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## Basics of Eigenvalues and Eigenvectors

Definition. Let $$A$$ be an $$n\times n$$ matrix. If $$A\overrightarrow{x}=\lambda \overrightarrow{x}$$ for some nonzero vector $$\overrightarrow{x}$$ and some scalar $$\lambda$$, then $$\lambda$$ is an eigenvalue of $$A$$ and $$\overrightarrow{x}$$ is an eigenvector of $$A$$ corresponding to $$\lambda$$.

Example. Consider $$A=\left[\begin{array}{rr}1&2\\0&3\end{array} \right],\;\lambda=3,\; \overrightarrow{v}=\left[\begin{array}{r}1\\1\end{array} \right],\; \overrightarrow{u}=\left[\begin{array}{r}-2\\1\end{array} \right]$$.
Since $$A\overrightarrow{v} =\left[\begin{array}{rr}1&2\\0&3\end{array} \right]\left[\begin{array}{r}1\\1\end{array} \right] =\left[\begin{array}{r}3\\3\end{array} \right] =3\left[\begin{array}{r}1\\1\end{array} \right] =\lambda\overrightarrow{v}$$, $$3$$ is an eigenvalue of $$A$$ and $$\overrightarrow{v}$$ is an eigenvector of $$A$$ corresponding to the eigenvalue $$3$$.
Since $$A\overrightarrow{u} =\left[\begin{array}{rr}1&2\\0&3\end{array} \right]\left[\begin{array}{r}-2\\1\end{array} \right] =\left[\begin{array}{r}0\\3\end{array} \right] \neq \lambda\left[\begin{array}{r}-2\\1\end{array} \right] =\lambda\overrightarrow{u}$$ for all scalars $$\lambda$$, $$\overrightarrow{u}$$ is not an eigenvector of $$A$$.

Remark. For a real matrix, an eigenvalue can be a complex number and an eigenvector can be a complex vector.

Example. Consider $$A=\left[\begin{array}{rr}0&1\\-1&0\end{array} \right]$$. Since $$\left[\begin{array}{rr}0&1\\-1&0\end{array} \right]\left[\begin{array}{r}1\\i\end{array} \right] =\left[\begin{array}{r}i\\-1\end{array} \right] =i\left[\begin{array}{r}1\\i\end{array} \right]$$, $$i$$ is an eigenvalue of $$A$$ and $$\left[\begin{array}{r}1\\i\end{array} \right]$$ is an eigenvector of $$A$$ corresponding to the eigenvalue $$i$$.

Remark. An eigenvector must be a nonzero vector by definition. So the following are equivalent:

1. $$\lambda$$ is an eigenvalue of $$A$$.

2. $$A\overrightarrow{x}=\lambda \overrightarrow{x}$$ for some nonzero vector $$\overrightarrow{x}$$.

3. $$(A-\lambda I)\overrightarrow{x}=\overrightarrow{0}$$ for some nonzero vector $$\overrightarrow{x}$$.

4. $$(A-\lambda I)\overrightarrow{x}=\overrightarrow{0}$$ has a nontrivial solution $$\overrightarrow{x}$$.

5. $$A-\lambda I$$ is not invertible (by IMT).

6. $$\det(A-\lambda I)=0$$.

Definition. $$\det(\lambda I-A)$$ is a polynomial of $$\lambda$$ and it is the characteristic polynomial of $$A$$. $$\det(\lambda I-A)=0$$ is the characteristic equation of $$A$$.

Remark. Since the roots of the characteristic polynomial are the eigenvalues of the $$n\times n$$ matrix $$A$$, $$A$$ has $$n$$ eigenvalues, not necessarily distinct.

Definition. The multiplicity of a root $$\lambda$$ in $$\det(\lambda I-A)$$ is the algebraic multiplicity of the eigenvalue $$\lambda$$ of $$A$$.

Remark. If $$\lambda$$ is an eigenvalue of $$A$$, then $$\operatorname{NS}(A-\lambda I)$$ is the union of $$\{\overrightarrow{0}\}$$ and the set of all eigenvectors of $$A$$ corresponding to the eigenvalue $$\lambda$$.

Definition. Suppose $$\lambda$$ is an eigenvalue of the matrix $$A$$. Then $\operatorname{NS}(A-\lambda I)=\{\overrightarrow{x}\;|\;(A-\lambda I)\overrightarrow{x}=\overrightarrow{0}\}$ is the eigenspace of $$A$$ corresponding to the eigenvalue $$\lambda$$ and $$\operatorname{dim}(\operatorname{NS}(A-\lambda I))$$ is the geometric multiplicity of the eigenvalue $$\lambda$$.

Example. Let $$A=\left[\begin{array}{rrr}3&0&0\\0&4&1\\0&-2&1\end{array} \right]$$.

1. Find the characteristic polynomial of $$A$$.

2. Find the eigenvalues of $$A$$ with their algebraic multiplicities.

3. Find the eigenspaces of $$A$$ and geometric multiplicities of the eigenvalues of $$A$$.

Solution. (a) The characteristic polynomial of $$A$$ is $\begin{eqnarray*} \det(\lambda I-A)&=&\left|\begin{array}{ccc}\lambda-3&0&0\\0&\lambda-4&-1\\0&2&\lambda-1\end{array} \right|\\ &=&(\lambda-3)\left|\begin{array}{cc}\lambda-4&-1\\2&\lambda-1\end{array} \right|-0+0\\ &=&(\lambda-3)(\lambda^2-5\lambda+6)\\ &=&(\lambda-3)(\lambda-3)(\lambda-2). \end{eqnarray*}$ (b) $$\det(\lambda I-A)=(\lambda-2)(\lambda-3)^2=0\implies \lambda=2,3,3$$. So $$2$$ and $$3$$ are eigenvalue of $$A$$ with algebraic multiplicities $$1$$ and $$2$$ respectively.

