Section 13.2

Supplementary Problems

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Problem 13.2.1

Let AA, BB be n×nn \times n matrices over C\mathbb {C}.

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(i)

Consider the maps

f1(A,B)=A⊗Bf2(A,B)=vec⁡(A)(vec⁡(B))Tf3(A,B)=vec⁡(A)(vec⁡(BT))Tf4(A,B)=vec⁡(B)(vec⁡(AT))T. \begin{align} f_{1}(A,B) & = A \otimes B \\ f_{2}(A,B) & = \operatorname {vec}(A)(\operatorname {vec}(B))^{T} \\ f_{3}(A,B) & = \operatorname {vec}(A)(\operatorname {vec}(B^{T}))^{T} \\ f_{4}(A,B) & = \operatorname {vec}(B)(\operatorname {vec}(A^{T}))^{T}. \end{align}

Is

tr⁡(f1(A,B))=tr⁡(f2(A,B)),tr⁡(f2(A,B))=tr⁡(f3(A,B)),tr⁡(f3(A,B))=tr⁡(f4(A,B)) ? \begin{align} \operatorname {tr}(f_{1}(A,B)) & = \operatorname {tr}(f_{2}(A,B)), \\ \operatorname {tr}(f_{2}(A,B)) & = \operatorname {tr}(f_{3}(A,B)), \\ \operatorname {tr}(f_{3}(A,B)) & = \operatorname {tr}(f_{4}(A,B)) \, ? \end{align}
(ii)

Show that

tr⁡(vec⁡(B)(vec⁡(A))T)≡tr⁡(ATB). \operatorname {tr}(\operatorname {vec}(B)(\operatorname {vec}(A))^{T}) \equiv \operatorname {tr}(A^{T}B).
Problem 13.2.2

Let AA be a 2×22 \times 2 matrix.

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(i)

Find vec⁡(A⊗I2+I2⊗A)\operatorname {vec}(A \otimes I_{2}+I_{2} \otimes A) and vec⁡(A⊗A)\operatorname {vec}(A \otimes A).

(ii)

Find the condition on AA such that vec⁡(A⊗A)=vec⁡(A⊗I2+I2⊗A)\operatorname {vec}(A \otimes A) = \operatorname {vec}(A \otimes I_{2}+I_{2} \otimes A).

Problem 13.2.3

Let AA, BB be n×nn \times n matrices. Show that

vec⁡(A∙B)=vec⁡(A)∙vec⁡(B) \operatorname {vec}(A \bullet B) = \operatorname {vec}(A) \bullet \operatorname {vec}(B)

where ∙\bullet denotes the Hadamard product.

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Problem 13.2.4

Find an invertible 3×33 \times 3 matrix XX which satisfies the following three matrix equations simultaneously

X−1(0−i0i00000)X=(10000000−1)X−1(00000−i0i0)X=12(010101010)X−1(00i000−i00)X=i2(0−1010−1010). \begin{align} X^{-1}\begin{pmatrix} 0 & -i & 0 \\ i & 0 & 0 \\ 0 & 0 & 0 \end{pmatrix}X & = \begin{pmatrix} 1 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & -1 \end{pmatrix} \\ X^{-1}\begin{pmatrix} 0 & 0 & 0 \\ 0 & 0 & -i \\ 0 & i & 0 \end{pmatrix}X & = \frac{1}{\sqrt{2}}\begin{pmatrix} 0 & 1 & 0 \\ 1 & 0 & 1 \\ 0 & 1 & 0 \end{pmatrix} \\ X^{-1}\begin{pmatrix} 0 & 0 & i \\ 0 & 0 & 0 \\ -i & 0 & 0 \end{pmatrix}X & = \frac{i}{\sqrt{2}}\begin{pmatrix} 0 & -1 & 0 \\ 1 & 0 & -1 \\ 0 & 1 & 0 \end{pmatrix}. \end{align}

Cast the equations into a Sylvester equation and then solve the equations. The first equation can be written as

(I3⊗(0−i0i00000)−(10000000−1)T⊗I3)vec⁡(X)=0→ \left( I_{3} \otimes \begin{pmatrix} 0 & -i & 0 \\ i & 0 & 0 \\ 0 & 0 & 0 \end{pmatrix} - \begin{pmatrix} 1 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & -1 \end{pmatrix}^{T} \otimes I_{3} \right)\operatorname {vec}(X) = \overrightarrow {0}

where 0→\overrightarrow {0} is the zero column vector.

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Problem 13.2.5

Consider the Pauli spin matrices σ1\sigma_{1}, σ2\sigma_{2} and

R:=σ1⊗σ2≡(0110)⊗(0−ii0)≡(000−i00i00−i00i000). R := \sigma _{1} \otimes \sigma _{2} \equiv \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix} \otimes \begin{pmatrix} 0 & -i \\ i & 0 \end{pmatrix} \equiv \begin{pmatrix} 0 & 0 & 0 & -i \\ 0 & 0 & i & 0 \\ 0 & -i & 0 & 0 \\ i & 0 & 0 & 0 \end{pmatrix}.

Apply the vec-operator on RR, i.e. vec⁡(R)\operatorname {vec}(R). Calculate the discrete Fourier transform of the vector in C4\mathbb {C}^{4}. Then apply vec⁡−1\operatorname {vec}^{-1} to this vector. Compare this 4×44 \times 4 matrix with the 4×44 \times 4 matrix RR. Discuss.

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Problem 13.2.6

Let AA be an n×nn \times n matrix over C\mathbb {C}. Find a matrix SnS_{n} such that Snvec⁡(A)=tr⁡(A)S_{n}\operatorname {vec}(A) = \operatorname {tr}(A). Prove the equivalence. Illustrate the answer with

A=(123312231) A = \begin{pmatrix} 1 & 2 & 3 \\ 3 & 1 & 2 \\ 2 & 3 & 1 \end{pmatrix}

where

Sn=vec⁡(I3)=(100010001)T S_{n} = \operatorname {vec}(I_{3}) = \begin{pmatrix} 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \end{pmatrix}^{T}

and

Snvec⁡(A)=(100010001)vec⁡(A)=3. S_{n}\operatorname {vec}(A) = \begin{pmatrix} 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \end{pmatrix}\operatorname {vec}(A) = 3.
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Problem 13.2.7
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(i)

Let AA, BB be n×nn \times n matrices over C\mathbb {C}. Show that

tr⁡(vec⁡(B)(vec⁡(A))T)=tr⁡(ATB) \operatorname {tr}(\operatorname {vec}(B)(\operatorname {vec}(A))^{T}) = \operatorname {tr}(A^{T}B)

applying tr⁡(vec⁡(B)(vec⁡(A))T)=tr⁡((vec⁡(In))T(I⊗ATB)vec⁡(I))\operatorname {tr}(\operatorname {vec}(B)(\operatorname {vec}(A))^{T}) = \operatorname {tr}((\operatorname {vec}(I_{n}))^{T}(I \otimes A^{T}B)\operatorname {vec}(I)).

(ii)

Let AA be an m×nm \times n matrix, BB be a p×pp \times p matrix, XX be a p×np \times n matrix and CC be an n×nn \times n matrix. The underlying field is R\mathbb {R}. Show that

tr⁡(AXTBXC)≡(vec⁡(X))T((CA)⊗BT)vec⁡(X). \operatorname {tr}(AX^{T}BXC) \equiv (\operatorname {vec}(X))^{T}((CA) \otimes B^{T})\operatorname {vec}(X).