Why linear maps act like matrix multiplication?proof linear maps act like matrix multiplicationlinear...

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Why linear maps act like matrix multiplication?


proof linear maps act like matrix multiplicationlinear transformation of a basis where $T(v_1) = w_1, …, T(v_n) = w_n$Basis in a Linear MapTranspose of the matrix of a linear mapHow to define the matrix of a linear map?Prove that all entries in $M(T, (v_1, … ,v_n), (w_1, … , w_m))$ are zero except for the entries in row j, column jNeed help to understand the uniqueness of linear maps on basis of domain.Linear Maps, Basis of Domain, and MatrixIs the linear map on basis of $V$ a basis of $W$?Prove there exist a basis of $V$ and a basis of $W$ such that all entries of $mathcal{M}(T)$ are 0 except row j and column j













4












$begingroup$


In Linear Algebra Done Right, it said




Suppose $T in mathcal{L}(V,W)$ and $v in V$. Suppose $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$. Then $$M(Tv) = M(T)M(v)$$




$M(T)$ is the m-by-n matrix whose entries $A_{j,k}$ are defined by $Tv_k = A_{1,k}w_1 + ... + A_{m,k}w_m$ suppose $T in mathcal{L}(V,W)$ and $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$.



$M(v)$ is the matrix of vector $v$.



I generally follow the following proof:



Suppose $v = c_1v_1 + ... + c_nv_n$, where $c_1,...,c_n in mathbb{F}$. Thus
$$Tv = c_1Tv_1 +...+c_nTv_n$$



Hence



begin{equation}
begin{split}
M(Tv) &= c_1M(Tv_1) + ...+ c_nM(Tv_n)\
& = c_1M(T)_{.,1} +...+c_nM(T)_{.,n} \
& = M(T)M(v)
end{split}
end{equation}



But I have questions on the meaning of the proof. The book said it means each m-by-n matrix $A$ induces a linear map from $mathbb{F}^{n,1}$ to $mathbb{F}^{m,1}$. The result can be used to think of every linear map as a matrix multiplication map after suitable relabeling via the isomorphisms given by $M$.




  1. Is the shape of $M(Tv)$ m by 1, $M(T)$ m by n, and $M(v)$ n by 1?

  2. What is meant by suitable relabeling via the isomorphisms given by $M$? Does it just mean $M(T)$ is a isomorphism linear map between $M(v)$ and $M(Tv)$?










share|cite|improve this question











$endgroup$












  • $begingroup$
    In your blockquote, you haven't told us what $M$ means.
    $endgroup$
    – Gerry Myerson
    yesterday










  • $begingroup$
    @GerryMyerson revised
    $endgroup$
    – JOHN
    yesterday










  • $begingroup$
    Sorry, I don't know what it means for $M(v)$ to be the matrix of the vector $v$. What's the matrix of the vector $(1,2,3)$?
    $endgroup$
    – Gerry Myerson
    yesterday






  • 1




    $begingroup$
    @GerryMyerson Knowing Axler, it will be the coordinate vector for the given basis (in this case, $v_1, ldots, v_n$). For example, if the basis is $(0, 0, 1), (0, 1, 1), (1, 1, 1)$ in the space $Bbb{R}^3$, then $M(1, 2, 3) = begin{bmatrix} 1 \ 1 \ 1 end{bmatrix}$.
    $endgroup$
    – Theo Bendit
    yesterday


















4












$begingroup$


In Linear Algebra Done Right, it said




Suppose $T in mathcal{L}(V,W)$ and $v in V$. Suppose $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$. Then $$M(Tv) = M(T)M(v)$$




$M(T)$ is the m-by-n matrix whose entries $A_{j,k}$ are defined by $Tv_k = A_{1,k}w_1 + ... + A_{m,k}w_m$ suppose $T in mathcal{L}(V,W)$ and $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$.



$M(v)$ is the matrix of vector $v$.



I generally follow the following proof:



Suppose $v = c_1v_1 + ... + c_nv_n$, where $c_1,...,c_n in mathbb{F}$. Thus
$$Tv = c_1Tv_1 +...+c_nTv_n$$



Hence



begin{equation}
begin{split}
M(Tv) &= c_1M(Tv_1) + ...+ c_nM(Tv_n)\
& = c_1M(T)_{.,1} +...+c_nM(T)_{.,n} \
& = M(T)M(v)
end{split}
end{equation}



But I have questions on the meaning of the proof. The book said it means each m-by-n matrix $A$ induces a linear map from $mathbb{F}^{n,1}$ to $mathbb{F}^{m,1}$. The result can be used to think of every linear map as a matrix multiplication map after suitable relabeling via the isomorphisms given by $M$.




