Geometric understanding of subtracting lambda from diagonals












6














Given the definition of eigenvalues/eigenvectors:



$Av = lambda v $



you could rearrange the terms to be:



$(A - lambda I)v = 0$



Geometrically, the first equation says that multiplying by $A$ is the same as scaling the vector $v$ by $lambda$. However, in the second equation, how do you visualize the effect of subtracting the matrix $lambda I$ from matrix $A$ and how does that induce a linearly dependent set of basis vectors?



TL;DR: I understand that the new matrix $(A-lambda I)$ collapses the span of $v$ into a lower dimension but I don't understand how $A$ relates to $(A-lambda I)$ geometrically.










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  • 3




    Interesting question. One possible idea. If $A$ happened to be diagonal then you can visualize the parallelepiped formed by stretching the basis vectors using the diagonal elements as scale factors. Subtracting a constant from the diagonal changes that parallelepiped (not simply by scaling it). When you subtract an eigenvalue the pppd has zero volume.
    – Ethan Bolker
    Nov 24 at 16:53










  • First, how do you visualize $A$ geometrically?
    – Rahul
    Nov 24 at 20:13
















6














Given the definition of eigenvalues/eigenvectors:



$Av = lambda v $



you could rearrange the terms to be:



$(A - lambda I)v = 0$



Geometrically, the first equation says that multiplying by $A$ is the same as scaling the vector $v$ by $lambda$. However, in the second equation, how do you visualize the effect of subtracting the matrix $lambda I$ from matrix $A$ and how does that induce a linearly dependent set of basis vectors?



TL;DR: I understand that the new matrix $(A-lambda I)$ collapses the span of $v$ into a lower dimension but I don't understand how $A$ relates to $(A-lambda I)$ geometrically.










share|cite|improve this question


















  • 3




    Interesting question. One possible idea. If $A$ happened to be diagonal then you can visualize the parallelepiped formed by stretching the basis vectors using the diagonal elements as scale factors. Subtracting a constant from the diagonal changes that parallelepiped (not simply by scaling it). When you subtract an eigenvalue the pppd has zero volume.
    – Ethan Bolker
    Nov 24 at 16:53










  • First, how do you visualize $A$ geometrically?
    – Rahul
    Nov 24 at 20:13














6












6








6


1





Given the definition of eigenvalues/eigenvectors:



$Av = lambda v $



you could rearrange the terms to be:



$(A - lambda I)v = 0$



Geometrically, the first equation says that multiplying by $A$ is the same as scaling the vector $v$ by $lambda$. However, in the second equation, how do you visualize the effect of subtracting the matrix $lambda I$ from matrix $A$ and how does that induce a linearly dependent set of basis vectors?



TL;DR: I understand that the new matrix $(A-lambda I)$ collapses the span of $v$ into a lower dimension but I don't understand how $A$ relates to $(A-lambda I)$ geometrically.










share|cite|improve this question













Given the definition of eigenvalues/eigenvectors:



$Av = lambda v $



you could rearrange the terms to be:



$(A - lambda I)v = 0$



Geometrically, the first equation says that multiplying by $A$ is the same as scaling the vector $v$ by $lambda$. However, in the second equation, how do you visualize the effect of subtracting the matrix $lambda I$ from matrix $A$ and how does that induce a linearly dependent set of basis vectors?



TL;DR: I understand that the new matrix $(A-lambda I)$ collapses the span of $v$ into a lower dimension but I don't understand how $A$ relates to $(A-lambda I)$ geometrically.







linear-algebra geometry eigenvalues-eigenvectors visualization






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asked Nov 24 at 16:44









hlinee

635




635








  • 3




    Interesting question. One possible idea. If $A$ happened to be diagonal then you can visualize the parallelepiped formed by stretching the basis vectors using the diagonal elements as scale factors. Subtracting a constant from the diagonal changes that parallelepiped (not simply by scaling it). When you subtract an eigenvalue the pppd has zero volume.
    – Ethan Bolker
    Nov 24 at 16:53










  • First, how do you visualize $A$ geometrically?
    – Rahul
    Nov 24 at 20:13














  • 3




    Interesting question. One possible idea. If $A$ happened to be diagonal then you can visualize the parallelepiped formed by stretching the basis vectors using the diagonal elements as scale factors. Subtracting a constant from the diagonal changes that parallelepiped (not simply by scaling it). When you subtract an eigenvalue the pppd has zero volume.
    – Ethan Bolker
    Nov 24 at 16:53










