Transpose: a linear transformation?

In summary, the conversation is discussing whether transposing is a linear transformation and if every linear transformation can be accomplished by a matrix. It is clarified that linear transformations apply to both vectors and matrices, and while no 2 by 2 matrix can map to its transpose, a 4 by 4 matrix can represent this transformation. The conversation also mentions the necessary and sufficient condition for a matrix to square to 1. Overall, the conversation is mostly a confusion of words and clarifies the concept of linear transformations and matrices.
  • #1
kostoglotov
234
6
Alternate title: Is the textbook contradicting itself?

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imgur link: http://i.imgur.com/3sTVgwr.jpg

But

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imgur link: http://i.imgur.com/33Ufncb.jpg

So...it would appear that transposing has the property of linearity, but no matrix can achieve it...is transposing a linear transformation? The text said every linear transformation would be accomplished by a matrix.

Or, strictly speaking, do linear transformations only apply to vectors?
 
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  • #2
This looks to me as mostly a confusion of words. First, "do linear transformations only apply to vectors?". The set of all n by m matrices with the usual addition and scalar multiplication of matrices is a vector space and, in the sense of linear algebra, there is no difference between a "matrix" and a "vector".
sS
Second, the second statement, that "Show no matrix will do it" (map a matrix to its transpose) is incorrect- there is such a matrix but applying it to a vector space of matrices is not the usual matrix multiplication.

To write a linear transformation, from one vector space to another, as a matrix we
1: choose an ordered basis for the both vector spaces.
2: Apply that linear transformation to each basis vector of the first space.
3: Write the result as a linear combination of the basis vectors for the second space.
4: Take the coefficients of each such linear combination as a column for the matrix.

If the two vector spaces are, say, the vector space of two by two matrices, then each such matrix is of the form [itex]\begin{bmatrix}a & b \\ c & d \end{bmatrix}[/itex] and "standard" ordered basis for the vector space is
[tex]\{\begin{bmatrix}1 & 0 \\ 0 & 0 \end{bmatrix}, \begin{bmatrix}0 & 1 \\ 0 & 0 \end{bmatrix}, \begin{bmatrix}0 & 0 \\ 1 & 0 \end{bmatrix}, \begin{bmatrix}1 & 0 \\ 0 & 1 \end{bmatrix}\}[/tex]

That vectors space is four dimensional so the space of linear transformations from it to itself is 4x4= 16 dimensional and a matrix representing any such linear transformation is a 4 by 4 matrix. There is no 2 by 2 matrix such that, multiplied by a 2 by 2 matrix gives its transpose but there is a four by four matrix that, multiplied by the coefficient matrix as given by the ordered basis above, gives the coefficient matrix of its transpose, which is what Linear Algebra says must exist.

In the vector space of two by two matrices, the matrix representing the "transpose" linear transformation is
[tex]\begin{bmatrix}1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1\end{bmatrix}[/tex]
 
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  • #3
HallsofIvy said:
This looks to me as mostly a confusion of words. First, "do linear transformations only apply to vectors?". The set of all n by m matrices with the usual addition and scalar multiplication of matrices is a vector space and, in the sense of linear algebra, there is no difference between a "matrix" and a "vector".
sS
Second, the second statement, that "Show no matrix will do it" (map a matrix to its transpose) is incorrect- there is such a matrix but applying it to a vector space of matrices is not the usual matrix multiplication.

To write a linear transformation, from one vector space to another, as a matrix we
1: choose an ordered basis for the both vector spaces.
2: Apply that linear transformation to each basis vector of the first space.
3: Write the result as a linear combination of the basis vectors for the second space.
4: Take the coefficients of each such linear combination as a column for the matrix.

If the two vector spaces are, say, the vector space of two by two matrices, then each such matrix is of the form [itex]\begin{bmatrix}a & b \\ c & d \end{bmatrix}[/itex] and "standard" ordered basis for the vector space is
[tex]\{\begin{bmatrix}1 & 0 \\ 0 & 0 \end{bmatrix}, \begin{bmatrix}0 & 1 \\ 0 & 0 \end{bmatrix}, \begin{bmatrix}0 & 0 \\ 1 & 0 \end{bmatrix}, \begin{bmatrix}1 & 0 \\ 0 & 1 \end{bmatrix}\}[/tex]

That vectors space is four dimensional so the space of linear transformations from it to itself is 4x4= 16 dimensional and a matrix representing any such linear transformation is a 4 by 4 matrix. There is no 2 by 2 matrix such that, multiplied by a 2 by 2 matrix gives its transpose but there is a four by four matrix that, multiplied by the coefficient matrix as given by the ordered basis above, gives the coefficient matrix of its transpose, which is what Linear Algebra says must exist.

In the vector space of two by two matrices, the matrix representing the "transpose" linear transformation is
[tex]\begin{bmatrix}1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1\end{bmatrix}[/tex]

Just to double check, OP make sure that your matrix squares to 1 . since ## (A^T)^T=A ##. This is of course necessary but not sufficient.
 

1. What is a linear transformation?

A linear transformation is a mathematical operation that maps one vector space to another in a linear manner. This means that the output of the transformation is a linear combination of the input vectors.

2. What is the purpose of transposing in a linear transformation?

Transposing in a linear transformation allows for the manipulation of the input vectors to achieve a desired output. It can also help in solving systems of linear equations and finding inverses of matrices.

3. How is transposing different from other linear transformations?

Transposing is a specific type of linear transformation where the input and output vector spaces are of the same dimension. Other linear transformations may have different input and output dimensions, or may not necessarily be linear combinations of the input vectors.

4. What are some common applications of transposing in linear algebra?

Transposing is commonly used in data analysis, particularly in machine learning and statistics, to transform data for easier analysis and visualization. It is also used in solving systems of linear equations, finding eigenvalues and eigenvectors, and in determining the rank and nullity of a matrix.

5. How does transposing affect the properties of a matrix?

Transposing a matrix does not change its determinant or eigenvalues, but it does change its trace and rank. It also changes the properties of the matrix's inverse and transpose, and can have an impact on the matrix's orthogonality and symmetry.

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