## Example Problems on Linear Algebra

ST705 Homework 2 on Linear Algebra

# 1

Let $x, \mu_{1}, \mu_{2} \in \mathbb{R}^{p}$ and $\Sigma_{1}, \Sigma_{2} \in \mathbb{R}^{p\times p}$ invertible and symmetric. Derive expressions for $\widetilde{\mu} \in \mathbb{R}^{p}$, $\widetilde{\Sigma} \in \mathbb{R}^{p\times p}$, and $c \in \mathbb{R}$ that satisfy

$-(x - \mu_{1})'\Sigma_{1}^{-1}(x - \mu_{1}) - (x - \mu_{2})'\Sigma_{2}^{-1}(x - \mu_{2}) = -(x - \widetilde{\mu})'\widetilde{\Sigma}^{-1}(x - \widetilde{\mu}) + c,$

where $c$ does not depend on $x$.

We will take $\widetilde{\Sigma}^{-1} = \Sigma_{1}^{-1} + \Sigma_{2}^{-1}$ and $\widetilde{\mu} = \widetilde{\Sigma}(\Sigma_1^{-1} \mu_1 + \Sigma_2^{-1} \mu_2)$. Notice we are assuming that $\widetilde{\Sigma}^{-1}$ is invertible. We will start by expanding the left hand side of the equation are regrouping the terms.

\begin{align} \text{LHS} & = \\ & - \Big[ x^T \Sigma_{1}^{-1} x - x^T \Sigma_{1}^{-1} \mu_1 - \mu_1^T \Sigma_{1}^{-1} x + \mu_1^T \Sigma_{1}^{-1} \mu_1 + \\ & x^T \Sigma_{2}^{-1} x - x^T \Sigma_{2}^{-1} \mu_2 - \mu_2^T \Sigma_{2}^{-1} x + \mu_2^T \Sigma_{2}^{-1} \mu_2 \Big] \\ & = - \Big[ x^T \widetilde{\Sigma}^{-1} x - x^T (\Sigma_{1}^{-1} \mu_1 + \Sigma_{2}^{-1} \mu_2) - (\mu_1^T \Sigma_{1}^{-1} + \mu_2^T \Sigma_{2}^{-1}) x + c\Big] \end{align}

Constants will be added and removed to the $c$ term as needed. Let’s look at the second term. Again recall that we are assuming that $\widetilde{\Sigma}^{-1}$ is invertible.

\begin{align} x^T (\Sigma_{1}^{-1} \mu_1 + \Sigma_{2}^{-1} \mu_2) & = x^T \widetilde{\Sigma}^{-1} \widetilde{\Sigma} (\Sigma_{1}^{-1} \mu_1 + \Sigma_{2}^{-1} \mu_2) \\ & = x^T \widetilde{\Sigma}^{-1} \widetilde{\mu} \end{align}

We can do the same thing to the third term.

\begin{align} (\mu_1^T \Sigma_{1}^{-1} + \mu_2^T \Sigma_{2}^{-1}) x & = (\mu_1^T \Sigma_{1}^{-1} + \mu_2^T \Sigma_{2}^{-1}) \widetilde{\Sigma} \widetilde{\Sigma}^{-1} x \\ & = \widetilde{\mu} \widetilde{\Sigma}^{-1} x \end{align}

Finally, we can add and subtract $\widetilde{\mu} \widetilde{\Sigma}^{-1} \widetilde{\mu}$, keeping one of them in the $c$ term as it is independent of $x$. Thus,

\begin{align} LHS & = - \Big[ x^T \widetilde{\Sigma}^{-1} x - x^T (\Sigma_{1}^{-1} \mu_1 + \Sigma_{2}^{-1} \mu_2) - (\mu_1^T \Sigma_{1}^{-1} + \mu_2^T \Sigma_{2}^{-1}) x + c\Big] \\ & = - \Big[ x^T \widetilde{\Sigma}^{-1} x - x^T \widetilde{\Sigma}^{-1} \widetilde{\mu} - \widetilde{\mu} \widetilde{\Sigma}^{-1} x + \widetilde{\mu} \widetilde{\Sigma}^{-1} \widetilde{\mu} + c \Big] \\ & = -(x - \widetilde{\mu})'\widetilde{\Sigma}^{-1}(x - \widetilde{\mu}) + c \end{align}

