Linear Algebra Communications Assignment Help

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Problem 1.
Let L and M be finite dimensional linear spaces and let f : L M → be a linear map. Prove
that the subspaces ker f and Imf are finite dimensional and
dim ker f + dim Imf = dim L.
Hint: Use the extension of a basis theorem.
Problem 2.
Consider the space L(V, V ) of linear maps from a finite dimensional real linear space V to
itself. Which of the following subsets of L(V, V ) are linear spaces:
(a) {f ∈ L(V, V ) dim Imf = 0};
(b) {f ∈ L(V, V ) | dim kerf = 0};
(c) {f ∈ L(V, V ) | | dim Imf < dim V }.
Reformulate the obtained results in terms of matrices.
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Problem 3.
(a) Show that there are no finite square real or complex matrices X and Y such that XY −Y X
= I, where I is the identity matrix. d
(b) Check that multiplication by x and differentiation are linear dx maps on the (real or
complex) space P of all polynomials in one d d variable x. Compute the action of dx ◦ x
− x ◦ in P. Here dx “ ◦” denotes the composition of two maps.
(c) Introduce the concept of a matrix with infinitely many rows and columns. Define the
multiplication for infinite matrices in which each column and each row has a finite
number of nonzero elements. Using (b), find a solution for XY − Y X = I in terms of
infinite matrices.

Solution 1.
Let L and M be finite dimensional linear spaces and let f : L → M be a linear map. We want
to show that Imf and ker f are finite dimensional and that dim Im f + dim kerf = dim L. We
assume that dim L = n. Let {bj} be a basis for kernel of f. By the Basis Extension Theorem we
know that there are linearly independent vectors {ck} such that the ordered set {ai} = {bj ,
ck} forms a basis for L. Since the total number of basis elements for L is finite, it must be
that ker f ⊆ L is also finite dimensional. Thus, dim ker f = l ≤ n.
Next we show that {f(ai)}n span Imf. We choose v ∈ Imf. Since i=l+1 n ai} spans L, there exist
scalars di such that v = f ( diai). Noting that i=1 f(ai) = 0, 1 ≤ i ≤ l, we have v = { i n =l+1
cif(ai). Thus, {f(ai)}i n =l+1 spans Imf.
f. To verify the linear independence of {f(ai)}n we consider the linear n i=l+1 n combination
i=l+1 cif(ai) = 0. Then by linearity f i=l+1 ciai = 0. Since ai are linearly independent it must be
that all ci = 0. Hence all n {f(ai)}i=l+1 are linearly independent and we can consider them a
basis for Imf. This also confirms that Imf is finite dimensional.
Hence, dim Imf + dim kerf = dim L

Solution2.
(a) {f ∈ L(V, V ) | dim Imf = 0}
This is a subspace. The defining condition of this subset is equivalent to f = 0. Thus for all v
∈ V , f(v) = 0. By linearity, this is also true for any combination of such linear maps.
Considering the matrix representation of this subspace of linear maps, we conclude that it
is isomorphic to the subspace of zero matrices.
(b) {f ∈ L(V, V ) | dim kerf = 0}
This is not a subspace, unless dim V = 0. We prove this by contradiction. Suppose v ∈ V.
Then necessarily for f in the subset, f(v) = 0. Further, by linearity it must be that kf(v) = 0, k
∈ F. We pick k = 0 and then for any vector v ∈ V , the action of kf on v gives 0, independent
of v. Thus dim ker kf �= 0. However, if dim V = 0 then also dim ker f = 0. In this case V ⊆ {0}
and necessarily f = 0.
Formulated in terms of matrices, we can consider this subset of linear transforms
isomorphic with the set of matrices with rank equal to the dimension of V . This is not a
subspace since multiplication of any such matrix by 0 will result in a matrix that has rank
less than the dimension of V unless the dimension of V is 0.

c) {f ∈ L(V, V ) | dim Imf < dim V }.
This is not a subspace, unless dim V = 1. We prove this by con n tradiction. Let dim V ≥ 2
and let {ai}i=1 be a basis for V. Further, let fi(aj ) aj , for i = j and fi(aj ) 0, for i =� j. Then
for Thus, dim Imfi = 1 < dim V . = = n i=1 ciai, fi(v) = ciai v = . However, n n ( fi) v = v and
dim Im ( fi) = n = dim V . For dim V = 1 i=1 i=1 it must be that f = 0, dim Imf = 0 and proof
follows as in part (a). We cannot have dim V = 0, since then dim Imf = dim V .
We can consider this subset of linear transforms isomorphic to the set of matrices with
rank less than the dimension of V . For dim V > 1 we consider a subset of such linearly
independent matrices of rank 1 and note that the sum of these matrices may result in a
matrix of rank dim V . In case dim V = 1 our subset is a subspace of zero matrices as
described in part (a).
Solution 3.
We want to prove that there are no finite square matrices such that XY −Y X = I. By
contradiction, suppose we could find such X and Y . Let X, Y, I ∈ Fm×m. Then trace (XY − Y
X) = trace I = m.
However,

Thus, it is impossible to find finite square matrices that satisfy XY − Y X = I.
Both of these maps are linear:

We calculate the compositions of these two maps:

c) We introduce the concept of matrices with infinitely many rows and columns. To
define multiplication of two such matrices, we restrict our attention to matrices with
finitely many nonzero elements on any given row or column. Then for any given row
{aij}∞ there j=1 exists a number Ni such that aij = 0, for j > Ni. Similarily for any given
column {aij}∞ i=1 there exists a number Mj such that aij = 0, for i > Mj . Multiplication of
two infinite matrices A and B is then defined as
In order to construct the matrix representations of linear transd forms x and we consider
their actions on the standard basis ele dx ment xk. For k ≥ 0, x(xk) = xk+1. Similarily for k >
0, d (xk dx ) = d kxk−1, k = 0, dx (x0) = 0. Hence,

By matrix multiplication:
Hence, X and Y as defined above are a solution to XY −Y X = I

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