Matrix of reflection in $R^3$











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Please, can you explain me how do we get this formula
$$ A = I - 2nn^{T} $$ in $$ R^{3} $$? This should be matrix of reflection, but I don't know how to prove that.










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    This form is called "Householder transform" in the domain of Numerical Analysis.
    – Jean Marie
    Nov 26 at 10:32















up vote
1
down vote

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Please, can you explain me how do we get this formula
$$ A = I - 2nn^{T} $$ in $$ R^{3} $$? This should be matrix of reflection, but I don't know how to prove that.










share|cite|improve this question




















  • 1




    Welcome to MSE. Your question is phrased as an isolated problem, without any further information or context. This does not match many users' quality standards, so it may attract downvotes, or be put on hold. To prevent that, please edit the question. This will help you recognise and resolve the issues. Concretely: please provide context, and include your work and thoughts on the problem. These changes can help in formulating more appropriate answers.
    – José Carlos Santos
    Nov 26 at 9:43






  • 1




    This form is called "Householder transform" in the domain of Numerical Analysis.
    – Jean Marie
    Nov 26 at 10:32













up vote
1
down vote

favorite









up vote
1
down vote

favorite











Please, can you explain me how do we get this formula
$$ A = I - 2nn^{T} $$ in $$ R^{3} $$? This should be matrix of reflection, but I don't know how to prove that.










share|cite|improve this question















Please, can you explain me how do we get this formula
$$ A = I - 2nn^{T} $$ in $$ R^{3} $$? This should be matrix of reflection, but I don't know how to prove that.







linear-algebra geometry matrix-equations reflection






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edited Nov 26 at 10:20









Darío A. Gutiérrez

2,57341530




2,57341530










asked Nov 26 at 9:37









Lazar

63




63








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    Welcome to MSE. Your question is phrased as an isolated problem, without any further information or context. This does not match many users' quality standards, so it may attract downvotes, or be put on hold. To prevent that, please edit the question. This will help you recognise and resolve the issues. Concretely: please provide context, and include your work and thoughts on the problem. These changes can help in formulating more appropriate answers.
    – José Carlos Santos
    Nov 26 at 9:43






  • 1




    This form is called "Householder transform" in the domain of Numerical Analysis.
    – Jean Marie
    Nov 26 at 10:32














  • 1




    Welcome to MSE. Your question is phrased as an isolated problem, without any further information or context. This does not match many users' quality standards, so it may attract downvotes, or be put on hold. To prevent that, please edit the question. This will help you recognise and resolve the issues. Concretely: please provide context, and include your work and thoughts on the problem. These changes can help in formulating more appropriate answers.
    – José Carlos Santos
    Nov 26 at 9:43






  • 1




    This form is called "Householder transform" in the domain of Numerical Analysis.
    – Jean Marie
    Nov 26 at 10:32








1




1




Welcome to MSE. Your question is phrased as an isolated problem, without any further information or context. This does not match many users' quality standards, so it may attract downvotes, or be put on hold. To prevent that, please edit the question. This will help you recognise and resolve the issues. Concretely: please provide context, and include your work and thoughts on the problem. These changes can help in formulating more appropriate answers.
– José Carlos Santos
Nov 26 at 9:43




Welcome to MSE. Your question is phrased as an isolated problem, without any further information or context. This does not match many users' quality standards, so it may attract downvotes, or be put on hold. To prevent that, please edit the question. This will help you recognise and resolve the issues. Concretely: please provide context, and include your work and thoughts on the problem. These changes can help in formulating more appropriate answers.
– José Carlos Santos
Nov 26 at 9:43




1




1




This form is called "Householder transform" in the domain of Numerical Analysis.
– Jean Marie
Nov 26 at 10:32




This form is called "Householder transform" in the domain of Numerical Analysis.
– Jean Marie
Nov 26 at 10:32










2 Answers
2






active

oldest

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up vote
1
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Let $a,b,n$ be unit vectors orthogonal to each other - $a,b$ basis for the plane, $n$ orthogonal to the plane.



You can easily check that any vector $v$ is represented in the basis {$a,b,n$} as



$v=a(a^Tv)+b(b^Tv)+n(n^Tv)=(aa^T+bb^T+nn^T)v$



($a^Tv, b^Tv, n^Tv$ are here scalars)



hence $aa^T+bb^T+nn^T=I$.