  • First, how do you visualize $A$ geometrically?
    – Rahul
    Nov 24 at 20:13








3




3




Interesting question. One possible idea. If $A$ happened to be diagonal then you can visualize the parallelepiped formed by stretching the basis vectors using the diagonal elements as scale factors. Subtracting a constant from the diagonal changes that parallelepiped (not simply by scaling it). When you subtract an eigenvalue the pppd has zero volume.
– Ethan Bolker
Nov 24 at 16:53




Interesting question. One possible idea. If $A$ happened to be diagonal then you can visualize the parallelepiped formed by stretching the basis vectors using the diagonal elements as scale factors. Subtracting a constant from the diagonal changes that parallelepiped (not simply by scaling it). When you subtract an eigenvalue the pppd has zero volume.
– Ethan Bolker
Nov 24 at 16:53












First, how do you visualize $A$ geometrically?
– Rahul
Nov 24 at 20:13




First, how do you visualize $A$ geometrically?
– Rahul
Nov 24 at 20:13










1 Answer
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May I ask what do you understand by geometric relation or visualization? What you said




$(A-lambda I)$ collapses the span of $v$ into a lower dimension




is exactly how I visualize it. To be more precisely, it completely removes the image of ${rm span}(v)$. Thus only the co-space of ${rm span}(v)$ is possibly nontrivially acted upon.



The action on the remaining co-space is also modified, namely by subtracting $lambda$ times the input vector from the image, $Aw - lambda w$ (for $w perp v$). In the case that $v$ has geometric multiplicity one, then this just happens to not be collapsed to zero.






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    1 Answer
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    active

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    2














    May I ask what do you understand by geometric relation or visualization? What you said




    $(A-lambda I)$ collapses the span of $v$ into a lower dimension




    is exactly how I visualize it. To be more precisely, it completely removes the image of ${rm span}(v)$. Thus only the co-space of ${rm span}(v)$ is possibly nontrivially acted upon.



    The action on the remaining co-space is also modified, namely by subtracting $lambda$ times the input vector from the image, $Aw - lambda w$ (for $w perp v$). In the case that $v$ has geometric multiplicity one, then this just happens to not be collapsed to zero.






    share|cite|improve this answer


























      2














      May I ask what do you understand by geometric relation or visualization? What you said




      $(A-lambda I)$ collapses the span of $v$ into a lower dimension




      is exactly how I visualize it. To be more precisely, it completely removes the image of ${rm span}(v)$. Thus only the co-space of ${rm span}(v)$ is possibly nontrivially acted upon.



      The action on the remaining co-space is also modified, namely by subtracting $lambda$ times the input vector from the image, $Aw - lambda w$ (for $w perp v$). In the case that $v$ has geometric multiplicity one, then this just happens to not be collapsed to zero.






      share|cite|improve this answer
























        2












        2








        2






        May I ask what do you understand by geometric relation or visualization? What you said




        $(A-lambda I)$ collapses the span of $v$ into a lower dimension




        is exactly how I visualize it. To be more precisely, it completely removes the image of ${rm span}(v)$. Thus only the co-space of ${rm span}(v)$ is possibly nontrivially acted upon.



        The action on the remaining co-space is also modified, namely by subtracting $lambda$ times the input vector from the image, $Aw - lambda w$ (for $w perp v$). In the case that $v$ has geometric multiplicity one, then this just happens to not be collapsed to zero.






        share|cite|improve this answer












        May I ask what do you understand by geometric relation or visualization? What you said




        $(A-lambda I)$ collapses the span of $v$ into a lower dimension




        is exactly how I visualize it. To be more precisely, it completely removes the image of ${rm span}(v)$. Thus only the co-space of ${rm span}(v)$ is possibly nontrivially acted upon.



        The action on the remaining co-space is also modified, namely by subtracting $lambda$ times the input vector from the image, $Aw - lambda w$ (for $w perp v$). In the case that $v$ has geometric multiplicity one, then this just happens to not be collapsed to zero.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Nov 24 at 19:51









        rehctawrats

        21018




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