  1. Is the shape of $M(Tv)$ m by 1, $M(T)$ m by n, and $M(v)$ n by 1?

  2. What is meant by suitable relabeling via the isomorphisms given by $M$? Does it just mean $M(T)$ is a isomorphism linear map between $M(v)$ and $M(Tv)$?










share|cite|improve this question











$endgroup$












  • $begingroup$
    In your blockquote, you haven't told us what $M$ means.
    $endgroup$
    – Gerry Myerson
    yesterday










  • $begingroup$
    @GerryMyerson revised
    $endgroup$
    – JOHN
    yesterday










  • $begingroup$
    Sorry, I don't know what it means for $M(v)$ to be the matrix of the vector $v$. What's the matrix of the vector $(1,2,3)$?
    $endgroup$
    – Gerry Myerson
    yesterday






  • 1




    $begingroup$
    @GerryMyerson Knowing Axler, it will be the coordinate vector for the given basis (in this case, $v_1, ldots, v_n$). For example, if the basis is $(0, 0, 1), (0, 1, 1), (1, 1, 1)$ in the space $Bbb{R}^3$, then $M(1, 2, 3) = begin{bmatrix} 1 \ 1 \ 1 end{bmatrix}$.
    $endgroup$
    – Theo Bendit
    yesterday
















4












4








4





$begingroup$


In Linear Algebra Done Right, it said




Suppose $T in mathcal{L}(V,W)$ and $v in V$. Suppose $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$. Then $$M(Tv) = M(T)M(v)$$




$M(T)$ is the m-by-n matrix whose entries $A_{j,k}$ are defined by $Tv_k = A_{1,k}w_1 + ... + A_{m,k}w_m$ suppose $T in mathcal{L}(V,W)$ and $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$.



$M(v)$ is the matrix of vector $v$.



I generally follow the following proof:



Suppose $v = c_1v_1 + ... + c_nv_n$, where $c_1,...,c_n in mathbb{F}$. Thus
$$Tv = c_1Tv_1 +...+c_nTv_n$$



Hence



begin{equation}
begin{split}
M(Tv) &= c_1M(Tv_1) + ...+ c_nM(Tv_n)\
& = c_1M(T)_{.,1} +...+c_nM(T)_{.,n} \
& = M(T)M(v)
end{split}
end{equation}



But I have questions on the meaning of the proof. The book said it means each m-by-n matrix $A$ induces a linear map from $mathbb{F}^{n,1}$ to $mathbb{F}^{m,1}$. The result can be used to think of every linear map as a matrix multiplication map after suitable relabeling via the isomorphisms given by $M$.




  1. Is the shape of $M(Tv)$ m by 1, $M(T)$ m by n, and $M(v)$ n by 1?

  2. What is meant by suitable relabeling via the isomorphisms given by $M$? Does it just mean $M(T)$ is a isomorphism linear map between $M(v)$ and $M(Tv)$?










share|cite|improve this question











$endgroup$




In Linear Algebra Done Right, it said




Suppose $T in mathcal{L}(V,W)$ and $v in V$. Suppose $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$. Then $$M(Tv) = M(T)M(v)$$




$M(T)$ is the m-by-n matrix whose entries $A_{j,k}$ are defined by $Tv_k = A_{1,k}w_1 + ... + A_{m,k}w_m$ suppose $T in mathcal{L}(V,W)$ and $v_1,...,v_n$ is a basis of $V$ and $w_1,...,w_m$ is a basis of $W$.



$M(v)$ is the matrix of vector $v$.