During the reflection components in the plane are unchanged, component orthogonal to the plane is negated.



Hence we have $w= (aa^T+bb^T-nn^T)v=(I-2nn^T)v$.






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  • Very nice! Thank you.
    – Giuseppe Negro
    Dec 3 at 11:06


















up vote
0
down vote













This is maybe more of a trick than of a proof, but I find this approach much easier to memorize.



Let $x_1, x_2, x_3$ be the Cartesian coordinates of the space. It is clear that the reflection around the plane $x_1x_2$ is the map
$$
(x_1, x_2, x_3)mapsto (x_1, x_2, -x_3), $$

and this is a linear map that can be written in matrix form as
$$
A_{(0,0,1)}begin{bmatrix} x_1 \ x_2 \ x_3end{bmatrix} = left( I-begin{bmatrix} 0 & 0 &0\ 0 & 0 & 0 \ 0 & 0 & 2end{bmatrix}right)begin{bmatrix}x_1\ x_2 \ x_3end{bmatrix},$$

that is,
$$
A_{(0,0,1)}=I-2begin{bmatrix} 0 \ 0 \ 1end{bmatrix}begin{bmatrix} 0 &0 &1end{bmatrix}.$$



We write
$$tag{1}
A_{(0,0,1)}vec x = vec y.$$



Now, to obtain the formula for the reflection around the plane having $vec{n}$ as normal vector, we change variables;
$$
vec x = Rvec x ', quad vec y = Rvec y', $$

where $R$ is a rotation matrix such that
$$
Rvec n = begin{bmatrix} 0 \0 \ 1end{bmatrix}.$$

Applying this change of variable in (1) yields
$$tag{2}
A_{vec n}vec x ' =vec y ',$$

where $A_{vec n}$ is the reflection matrix we want to compute. And now it is a matter of unwrapping $vec x', vec y'$ in (2) and computing, using the fact that $R^TR=I$, because $R$ is a rotation.






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    2 Answers
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    2 Answers
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    up vote
    1
    down vote













    Let $a,b,n$ be unit vectors orthogonal to each other - $a,b$ basis for the plane, $n$ orthogonal to the plane.



    You can easily check that any vector $v$ is represented in the basis {$a,b,n$} as



    $v=a(a^Tv)+b(b^Tv)+n(n^Tv)=(aa^T+bb^T+nn^T)v$



    ($a^Tv, b^Tv, n^Tv$ are here scalars)



    hence $aa^T+bb^T+nn^T=I$.



    During the reflection components in the plane are unchanged, component orthogonal to the plane is negated.



    Hence we have $w= (aa^T+bb^T-nn^T)v=(I-2nn^T)v$.






    share|cite|improve this answer























    • Very nice! Thank you.
      – Giuseppe Negro
      Dec 3 at 11:06















    up vote
    1
    down vote













    Let $a,b,n$ be unit vectors orthogonal to each other - $a,b$ basis for the plane, $n$ orthogonal to the plane.



    You can easily check that any vector $v$ is represented in the basis {$a,b,n$} as



    $v=a(a^Tv)+b(b^Tv)+n(n^Tv)=(aa^T+bb^T+nn^T)v$



    ($a^Tv, b^Tv, n^Tv$ are here scalars)



    hence $aa^T+bb^T+nn^T=I$.



    During the reflection components in the plane are unchanged, component orthogonal to the plane is negated.



    Hence we have $w= (aa^T+bb^T-nn^T)v=(I-2nn^T)v$.






    share|cite|improve this answer























    • Very nice! Thank you.
      – Giuseppe Negro
      Dec 3 at 11:06













    up vote
    1
    down vote










    up vote
    1
    down vote









    Let $a,b,n$ be unit vectors orthogonal to each other - $a,b$ basis for the plane, $n$ orthogonal to the plane.



    You can easily check that any vector $v$ is represented in the basis {$a,b,n$} as



    $v=a(a^Tv)+b(b^Tv)+n(n^Tv)=(aa^T+bb^T+nn^T)v$



    ($a^Tv, b^Tv, n^Tv$ are here scalars)



    hence $aa^T+bb^T+nn^T=I$.



    During the reflection components in the plane are unchanged, component orthogonal to the plane is negated.



