Is a Matrix of Only Diagonal Ones Always Equal to its Transpose?

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In summary, the conversation discusses the transpose of a matrix of only diagonal ones and whether it is always equal to its original state. The conclusion is that this is indeed the case, as it works for any diagonal matrix where all non-diagonal elements are 0. The definition of "transpose" is also mentioned in relation to this concept.
  • #1
mikedamike
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Very quick question.

Im 99% sure that the transpose of a matrix of only diagonal ones is always equal to its original state.

Ie
|1000| |1000| T
|0100| |0100|
|0010| = |0010|
|0001| |0001|


So my question is am i correct ?

Thanks in advance.
 
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  • #2
hi mikedamike! :smile:
mikedamike said:
Very quick question.

Im 99% sure that the transpose of a matrix of only diagonal ones is always equal to its original state.

Ie
Code:
              |1000|       |1000| T
              |0100|       |0100|
              |0010| =    |0010|
              |0001|       |0001|


So my question is am i correct ?

Thanks in advance.

yes, and it works for any diagonal matrix …

(a)Tii = (a)ii :wink:
 
  • #3
The definition of "transpose" is that if the elements of A are "[itex]a_{ij}[/itex]" then the elements of A^T are [itex]a_{ji}[/itex]. So if all non-diagonal elements are 0, we have [itex]a_{ij}= a_{ji}= 0[/itex]. And, of course, on the diagonal i= j so we still have [itex]a_{ij}= a_{ji}[/itex].
 

1. What is a matrix?

A matrix is a rectangular array of numbers or symbols that are arranged in rows and columns. It is a fundamental tool in linear algebra and has various applications in mathematics, physics, and engineering.

2. How do you multiply matrices?

To multiply two matrices, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The resulting matrix will have the same number of rows as the first matrix and the same number of columns as the second matrix. The product of two matrices is found by multiplying the corresponding elements in each row of the first matrix by the corresponding elements in each column of the second matrix and then summing the products.

3. What is the identity matrix?

The identity matrix is a square matrix with 1s on the main diagonal and 0s everywhere else. When a matrix is multiplied by the identity matrix, the result is the original matrix.

4. Can a matrix be inverted?

Not all matrices can be inverted. A matrix can only be inverted if it is square and its determinant is not equal to 0. The inverse of a matrix is another matrix that, when multiplied by the original matrix, produces the identity matrix.

5. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra. Eigenvalues are scalars that represent the scaling factor of the eigenvectors. Eigenvectors are special vectors that, when multiplied by a matrix, produce a scalar multiple of themselves. They are used to understand the behavior of linear transformations and have numerous applications in fields such as engineering, physics, and data analysis.

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