Let such that where . Then for and it holds , so is a linear transformation.
The matrix is a linear mapping . Let be a basis for . Then the space has dimension at most . Then, using the dimension formula we have , so rearranging we get .
Let be a matrix of rank 1. Then, the image of is a space spanned by a single vector, say , and for some nonzero . We can assume that , since the vector is unique up to a scaling and change of basis. Then, the kernel of is given by the vectors for . Next, consider the matrix , so that as well, and . It is easy to see that and describe the same linear transformation, so they are equal as matrices. The representation of is not unique, since we could scale one of the vectors and arbitrarily as long as we scale the other accordingly.
a) It is very easy to see that performing the vector space operations coordinate-wise preserves the vector space structure in the product space.
b) Let . Then we have and , so is a linear operator.
c) We have where so . Furthermore, we have by definition that , and . Therefore, the dimension formula has the form .
We can write , and . We have the following multiplication table
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where the first element of a column denotes the matrix that is multiplied from the right by the first element of a given row. Then, . For we have then in the given basis the form
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The matrix with the given property satisfies the equation . Solving this yields that the matrix has to have the form for any