I generally follow the following proof:



Suppose $v = c_1v_1 + ... + c_nv_n$, where $c_1,...,c_n in mathbb{F}$. Thus
$$Tv = c_1Tv_1 +...+c_nTv_n$$



Hence



begin{equation}
begin{split}
M(Tv) &= c_1M(Tv_1) + ...+ c_nM(Tv_n)\
& = c_1M(T)_{.,1} +...+c_nM(T)_{.,n} \
& = M(T)M(v)
end{split}
end{equation}



But I have questions on the meaning of the proof. The book said it means each m-by-n matrix $A$ induces a linear map from $mathbb{F}^{n,1}$ to $mathbb{F}^{m,1}$. The result can be used to think of every linear map as a matrix multiplication map after suitable relabeling via the isomorphisms given by $M$.




  1. Is the shape of $M(Tv)$ m by 1, $M(T)$ m by n, and $M(v)$ n by 1?

  2. What is meant by suitable relabeling via the isomorphisms given by $M$? Does it just mean $M(T)$ is a isomorphism linear map between $M(v)$ and $M(Tv)$?







matrices linear-transformations






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited yesterday







JOHN

















asked yesterday









JOHN JOHN

4589




4589












  • $begingroup$
    In your blockquote, you haven't told us what $M$ means.
    $endgroup$
    – Gerry Myerson
    yesterday










  • $begingroup$
    @GerryMyerson revised
    $endgroup$
    – JOHN
    yesterday










  • $begingroup$
    Sorry, I don't know what it means for $M(v)$ to be the matrix of the vector $v$. What's the matrix of the vector $(1,2,3)$?
    $endgroup$
    – Gerry Myerson
    yesterday






  • 1




    $begingroup$
    @GerryMyerson Knowing Axler, it will be the coordinate vector for the given basis (in this case, $v_1, ldots, v_n$). For example, if the basis is $(0, 0, 1), (0, 1, 1), (1, 1, 1)$ in the space $Bbb{R}^3$, then $M(1, 2, 3) = begin{bmatrix} 1 \ 1 \ 1 end{bmatrix}$.
    $endgroup$
    – Theo Bendit
    yesterday




















  • $begingroup$
    In your blockquote, you haven't told us what $M$ means.
    $endgroup$
    – Gerry Myerson
    yesterday










  • $begingroup$
    @GerryMyerson revised
    $endgroup$
    – JOHN
    yesterday










  • $begingroup$
    Sorry, I don't know what it means for $M(v)$ to be the matrix of the vector $v$. What's the matrix of the vector $(1,2,3)$?
    $endgroup$
    – Gerry Myerson
    yesterday






  • 1




    $begingroup$
    @GerryMyerson Knowing Axler, it will be the coordinate vector for the given basis (in this case, $v_1, ldots, v_n$). For example, if the basis is $(0, 0, 1), (0, 1, 1), (1, 1, 1)$ in the space $Bbb{R}^3$, then $M(1, 2, 3) = begin{bmatrix} 1 \ 1 \ 1 end{bmatrix}$.
    $endgroup$
    – Theo Bendit
    yesterday


















$begingroup$
In your blockquote, you haven't told us what $M$ means.
$endgroup$
– Gerry Myerson
yesterday




$begingroup$
In your blockquote, you haven't told us what $M$ means.
$endgroup$
– Gerry Myerson
yesterday












$begingroup$
@GerryMyerson revised
$endgroup$
– JOHN
yesterday




$begingroup$
@GerryMyerson revised
$endgroup$
– JOHN
yesterday












$begingroup$
Sorry, I don't know what it means for $M(v)$ to be the matrix of the vector $v$. What's the matrix of the vector $(1,2,3)$?
$endgroup$
– Gerry Myerson
yesterday




$begingroup$
Sorry, I don't know what it means for $M(v)$ to be the matrix of the vector $v$. What's the matrix of the vector $(1,2,3)$?
$endgroup$
– Gerry Myerson
yesterday




1




1




$begingroup$
@GerryMyerson Knowing Axler, it will be the coordinate vector for the given basis (in this case, $v_1, ldots, v_n$). For example, if the basis is $(0, 0, 1), (0, 1, 1), (1, 1, 1)$ in the space $Bbb{R}^3$, then $M(1, 2, 3) = begin{bmatrix} 1 \ 1 \ 1 end{bmatrix}$.
$endgroup$
– Theo Bendit
yesterday






$begingroup$
@GerryMyerson Knowing Axler, it will be the coordinate vector for the given basis (in this case, $v_1, ldots, v_n$). For example, if the basis is $(0, 0, 1), (0, 1, 1), (1, 1, 1)$ in the space $Bbb{R}^3$, then $M(1, 2, 3) = begin{bmatrix} 1 \ 1 \ 1 end{bmatrix}$.
$endgroup$
– Theo Bendit
yesterday