# 2

Let $A$ be a positive definite matrix, and show that

$\text{tr}(I - A^{-1}) \le \log\det(A) \le \text{tr}(A - I).$

Take $A$ to be $n \times n$. Recall that if $A$ is a positive definite matrix that $A^{-1}$ is also positive definite, they both have positive eigenvalues, the trace of them is the sum of their eigenvalues, and the determinant of $A$ is the product of those eigenvalues. If $A$ has eigenvalues $\{ \lambda_1, \dots, \lambda_n\}$ then $A^{-1}$ has eigenvalues $\{ \frac{ 1 }{ \lambda_1 }, \dots, \frac{ 1 }{ \lambda_n } \}$.

$tr(I - A^{-1}) = tr(I) - tr(A^{-1}) = n - \sum_{ i=1 }^{ n }\frac{ 1 }{ \lambda_i }$ $\log(\det(A)) = \log(\prod_{ i=1 }^{ n } \lambda_i) = \sum_{ i=1 }^{ n } \log(\lambda_i)$ $tr(A - I) = tr(A) - tr(I) = \sum_{ i=1 }^{ n } (\lambda_i) - n = \sum_{ i=1 }^{ n } (\lambda_i - 1)$

Recall that $\log( x ) \leq x - 1 \ \forall x \geq 0$. This gives us $\log\det(A) \le \text{tr}(A - I)$. Then,

$\log( x ) \leq x - 1 \Rightarrow \log( 1/x ) \leq 1/x - 1 \Rightarrow \log(x) \geq 1 - \frac{ 1 }{ x }.$

This gives us $\text{tr}(I - A^{-1}) \le \log\det(A)$. Thus,

$\text{tr}(I - A^{-1}) \le \log\det(A) \le \text{tr}(A - I).$

# 3

Suppose you do not know that the rank of a matrix is equal to the number of nonzero eigenvalues. Show that the rank of a projection matrix is equal to its trace. First think about how to show this in the symmetric case, and then consider the more general case of a non-symmetric idempotent matrix.

We know that $A = A^T$ since $A$ is a projection matrix. Let’s first examine the symmetric case. We know that $A$ is diagonalizable.

$A = Q^T D Q$

Where $D$ is a matrix of eigenvalues. We know that these are all either 0 or 1. We also know that $Q$ is and orthogonal basis of eigenvectors, so $Q^TQ = I$. Thus,

$tr(A) = tr(Q^T D Q) = tr(Q^T Q D) = tr(D).$

The trace of $D$ is the sum of the eigenvalues of $A$. So, the trace of $A$ is the number of nonzero eigenvalues, say $k$. This is also the $\text{rank}(D)$. Since $A$ is symmetric, we also know that $\text{tr}( D ) = \text{tr}( A )$. Thus,

$\text{tr}( A ) = k = \text{rank}( D ) = \text{rank}( A ).$

Now let’s think about a general projection matrix $P \in \mathbb{R}^{n\times n}$. Assume that $\text{rank}( P ) = k$ and that $\{ u_1, \dots , u_{n-k} \}$ is a basis for the null space of $P$. Then,

$P u_i = 0 = 0 u_i.$

Since $u_i$ is a basis vector we know that $u_i \neq 0$. From this, we know that there must be $n-k$ 0 eigenvalues and thus, $k$ nonzero eigenvalues. Since $P$ is a projection matrix, we know that these nonzero eigenvalues are all 1. So,

$\text{tr}( P ) = \sum_{ i=0 }^{ n } \lambda_i = k.$

Earlier we defined $\text{rank}( P ) = k$, so $\text{rank}( P ) = k = \text{tr}( P )$.

# 4

Show that if $\text{rank}(BC) = \text{rank}(B)$, then $\text{column}(BC) = \text{column}(B)$, where column$(\cdot)$ denotes the column space.

