Dimension of a subspace of linear transformations. Exercise from FDVS by Paul R. Halmos

394 Views Asked by At

This is an exercise from Finite Dimensional Vector Spaces by Paul R. Halmos, page 61, Ex. 6:

Let $V$ be a $n$-dimensional vector space, $\mathcal{L}(V, V)$ set of all linear transformations on $V$ (also a vector space) and $S = \{B \in \mathcal{L}(V, V) : AB = 0\}$ for some $A \in \mathcal{L}(V, V)$. What values can dimension of $S$ have?

My solution looks very robotic. I represent linear transformations $A$ and $B$ as an $n^2$ dimensional vectors with coordinates $a_{ij}$ and $b_{ij}$, then I get a system of $n^2$ linear equations $\sum_{j=1}^n a_{ij} \cdot b_{jk} = 0$, for $i,k \in \{1, ... , n\}$. And then by choosing appropriate scalars $a_{ij}$ we can set dimension of $S$ to any value from $0$ to $n^2$ - I'm not sure, It looks like not any value, but only of the form $l*n$, for $l \in \{0,1, ... , n\}$.

Is there a better solution?

Update:

@Omnomnomnom, since in this book kernels were not yet introduced, I think the annihilators can be used to solve this problem. Could you please take a look at my solution:

Let $\{x_1, ..., x_n\}$ be a basis of $V$, linear transformation $Ax = \sum_{i=1}^n a_i(x) \cdot x_i$, where $\{a_1, ..., a_n\}$ are linear functionals on $V$ and $Bx = B(\sum_{i=1}^n \xi_i \cdot x_i ) = \sum_{i=1}^n \xi_i \cdot b_i$, where $\{b_1, ..., b_n\}$ are some vectors in $V$ (column of the matrix of $B$). Then $AB=0$ means that $a_i(b_j)=0$, for all $i,j = 1..n$ or $b_j \in \{a_i\}^o$ annihilator of $a_i$. Since $\operatorname{span}(a_1)$ is one dimensional (assuming $a_1 \neq 0$), the annihilator subspace $\{a_1\}^o$ is $n-1$ dimensional. And we can choose $n-1$ linearly independent vectors $b_i$ in that annihilator subspace. Then dimension of $B$ is $n(n-1)$. By adding more linearly independent $a_i$ to $A$, the dimension of $B = n(n-k)$, where $k = \operatorname{dim} \operatorname{span} \{a_1, ..., a_n\} \in \{0, 1, ..., n\}$.

1

There are 1 best solutions below

2
On BEST ANSWER

Hint: Note that $AB = 0$ if and only if the image of $B$ is in the kernel of $A$. So, we can identify $S$ with $\mathcal L(V,\ker A)$.

For a more matrix-oriented approach: note that $AB = 0$ if and only if every column of $B$ lies in the null space of $A$. Now, using a basis for the nullspace of $A$, construct a basis for $S$.