2 Answers
2






active

oldest

votes


















16












$begingroup$

In answer to your first question, yes to all three: $v$ is an element of the $n$-dimensional space $V$, so the coordinate vector with respect to the basis will be an $n times 1$ column vector. Similarly, $Tv in W$, which is an $n$-dimensional space, so $M(Tv)$ will be an $m times 1$ column vector. Finally, $M(T)$ is built from transforming the $n$ basis vectors of the domain, forming each an $m times 1$ coordinate column vector, which are put into an $m times n$ matrix.



In answer to your second question, consult the following commutative diagram (made in Paint :( ):



enter image description here



The process of applying $T$ to a vector $v in V$ is the top row of the diagram. However, there's a parallel process happening between $Bbb{F}^n$ and $Bbb{F}^m$, mirroring the same process.



The isomorphism being referred to are the double arrows, taking us between $V$ and $Bbb{F}^n$ and $W$ and $Bbb{F}^m$, by way of coordinate vectors. The coordinate vector map on $V$ is a linear map between $V$ and $Bbb{F}^n$ that is invertible, making it an isomorphism (and similarly for $W$). That is, the two spaces are structurally identical, and anything we can do with one space, we can view it in the other.



In $V$, we have some abstract vectors, and an abstract linear transformation $T$ that maps vectors in $V$ to vectors in $W$. However, using this isomorphism, we can view $V$ slightly differently as $Bbb{F}^n$, and similarly for $W$, which means $T$ boils down to a linear map from $Bbb{F}^n$ to $Bbb{F}^m$, which can be characterised as matrix multiplication. The matrix, in particular, is $M(T)$.






share|cite|improve this answer









$endgroup$













  • $begingroup$
    Very nice graph!
    $endgroup$
    – JOHN
    yesterday






  • 4




    $begingroup$
    Upvote for the commutative paint diagram!
    $endgroup$
    – Jannik Pitt
    yesterday










  • $begingroup$
    it seems that T and M(T) is also isomorphic ?
    $endgroup$
    – JOHN
    yesterday










  • $begingroup$
    Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
    $endgroup$
    – Theo Bendit
    yesterday












  • $begingroup$
    @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
    $endgroup$
    – Theo Bendit
    yesterday



















4












$begingroup$


  1. Yes, those would be the shapes of those vectors when represented as matrices. Given that we're multiply by vectors on the right.


  2. There is a theorem that if $V$ is an $n-$dimensional vector space over a field $F,$ then $V$ is isomorphic to $F^n.$ Here the isomorphic mappings assign coordinates to our vectors and our linear transformation. It doesn't mean that $M$ is an isomorphism between $M(v)$ and $M(Tv).$ These are particular vectors. The map $M$ actually induces an isomorphism from $Vto F^n$, isomorphism from $Tto F^{ntimes m}$, and an isomorphism from $Wto F^m.$



I actually like the way that this is done. The Author is telling you that you're representation of $T$ by a matrix depends on your choice of basis in $F^n$. A fact that is important to remember.






share|cite|improve this answer









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    2 Answers
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    2 Answers
    2






    active

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    active

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    active

    oldest

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    16












    $begingroup$

    In answer to your first question, yes to all three: $v$ is an element of the $n$-dimensional space $V$, so the coordinate vector with respect to the basis will be an $n times 1$ column vector. Similarly, $Tv in W$, which is an $n$-dimensional space, so $M(Tv)$ will be an $m times 1$ column vector. Finally, $M(T)$ is built from transforming the $n$ basis vectors of the domain, forming each an $m times 1$ coordinate column vector, which are put into an $m times n$ matrix.



    In answer to your second question, consult the following commutative diagram (made in Paint :( ):



    enter image description here



    The process of applying $T$ to a vector $v in V$ is the top row of the diagram. However, there's a parallel process happening between $Bbb{F}^n$ and $Bbb{F}^m$, mirroring the same process.



    The isomorphism being referred to are the double arrows, taking us between $V$ and $Bbb{F}^n$ and $W$ and $Bbb{F}^m$, by way of coordinate vectors. The coordinate vector map on $V$ is a linear map between $V$ and $Bbb{F}^n$ that is invertible, making it an isomorphism (and similarly for $W$). That is, the two spaces are structurally identical, and anything we can do with one space, we can view it in the other.