Take

\begin{align} B : & \mathbb{R}^q \rightarrow \mathbb{R}^p \\ C : & \mathbb{R}^l \rightarrow \mathbb{R}^q. \end{align}

First we will show that $\text{ column }( BC ) \subset \text{ column }( B )$. We know that $\text{rank}(BC) = \text{rank}(B) = k$. This means that there exists a basis $\{u_1, \dots , u_k \} \subset \text{ column }( BC )$. Thus $\exists x_i \in \mathbb{R}^l$ such that

$u_i = BC x_i = B (C x_i ), \ \forall i \in \{1, \dots , k \}.$

Notice that $C x_i \in \mathbb{R}^q$. Since $u_i$ is a basis vector, we know that we can find its preimage and that it is unique from $u_j, \ i \neq j$. Thus, $u_i \in \text{ column }( B ) \ \forall i \in \{1, \dots , k \}$.

Now we will show $\text{ column }( B ) \subset \text{ column }( BC )$. Take $x \in \mathbb{R}^p$ such that $x = Bv$. Since $\text{rank}( B ) = \text{rank}( BC )$, we can say that $v = Cu$ for $u \in \mathbb{R}^l$. Therefore $x = Bv = BCu$, or $x \in C(BC)$.

# 5

The singular value decomposition of a matrix $A$ arises from the relationship of the eigenproblems of $A^TA$ and $AA^T$. The spectral decompositions of $A^T A$ and $AA^T$, both nonnegative definite with nonnegative eigenvalues, lead to the expressions

$A^T A = V \begin{bmatrix} \Lambda^2 & 0 \\ 0 & 0 \end{bmatrix} V^T \text{ and } AA^T = U \begin{bmatrix} \Lambda^2 & 0 \\ 0 & 0 \end{bmatrix} U^T$

where $U$ and $V$ are orthogonal matrices with eigenvectors as columns, and $\Lambda^2$ is the ($rxr$) diagonal matrix of nonzero eigenvalues with $\text{rank}(\Lambda) = \text{rank}( A ) = r$. The diagonal elements of $\Lambda$, square roots of eigenvalues of the inner and outer product matrices, are known as singular values of the matrix $A$. Note that the blocks of $0$ above are sized to fit the appropriate dimensions.

## a

Show that if $v^{(j)}$ is a column of $V$ and an eigenvector of $A^TA$, then $Av^{(j)}$ is an (unnormalized) eigenvector of $AA^T$.

Since $v^{(j)} = v$ is an eigenvector we know

$A^T A v = \lambda v.$

We can left multiply by $A$.

$A A^T A v = A \lambda v \rightarrow A A^T (Av) = \lambda (Av)$

Thus, $Av$ is an (unnormalized) eigenvector of $A A^T$.

## b

Show that $AV = U \Sigma$ where $\Sigma$ is some diagonal matrix.

Say $\text{rank}( A ) = p$. Then we can write

$AV = [Av_1 \ Av_2 \ \dots \ Av_p \ 0 \dots \ 0].$

Notice that

\begin{align} A A^T (A v_j ) & = \lambda_j A v_j \\ v_j^T A^T A A^T A v_j & = \lambda_j v_j^T A^T A v_j \\ ||A^T A v_j || ^2 & = \lambda_j ||Av_j||^2 \\ \lambda_j & = \frac{ ||A^T A v_j || ^2 }{ ||Av_j||^2 } \\ \lambda_j & = || A v_j ||^2 \\ \sqrt{ \lambda_j } & = || A v_j || \end{align}

We can use will divide each column in $AV$ by its corresponding eigenvalue, but we must also multiply it back in (in essence, we are multiplying by 1).

$AV = \underbrace{\Big[\frac{ A v_1 }{ \sqrt{ \lambda_1 } } \ \frac{ A v_2 }{ \sqrt{ \lambda_2 } } \ \dots \frac{ A v_p }{ \sqrt{ \lambda_p } } \ 0 \dots \ 0 \Big]}_{U} \cdot \underbrace{ \begin{bmatrix} \sqrt{ \lambda_1 } & & & & & \\ & \sqrt{ \lambda_2 } & & \\ & & \ddots & & & \\ & & & \sqrt{ \lambda_p } & & \\ & & & & 0 & \\ & & & & & \ddots \end{bmatrix} }_{\Sigma}$

Notice that all of the empty cells in $\Sigma$ are 0.

## c

Show that

$\Sigma = \begin{bmatrix} \Lambda & 0 \\ 0 & 0 \end{bmatrix}$

See the derivation in (b).

