Worksheet 5

Problem 2

Let AA be invertible.

  1. Show that AtA^t is invertible.
  2. Prove that (At)1=(A1)t\p{A^t}^{-1} = \p{A^{-1}}^t.
Solution.

We will do both parts at the same time by giving a left and right inverse for AtA^t. Recall that

(AB)t=BtAt.\p{AB}^t = B^t A^t.

Note that the order of multiplication is reversed when we take transposes. Since AA is invertible, A1A^{-1} exists and

{AA1=In,A1A=In.\begin{cases} AA^{-1} = I_n, \\ A^{-1}A = I_n. \end{cases}

Since Int=InI_n^t = I_n, transposing both sides of the equations above yields

{(A1)tAt=(AA1)t=Int=In,At(A1)t=(A1A)t=Int=In.\begin{cases} \p{A^{-1}}^tA^t = \p{AA^{-1}}^t = I_n^t = I_n, \\ A^t\p{A^{-1}}^t = \p{A^{-1}A}^t = I_n^t = I_n. \end{cases}

In summary,

(A1)tAt=At(A1)t=In.\p{A^{-1}}^tA^t = A^t\p{A^{-1}}^t = I_n.

AtA^t has a left and right inverse, so it's invertible, and we have shown that the left and right inverse are both (A1)t\p{A^{-1}}^t, i.e., (At)1=(A1)t\p{A^t}^{-1} = \p{A^{-1}}^t.

Problem 6

Let AA and BB be n×nn \times n matrices such that AB=InAB = I_n.

  1. Prove that AA and BB are invertible.
  2. Prove that A=B1A = B^{-1} and B=A1B = A^{-1}.
  3. State and prove analogous results for linear transformations defined on finite-dimensional vector spaces.
Solution.
  1. Note that AA is surjective: given wFnw \in \F^n, we have

    (AB)w=Inw=w    A(Bw)=w\p{AB}w = I_nw = w \implies A\p{Bw} = w

    since matrix multiplication is associative. If we set v=BwFnv = Bw \in \F^n, then Av=wAv = w, so because ww was arbitrary, this shows that AA is surjective.

    Because AA is a square matrix, surjective is equivalent to invertible, so AA is invertible.

  2. Now that we know that AA is invertible, we know that A1A^{-1} exists. Thus, multiplying both sides of AB=InAB = I_n by A1A^{-1},

    A1AB=A1In    B=A1.A^{-1}AB = A^{-1}I_n \implies B = A^{-1}.

    Hence, BB is equal to the invertible matrix A1A^{-1}, so BB itself is invertible with B1=(A1)1=AB^{-1} = \p{A^{-1}}^{-1} = A.

  3. Let T ⁣:VW\func{T}{V}{W}, S ⁣:WV\func{S}{W}{V} be linear maps between vector spaces, where dimV=dimW=n<\dim{V} = \dim{W} = n < \infty. If TS=IWT \circ S = I_W, then TT and SS are both invertible with T=S1T = S^{-1} and S=T1S = T^{-1}.

    To prove this, let's show that TT is surjective. The proof is the same as before: let wWw \in W, and notice that

    (TS)(w)=IW(w)    T(S(w))=w.\p{T \circ S}\p{w} = I_W\p{w} \implies T\p{S\p{w}} = w.

    As before, setting v=S(w)v = S\p{w} gives T(v)=wT\p{v} = w, so TT is surjective, hence invertible. I'll reprove this here in case you haven't seen this fact:

    Because TT is surjective, it follows that dimR(T)=dimW=n\dim R\p{T} = \dim W = n. Since VV is finite-dimensional, we may apply rank-nullity to get

    dimV=dimR(T)+dimN(T)    n=n+dimN(T)    dimN(T)=0    N(T)={0},\begin{aligned} \dim V = \dim R\p{T} + \dim N\p{T} &\implies n = n + \dim N\p{T} \\ &\implies \dim N\p{T} = 0 \\ &\implies N\p{T} = \set{0}, \end{aligned}

    so TT is injective. Thus, TT is a bijection, and the inverse of a linear map is linear, so TT is an isomorphism. (Recall that isomorphism here means that TT is a linear bijection and that T1T^{-1} is a linear map.)

    The same calculations as above still work:

    T1TS=T1IW    S=T1    S1=(T1)1=T.T^{-1} \circ T \circ S = T^{-1} \circ I_W \implies S = T^{-1} \implies S^{-1} = \p{T^{-1}}^{-1} = T.

Alternatively, you could have shown that BB is injective, hence invertible.

Exercise 1.

Let AA be an n×mn \times m matrix and BB be an m×nm \times n matrix such that AB=InAB = I_n. Show that BB is injective.