    Hence we have $w= (aa^T+bb^T-nn^T)v=(I-2nn^T)v$.






    share|cite|improve this answer














    Let $a,b,n$ be unit vectors orthogonal to each other - $a,b$ basis for the plane, $n$ orthogonal to the plane.



    You can easily check that any vector $v$ is represented in the basis {$a,b,n$} as



    $v=a(a^Tv)+b(b^Tv)+n(n^Tv)=(aa^T+bb^T+nn^T)v$



    ($a^Tv, b^Tv, n^Tv$ are here scalars)



    hence $aa^T+bb^T+nn^T=I$.



    During the reflection components in the plane are unchanged, component orthogonal to the plane is negated.



    Hence we have $w= (aa^T+bb^T-nn^T)v=(I-2nn^T)v$.







    share|cite|improve this answer














    share|cite|improve this answer



    share|cite|improve this answer








    edited Nov 26 at 15:55

























    answered Nov 26 at 14:42









    Widawensen

    4,38921445




    4,38921445












    • Very nice! Thank you.
      – Giuseppe Negro
      Dec 3 at 11:06


















    • Very nice! Thank you.
      – Giuseppe Negro
      Dec 3 at 11:06
















    Very nice! Thank you.
    – Giuseppe Negro
    Dec 3 at 11:06




    Very nice! Thank you.
    – Giuseppe Negro
    Dec 3 at 11:06










    up vote
    0
    down vote













    This is maybe more of a trick than of a proof, but I find this approach much easier to memorize.



    Let $x_1, x_2, x_3$ be the Cartesian coordinates of the space. It is clear that the reflection around the plane $x_1x_2$ is the map
    $$
    (x_1, x_2, x_3)mapsto (x_1, x_2, -x_3), $$

    and this is a linear map that can be written in matrix form as
    $$
    A_{(0,0,1)}begin{bmatrix} x_1 \ x_2 \ x_3end{bmatrix} = left( I-begin{bmatrix} 0 & 0 &0\ 0 & 0 & 0 \ 0 & 0 & 2end{bmatrix}right)begin{bmatrix}x_1\ x_2 \ x_3end{bmatrix},$$

    that is,
    $$
    A_{(0,0,1)}=I-2begin{bmatrix} 0 \ 0 \ 1end{bmatrix}begin{bmatrix} 0 &0 &1end{bmatrix}.$$



    We write
    $$tag{1}
    A_{(0,0,1)}vec x = vec y.$$



    Now, to obtain the formula for the reflection around the plane having $vec{n}$ as normal vector, we change variables;
    $$
    vec x = Rvec x ', quad vec y = Rvec y', $$

    where $R$ is a rotation matrix such that
    $$
    Rvec n = begin{bmatrix} 0 \0 \ 1end{bmatrix}.$$

    Applying this change of variable in (1) yields
    $$tag{2}
    A_{vec n}vec x ' =vec y ',$$

    where $A_{vec n}$ is the reflection matrix we want to compute. And now it is a matter of unwrapping $vec x', vec y'$ in (2) and computing, using the fact that $R^TR=I$, because $R$ is a rotation.






    share|cite|improve this answer



























      up vote
      0
      down vote













      This is maybe more of a trick than of a proof, but I find this approach much easier to memorize.



      Let $x_1, x_2, x_3$ be the Cartesian coordinates of the space. It is clear that the reflection around the plane $x_1x_2$ is the map
      $$
      (x_1, x_2, x_3)mapsto (x_1, x_2, -x_3), $$

      and this is a linear map that can be written in matrix form as
      $$
      A_{(0,0,1)}begin{bmatrix} x_1 \ x_2 \ x_3end{bmatrix} = left( I-begin{bmatrix} 0 & 0 &0\ 0 & 0 & 0 \ 0 & 0 & 2end{bmatrix}right)begin{bmatrix}x_1\ x_2 \ x_3end{bmatrix},$$

      that is,
      $$
      A_{(0,0,1)}=I-2begin{bmatrix} 0 \ 0 \ 1end{bmatrix}begin{bmatrix} 0 &0 &1end{bmatrix}.$$



