ 0\ lambda_i\ 0\ vdots\ 0end{bmatrix}$$
where $lambda_i$ is the eigenvalue associated with eigenvector $v_i.$ Thus, the matrix of $T$ is a diagonal matrix with the eigenvalues in the diagonal entries ordered by corresponding eigenvectors:
$$begin{bmatrix}
lambda_1 & 0 & cdots & 0\
0 & lambda_2 & cdots & 0\
vdots & vdots & ddots & vdots\
0 & 0 & cdots & lambda_nend{bmatrix}.$$






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    By definition, a basis for $V$ is a linearly-independent set of vectors in $V$ that spans the space $V,$ and the dimension of a finite-dimensional vector space is the number of elements in a basis for $V,$ so we know that any basis for $V$ must contain exactly $n$ linearly-independent vectors. We also know (see Theorem 5 on page 45 of Hoffman and Kunze's Linear Algebra) that every linearly-independent subset of $V$ is part of a basis for $V.$ You already know that the $n$ eigenvectors are linearly independent, so it follows that they form a basis for $V.$



    To find the matrix of $T$ with respect your ordered basis $mathscr B$ of eigenvectors, we use the fact that the ith column of that matrix is given by $[Tv_i]_{mathscr B}$ where $[ cdot ]_{mathscr B}$ denotes the coordinate matrix with respect to $mathscr B.$ We therefore compute
    $$[Tv_i]_{mathscr B} = [lambda_i v_i]_{mathscr B} = begin{bmatrix} 0\ vdots\ 0\ lambda_i\ 0\ vdots\ 0end{bmatrix}$$
    where $lambda_i$ is the eigenvalue associated with eigenvector $v_i.$ Thus, the matrix of $T$ is a diagonal matrix with the eigenvalues in the diagonal entries ordered by corresponding eigenvectors:
    $$begin{bmatrix}
    lambda_1 & 0 & cdots & 0\
    0 & lambda_2 & cdots & 0\
    vdots & vdots & ddots & vdots\
    0 & 0 & cdots & lambda_nend{bmatrix}.$$






    share|cite|improve this answer



























      up vote
      0
      down vote













      By definition, a basis for $V$ is a linearly-independent set of vectors in $V$ that spans the space $V,$ and the dimension of a finite-dimensional vector space is the number of elements in a basis for $V,$ so we know that any basis for $V$ must contain exactly $n$ linearly-independent vectors. We also know (see Theorem 5 on page 45 of Hoffman and Kunze's Linear Algebra) that every linearly-independent subset of $V$ is part of a basis for $V.$ You already know that the $n$ eigenvectors are linearly independent, so it follows that they form a basis for $V.$



      To find the matrix of $T$ with respect your ordered basis $mathscr B$ of eigenvectors, we use the fact that the ith column of that matrix is given by $[Tv_i]_{mathscr B}$ where $[ cdot ]_{mathscr B}$ denotes the coordinate matrix with respect to $mathscr B.$ We therefore compute
      $$[Tv_i]_{mathscr B} = [lambda_i v_i]_{mathscr B} = begin{bmatrix} 0\ vdots\ 0\ lambda_i\ 0\ vdots\ 0end{bmatrix}$$
      where $lambda_i$ is the eigenvalue associated with eigenvector $v_i.$ Thus, the matrix of $T$ is a diagonal matrix with the eigenvalues in the diagonal entries ordered by corresponding eigenvectors:
      $$begin{bmatrix}
      lambda_1 & 0 & cdots & 0\
      0 & lambda_2 & cdots & 0\
      vdots & vdots & ddots & vdots\
      0 & 0 & cdots & lambda_nend{bmatrix}.$$






      share|cite|improve this answer

























        up vote
        0
        down vote










        up vote
        0
        down vote









        By definition, a basis for $V$ is a linearly-independent set of vectors in $V$ that spans the space $V,$ and the dimension of a finite-dimensional vector space is the number of elements in a basis for $V,$ so we know that any basis for $V$ must contain exactly $n$ linearly-independent vectors. We also know (see Theorem 5 on page 45 of Hoffman and Kunze's Linear Algebra) that every linearly-independent subset of $V$ is part of a basis for $V.$ You already know that the $n$ eigenvectors are linearly independent, so it follows that they form a basis for $V.$



        To find the matrix of $T$ with respect your ordered basis $mathscr B$ of eigenvectors, we use the fact that the ith column of that matrix is given by $[Tv_i]_{mathscr B}$ where $[ cdot ]_{mathscr B}$ denotes the coordinate matrix with respect to $mathscr B.$ We therefore compute
        $$[Tv_i]_{mathscr B} = [lambda_i v_i]_{mathscr B} = begin{bmatrix} 0\ vdots\ 0\ lambda_i\ 0\ vdots\ 0end{bmatrix}$$
        where $lambda_i$ is the eigenvalue associated with eigenvector $v_i.$ Thus, the matrix of $T$ is a diagonal matrix with the eigenvalues in the diagonal entries ordered by corresponding eigenvectors:
        $$begin{bmatrix}
        lambda_1 & 0 & cdots & 0\
        0 & lambda_2 & cdots & 0\
        vdots & vdots & ddots & vdots\
        0 & 0 & cdots & lambda_nend{bmatrix}.$$






        share|cite|improve this answer














        By definition, a basis for $V$ is a linearly-independent set of vectors in $V$ that spans the space $V,$ and the dimension of a finite-dimensional vector space is the number of elements in a basis for $V,$ so we know that any basis for $V$ must contain exactly $n$ linearly-independent vectors. We also know (see Theorem 5 on page 45 of Hoffman and Kunze's Linear Algebra) that every linearly-independent subset of $V$ is part of a basis for $V.$ You already know that the $n$ eigenvectors are linearly independent, so it follows that they form a basis for $V.$



        To find the matrix of $T$ with respect your ordered basis $mathscr B$ of eigenvectors, we use the fact that the ith column of that matrix is given by $[Tv_i]_{mathscr B}$ where $[ cdot ]_{mathscr B}$ denotes the coordinate matrix with respect to $mathscr B.$ We therefore compute
        $$[Tv_i]_{mathscr B} = [lambda_i v_i]_{mathscr B} = begin{bmatrix} 0\ vdots\ 0\ lambda_i\ 0\ vdots\ 0end{bmatrix}$$
        where $lambda_i$ is the eigenvalue associated with eigenvector $v_i.$ Thus, the matrix of $T$ is a diagonal matrix with the eigenvalues in the diagonal entries ordered by corresponding eigenvectors:
        $$begin{bmatrix}
        lambda_1 & 0 & cdots & 0\
        0 & lambda_2 & cdots & 0\
        vdots & vdots & ddots & vdots\
        0 & 0 & cdots & lambda_nend{bmatrix}.$$







        share|cite|improve this answer














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        edited Nov 21 at 17:19

























        answered Nov 20 at 17:34









        Maurice P

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        1,3451732






























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