    In $V$, we have some abstract vectors, and an abstract linear transformation $T$ that maps vectors in $V$ to vectors in $W$. However, using this isomorphism, we can view $V$ slightly differently as $Bbb{F}^n$, and similarly for $W$, which means $T$ boils down to a linear map from $Bbb{F}^n$ to $Bbb{F}^m$, which can be characterised as matrix multiplication. The matrix, in particular, is $M(T)$.






    share|cite|improve this answer









    $endgroup$













    • $begingroup$
      Very nice graph!
      $endgroup$
      – JOHN
      yesterday






    • 4




      $begingroup$
      Upvote for the commutative paint diagram!
      $endgroup$
      – Jannik Pitt
      yesterday










    • $begingroup$
      it seems that T and M(T) is also isomorphic ?
      $endgroup$
      – JOHN
      yesterday










    • $begingroup$
      Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
      $endgroup$
      – Theo Bendit
      yesterday












    • $begingroup$
      @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
      $endgroup$
      – Theo Bendit
      yesterday
















    16












    $begingroup$

    In answer to your first question, yes to all three: $v$ is an element of the $n$-dimensional space $V$, so the coordinate vector with respect to the basis will be an $n times 1$ column vector. Similarly, $Tv in W$, which is an $n$-dimensional space, so $M(Tv)$ will be an $m times 1$ column vector. Finally, $M(T)$ is built from transforming the $n$ basis vectors of the domain, forming each an $m times 1$ coordinate column vector, which are put into an $m times n$ matrix.



    In answer to your second question, consult the following commutative diagram (made in Paint :( ):



    enter image description here



    The process of applying $T$ to a vector $v in V$ is the top row of the diagram. However, there's a parallel process happening between $Bbb{F}^n$ and $Bbb{F}^m$, mirroring the same process.



    The isomorphism being referred to are the double arrows, taking us between $V$ and $Bbb{F}^n$ and $W$ and $Bbb{F}^m$, by way of coordinate vectors. The coordinate vector map on $V$ is a linear map between $V$ and $Bbb{F}^n$ that is invertible, making it an isomorphism (and similarly for $W$). That is, the two spaces are structurally identical, and anything we can do with one space, we can view it in the other.



    In $V$, we have some abstract vectors, and an abstract linear transformation $T$ that maps vectors in $V$ to vectors in $W$. However, using this isomorphism, we can view $V$ slightly differently as $Bbb{F}^n$, and similarly for $W$, which means $T$ boils down to a linear map from $Bbb{F}^n$ to $Bbb{F}^m$, which can be characterised as matrix multiplication. The matrix, in particular, is $M(T)$.






    share|cite|improve this answer









    $endgroup$













    • $begingroup$
      Very nice graph!
      $endgroup$
      – JOHN
      yesterday






    • 4




      $begingroup$
      Upvote for the commutative paint diagram!
      $endgroup$
      – Jannik Pitt
      yesterday










    • $begingroup$
      it seems that T and M(T) is also isomorphic ?
      $endgroup$
      – JOHN
      yesterday










    • $begingroup$
      Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
      $endgroup$
      – Theo Bendit
      yesterday












    • $begingroup$
      @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
      $endgroup$
      – Theo Bendit
      yesterday














    16












    16








    16





    $begingroup$

    In answer to your first question, yes to all three: $v$ is an element of the $n$-dimensional space $V$, so the coordinate vector with respect to the basis will be an $n times 1$ column vector. Similarly, $Tv in W$, which is an $n$-dimensional space, so $M(Tv)$ will be an $m times 1$ column vector. Finally, $M(T)$ is built from transforming the $n$ basis vectors of the domain, forming each an $m times 1$ coordinate column vector, which are put into an $m times n$ matrix.



    In answer to your second question, consult the following commutative diagram (made in Paint :( ):



    enter image description here



    The process of applying $T$ to a vector $v in V$ is the top row of the diagram. However, there's a parallel process happening between $Bbb{F}^n$ and $Bbb{F}^m$, mirroring the same process.