## d

Show that we can write the singular value decomposition as

$U^T A V = \begin{bmatrix} \Lambda & 0 \\ 0 & 0 \end{bmatrix}$

Remember that $U$ is orthogonal, so $U^T U = U U^T = I$.

\begin{align} AV & = U \Sigma \\ U^T A V & = U^T U \Sigma \\ U^T A V & = \Sigma \end{align}

## e

Show that the following is the Moore-Penrose generalized inverse for $A$:

$A^+ = V \begin{bmatrix} \Lambda^{-1} & 0 \\ 0 & 0 \end{bmatrix} U^T$

To show that $A^+$ is the generalized inverse of $A$, we must show that $AA^+A = A$, $A^+ A A^+ = A^+$, and that $A A^+$ and $A^+A$ are symmetric.

\begin{align} A A^+ A & = A \Big[ V \Sigma^{-1} U^T U \Sigma V^T \Big] \\ & = A \Big[ V \Sigma^{-1} I \Sigma V^T \Big] \\ & = A \Big[ V I V^T \Big] \\ & = A \Big[ I \Big] \\ & = A \end{align} \begin{align} A^+ A A^+ & = A^+ \Big[ U \Sigma V^T V \Sigma^{-1} U^T \Big] \\ & = A^+ \Big[ U \Sigma I \Sigma^{-1} U^T \Big] \\ & = A^+ \Big[ U I U^T \Big] \\ & = A^+ \Big[ I \Big] \\ & = A^+ \end{align}

Notice that from the derivations above, $A A^+ = I = A^+ A$. Thus, they are symmetric.

# 6

Let $A$, $B$, $C$, and $D$ be real valued matrices of dimension $p\times p$, $p\times q$, $q\times p$, and $q\times q$, respectively. Show that if $D$ is invertible, then

$\det \begin{pmatrix} A & B \\ C & D \\ \end{pmatrix} = \det(D) \cdot \det(A - BD^{-1}C).$

Recall that if $X, Y \in \mathbb{R}^{z \times z}$ then $\det(X Y) = \det(X) \det(Y)$. Also recall that the multiplication of two block matrices (of appropriate dimensions) looks like

$\begin{pmatrix} A_1 & B_1 \\ C_1 & D_1 \\ \end{pmatrix} \begin{pmatrix} A_2 & B_2 \\ C_2 & D_2 \\ \end{pmatrix} = \begin{pmatrix} A_1 A_2 + B_1 C_2 & A_1 B_2 + B_1 D_2 \\ C_1 A_2 + D_1 C_2 & C_1 B_2 + D_1 D_2 \\ \end{pmatrix}.$

Now say

$M = \begin{pmatrix} A & B \\ C & D \\ \end{pmatrix}.$

Notice that $M$ has dimensions

$\begin{pmatrix} p \times p & p \times q \\ q \times p & q \times q \\ \end{pmatrix} = (p+q) \times (p+q).$

Now take

$L = \begin{pmatrix} I_p & 0 \\ -D^{-1}C & I_q \\ \end{pmatrix}.$

Notice that $L$ also has dimensions $(p+q) \times (p+q)$. Also notice that $L$ is a lower triangular matrix; thus, its determinant is the product of the diagonal entries, which are all 1. So, $\det(L) = 1$. Thus,

\begin{align} M L & = \begin{pmatrix} A & B \\ C & D \\ \end{pmatrix} \begin{pmatrix} I_p & 0 \\ -D^{-1}C & I_q \\ \end{pmatrix} \\ & = \begin{pmatrix} A I_p + B(-D^{-1} C) & A 0 + B I_q \\ C I_p + D(-D^{-1} C) & C 0 + D I_q \\ \end{pmatrix} \\ & = \begin{pmatrix} A - B D^{-1} C & B \\ 0 & D \\ \end{pmatrix}. \end{align}

Now we can take the determinant. Notice that $\det(ML) = \det(M)\det(L) = \det(M) 1 = \det(M)$. Thus,

\begin{align} \det(M) & = \det \begin{pmatrix} A - B D^{-1} C & B \\ 0 & D \\ \end{pmatrix} \\ & = \det(D) \det(A - B D^{-1} C) - \det(B ) 0 \\ & = \det(D) \det(A - B D^{-1} C) \end{align}