      We write
      $$tag{1}
      A_{(0,0,1)}vec x = vec y.$$



      Now, to obtain the formula for the reflection around the plane having $vec{n}$ as normal vector, we change variables;
      $$
      vec x = Rvec x ', quad vec y = Rvec y', $$

      where $R$ is a rotation matrix such that
      $$
      Rvec n = begin{bmatrix} 0 \0 \ 1end{bmatrix}.$$

      Applying this change of variable in (1) yields
      $$tag{2}
      A_{vec n}vec x ' =vec y ',$$

      where $A_{vec n}$ is the reflection matrix we want to compute. And now it is a matter of unwrapping $vec x', vec y'$ in (2) and computing, using the fact that $R^TR=I$, because $R$ is a rotation.






      share|cite|improve this answer

























        up vote
        0
        down vote










        up vote
        0
        down vote









        This is maybe more of a trick than of a proof, but I find this approach much easier to memorize.



        Let $x_1, x_2, x_3$ be the Cartesian coordinates of the space. It is clear that the reflection around the plane $x_1x_2$ is the map
        $$
        (x_1, x_2, x_3)mapsto (x_1, x_2, -x_3), $$

        and this is a linear map that can be written in matrix form as
        $$
        A_{(0,0,1)}begin{bmatrix} x_1 \ x_2 \ x_3end{bmatrix} = left( I-begin{bmatrix} 0 & 0 &0\ 0 & 0 & 0 \ 0 & 0 & 2end{bmatrix}right)begin{bmatrix}x_1\ x_2 \ x_3end{bmatrix},$$

        that is,
        $$
        A_{(0,0,1)}=I-2begin{bmatrix} 0 \ 0 \ 1end{bmatrix}begin{bmatrix} 0 &0 &1end{bmatrix}.$$



        We write
        $$tag{1}
        A_{(0,0,1)}vec x = vec y.$$



        Now, to obtain the formula for the reflection around the plane having $vec{n}$ as normal vector, we change variables;
        $$
        vec x = Rvec x ', quad vec y = Rvec y', $$

        where $R$ is a rotation matrix such that
        $$
        Rvec n = begin{bmatrix} 0 \0 \ 1end{bmatrix}.$$

        Applying this change of variable in (1) yields
        $$tag{2}
        A_{vec n}vec x ' =vec y ',$$

        where $A_{vec n}$ is the reflection matrix we want to compute. And now it is a matter of unwrapping $vec x', vec y'$ in (2) and computing, using the fact that $R^TR=I$, because $R$ is a rotation.






        share|cite|improve this answer














        This is maybe more of a trick than of a proof, but I find this approach much easier to memorize.



        Let $x_1, x_2, x_3$ be the Cartesian coordinates of the space. It is clear that the reflection around the plane $x_1x_2$ is the map
        $$
        (x_1, x_2, x_3)mapsto (x_1, x_2, -x_3), $$

        and this is a linear map that can be written in matrix form as
        $$
        A_{(0,0,1)}begin{bmatrix} x_1 \ x_2 \ x_3end{bmatrix} = left( I-begin{bmatrix} 0 & 0 &0\ 0 & 0 & 0 \ 0 & 0 & 2end{bmatrix}right)begin{bmatrix}x_1\ x_2 \ x_3end{bmatrix},$$

        that is,
        $$
        A_{(0,0,1)}=I-2begin{bmatrix} 0 \ 0 \ 1end{bmatrix}begin{bmatrix} 0 &0 &1end{bmatrix}.$$



        We write
        $$tag{1}
        A_{(0,0,1)}vec x = vec y.$$



        Now, to obtain the formula for the reflection around the plane having $vec{n}$ as normal vector, we change variables;
        $$
        vec x = Rvec x ', quad vec y = Rvec y', $$

        where $R$ is a rotation matrix such that
        $$
        Rvec n = begin{bmatrix} 0 \0 \ 1end{bmatrix}.$$

        Applying this change of variable in (1) yields
        $$tag{2}
        A_{vec n}vec x ' =vec y ',$$

        where $A_{vec n}$ is the reflection matrix we want to compute. And now it is a matter of unwrapping $vec x', vec y'$ in (2) and computing, using the fact that $R^TR=I$, because $R$ is a rotation.







        share|cite|improve this answer














        share|cite|improve this answer



        share|cite|improve this answer








        answered Nov 26 at 10:15


























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        Giuseppe Negro































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