    The isomorphism being referred to are the double arrows, taking us between $V$ and $Bbb{F}^n$ and $W$ and $Bbb{F}^m$, by way of coordinate vectors. The coordinate vector map on $V$ is a linear map between $V$ and $Bbb{F}^n$ that is invertible, making it an isomorphism (and similarly for $W$). That is, the two spaces are structurally identical, and anything we can do with one space, we can view it in the other.



    In $V$, we have some abstract vectors, and an abstract linear transformation $T$ that maps vectors in $V$ to vectors in $W$. However, using this isomorphism, we can view $V$ slightly differently as $Bbb{F}^n$, and similarly for $W$, which means $T$ boils down to a linear map from $Bbb{F}^n$ to $Bbb{F}^m$, which can be characterised as matrix multiplication. The matrix, in particular, is $M(T)$.






    share|cite|improve this answer









    $endgroup$



    In answer to your first question, yes to all three: $v$ is an element of the $n$-dimensional space $V$, so the coordinate vector with respect to the basis will be an $n times 1$ column vector. Similarly, $Tv in W$, which is an $n$-dimensional space, so $M(Tv)$ will be an $m times 1$ column vector. Finally, $M(T)$ is built from transforming the $n$ basis vectors of the domain, forming each an $m times 1$ coordinate column vector, which are put into an $m times n$ matrix.



    In answer to your second question, consult the following commutative diagram (made in Paint :( ):



    enter image description here



    The process of applying $T$ to a vector $v in V$ is the top row of the diagram. However, there's a parallel process happening between $Bbb{F}^n$ and $Bbb{F}^m$, mirroring the same process.



    The isomorphism being referred to are the double arrows, taking us between $V$ and $Bbb{F}^n$ and $W$ and $Bbb{F}^m$, by way of coordinate vectors. The coordinate vector map on $V$ is a linear map between $V$ and $Bbb{F}^n$ that is invertible, making it an isomorphism (and similarly for $W$). That is, the two spaces are structurally identical, and anything we can do with one space, we can view it in the other.



    In $V$, we have some abstract vectors, and an abstract linear transformation $T$ that maps vectors in $V$ to vectors in $W$. However, using this isomorphism, we can view $V$ slightly differently as $Bbb{F}^n$, and similarly for $W$, which means $T$ boils down to a linear map from $Bbb{F}^n$ to $Bbb{F}^m$, which can be characterised as matrix multiplication. The matrix, in particular, is $M(T)$.







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered yesterday









    Theo BenditTheo Bendit

    20.7k12354




    20.7k12354












    • $begingroup$
      Very nice graph!
      $endgroup$
      – JOHN
      yesterday






    • 4




      $begingroup$
      Upvote for the commutative paint diagram!
      $endgroup$
      – Jannik Pitt
      yesterday










    • $begingroup$
      it seems that T and M(T) is also isomorphic ?
      $endgroup$
      – JOHN
      yesterday










    • $begingroup$
      Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
      $endgroup$
      – Theo Bendit
      yesterday












    • $begingroup$
      @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
      $endgroup$
      – Theo Bendit
      yesterday


















    • $begingroup$
      Very nice graph!
      $endgroup$
      – JOHN
      yesterday






    • 4




      $begingroup$
      Upvote for the commutative paint diagram!
      $endgroup$
      – Jannik Pitt
      yesterday










    • $begingroup$
      it seems that T and M(T) is also isomorphic ?
      $endgroup$
      – JOHN
      yesterday










    • $begingroup$
      Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
      $endgroup$
      – Theo Bendit
      yesterday












    • $begingroup$
      @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
      $endgroup$
      – Theo Bendit
      yesterday
















    $begingroup$
    Very nice graph!
    $endgroup$
    – JOHN
    yesterday




    $begingroup$
    Very nice graph!
    $endgroup$
    – JOHN
    yesterday




    4




    4




    $begingroup$
    Upvote for the commutative paint diagram!
    $endgroup$
    – Jannik Pitt
    yesterday




    $begingroup$
    Upvote for the commutative paint diagram!
    $endgroup$
    – Jannik Pitt
    yesterday












    $begingroup$
    it seems that T and M(T) is also isomorphic ?
    $endgroup$
    – JOHN
    yesterday




    $begingroup$
    it seems that T and M(T) is also isomorphic ?
    $endgroup$
    – JOHN
    yesterday












    $begingroup$
    Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
    $endgroup$
    – Theo Bendit
    yesterday






    $begingroup$
    Isomorphisms are maps between vector spaces; only vector spaces can be isomorphic (at least, until you study category theory). The map $M$ (given fixed bases) is itself an isomorphism between the space of linear maps from $V$ to $W$ and the $m times n$ matrices, but even then, it's not really correct to say that a map $T$ is "isomorphic" to a matrix $M(T)$. It would be more accurate (though not conventional) to describe the map $T$ as "similar" to the matrix $M(T)$, in a similar sense to similar matrices (bear in mind, $T$ is not a matrix).
    $endgroup$
    – Theo Bendit
    yesterday














    $begingroup$
    @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
    $endgroup$
    – Theo Bendit
    yesterday




    $begingroup$
    @JOHN Simply put, vectors cannot be isomorphic. Maps cannot be isomorphic. Only spaces can be isomorphic.
    $endgroup$
    – Theo Bendit
    yesterday











    4












    $begingroup$


    1. Yes, those would be the shapes of those vectors when represented as matrices. Given that we're multiply by vectors on the right.


    2. There is a theorem that if $V$ is an $n-$dimensional vector space over a field $F,$ then $V$ is isomorphic to $F^n.$ Here the isomorphic mappings assign coordinates to our vectors and our linear transformation. It doesn't mean that $M$ is an isomorphism between $M(v)$ and $M(Tv).$ These are particular vectors. The map $M$ actually induces an isomorphism from $Vto F^n$, isomorphism from $Tto F^{ntimes m}$, and an isomorphism from $Wto F^m.$



    I actually like the way that this is done. The Author is telling you that you're representation of $T$ by a matrix depends on your choice of basis in $F^n$. A fact that is important to remember.






    share|cite|improve this answer









    $endgroup$


















      4












      $begingroup$


      1. Yes, those would be the shapes of those vectors when represented as matrices. Given that we're multiply by vectors on the right.


      2. There is a theorem that if $V$ is an $n-$dimensional vector space over a field $F,$ then $V$ is isomorphic to $F^n.$ Here the isomorphic mappings assign coordinates to our vectors and our linear transformation. It doesn't mean that $M$ is an isomorphism between $M(v)$ and $M(Tv).$ These are particular vectors. The map $M$ actually induces an isomorphism from $Vto F^n$, isomorphism from $Tto F^{ntimes m}$, and an isomorphism from $Wto F^m.$



      I actually like the way that this is done. The Author is telling you that you're representation of $T$ by a matrix depends on your choice of basis in $F^n$. A fact that is important to remember.






      share|cite|improve this answer









      $endgroup$
















        4












        4








        4





        $begingroup$


        1. Yes, those would be the shapes of those vectors when represented as matrices. Given that we're multiply by vectors on the right.


        2. There is a theorem that if $V$ is an $n-$dimensional vector space over a field $F,$ then $V$ is isomorphic to $F^n.$ Here the isomorphic mappings assign coordinates to our vectors and our linear transformation. It doesn't mean that $M$ is an isomorphism between $M(v)$ and $M(Tv).$ These are particular vectors. The map $M$ actually induces an isomorphism from $Vto F^n$, isomorphism from $Tto F^{ntimes m}$, and an isomorphism from $Wto F^m.$



        I actually like the way that this is done. The Author is telling you that you're representation of $T$ by a matrix depends on your choice of basis in $F^n$. A fact that is important to remember.






        share|cite|improve this answer









        $endgroup$




        1. Yes, those would be the shapes of those vectors when represented as matrices. Given that we're multiply by vectors on the right.


        2. There is a theorem that if $V$ is an $n-$dimensional vector space over a field $F,$ then $V$ is isomorphic to $F^n.$ Here the isomorphic mappings assign coordinates to our vectors and our linear transformation. It doesn't mean that $M$ is an isomorphism between $M(v)$ and $M(Tv).$ These are particular vectors. The map $M$ actually induces an isomorphism from $Vto F^n$, isomorphism from $Tto F^{ntimes m}$, and an isomorphism from $Wto F^m.$



        I actually like the way that this is done. The Author is telling you that you're representation of $T$ by a matrix depends on your choice of basis in $F^n$. A fact that is important to remember.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered yesterday









        MelodyMelody

        1,01912




        1,01912






























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