(c) The eigenspace of $$A$$ corresponding to the eigenvalue $$3$$ is $\operatorname{NS}(A-3I)=\{\overrightarrow{x}\;|\;(A-3I)\overrightarrow{x}=\overrightarrow{0}\}.$ $[A-3I\;|\;\overrightarrow{0}]=\left[\begin{array}{rrr|r}0&0&0&0\\0&1&1&0\\0&-2&-2&0\end{array} \right] \xrightarrow{2R_2+R_3} \left[\begin{array}{rrr|r}0&0&0&0\\0&1&1&0\\0&0&0&0\end{array} \right] \xrightarrow{R_1\leftrightarrow R_2} \left[\begin{array}{rrr|r}0&\boxed{1}&1&0\\0&0&0&0\\0&0&0&0\end{array} \right]$ So we get $$x_2+x_3=0$$ where $$x_1$$ and $$x_3$$ are free variable. Thus $\overrightarrow{x}=\left[\begin{array}{r}x_1\\x_2\\x_3 \end{array}\right] =\left[\begin{array}{r}x_1\\-x_3\\x_3 \end{array}\right] =x_1\left[\begin{array}{r}1\\0\\0\end{array}\right]+ x_3\left[\begin{array}{r}0\\-1\\1\end{array}\right] \in \operatorname{Span} \left\{\left[\begin{array}{r}1\\0\\0\end{array}\right],\; \left[\begin{array}{r}0\\-1\\1\end{array}\right]\right\}.$ Thus the eigenspace of $$A$$ corresponding to the eigenvalue $$3$$ is $\operatorname{NS}(A-3I)=\operatorname{Span} \left\{\left[\begin{array}{r}1\\0\\0\end{array}\right],\; \left[\begin{array}{r}0\\-1\\1\end{array}\right]\right\},$ and the geometric multiplicity of the eigenvalue $$3$$ is $$\operatorname{dim}(\operatorname{NS}(A-3I))=2$$.

The eigenspace of $$A$$ corresponding to the eigenvalue $$2$$ is $\operatorname{NS}(A-2I)=\{\overrightarrow{x}\;|\;(A-2I)\overrightarrow{x}=\overrightarrow{0}\}.$ $[A-2I\;|\;\overrightarrow{0}]=\left[\begin{array}{rrr|r}1&0&0&0\\0&2&1&0\\0&-2&-1&0\end{array} \right] \xrightarrow{R_2+R_3} \left[\begin{array}{rrr|r}1&0&0&0\\0&2&1&0\\0&0&0&0\end{array} \right] \xrightarrow{\frac{R_2}{2}} \left[\begin{array}{rrr|r}\boxed{1}&0&0&0\\0&\boxed{1}&\frac{1}{2}&0\\0&0&0&0\end{array} \right]$ So we get $$x_1=0,\; x_2+\frac{x_3}{2}=0$$ where $$x_3$$ is a free variable. Thus $\overrightarrow{x}=\left[\begin{array}{r}x_1\\x_2\\x_3 \end{array}\right] =\left[\begin{array}{r}0\\-\frac{x_3}{2}\\x_3 \end{array}\right] =\frac{x_3}{2}\left[\begin{array}{r}0\\-1\\2 \end{array}\right] \in \operatorname{Span} \left\{\left[\begin{array}{r}0\\-1\\2 \end{array}\right]\right\}.$ Thus the eigenspace of $$A$$ corresponding to the eigenvalue $$2$$ is $\operatorname{NS}(A-2I)=\operatorname{Span} \left\{\left[\begin{array}{r}0\\-1\\2 \end{array}\right]\right\},$ and the geometric multiplicity of the eigenvalue $$2$$ is $$\operatorname{dim}(\operatorname{NS}(A-2I))=1$$.

Remark. Recall that $$\overrightarrow{x}\mapsto A\overrightarrow{x}$$ is a linear transformation from $$\mathbb R^n$$ to $$\mathbb R^n$$. This linear transformation is invariant on the eigenspaces of $$A$$:
If $$\lambda$$ is an eigenvalue of $$A$$ and $$\overrightarrow{x}\in \operatorname{NS}(A-\lambda I)$$, then $$A\overrightarrow{x}\in \operatorname{NS}(A-\lambda I)$$.

Example. In the preceding example, $$\overrightarrow{x}=\left[\begin{array}{r}4\\-5\\5\end{array}\right] \in \operatorname{NS}(A-3I)=\operatorname{Span} \left\{\left[\begin{array}{r}1\\0\\0\end{array}\right],\; \left[\begin{array}{r}0\\-1\\1\end{array}\right]\right\},$$ and also $$A\overrightarrow{x}=A\left[\begin{array}{r}4\\-5\\5\end{array}\right] =\left[\begin{array}{r}12\\-15\\15\end{array}\right] =3\left[\begin{array}{r}4\\-5\\5\end{array}\right] \in \operatorname{NS}(A-3I)=\operatorname{Span} \left\{\left[\begin{array}{r}1\\0\\0\end{array}\right],\; \left[\begin{array}{r}0\\-1\\1\end{array}\right]\right\}$$.

Theorem.(IMT contd.) Let $$A$$ be an $$n\times n$$ matrix. Then the following are equivalent:

• (a) $$A$$ is invertible.

• (o) $$0$$ is not an eigenvalue of $$A$$.

$$0$$ is an eigenvalue of $$A$$ iff $$A\overrightarrow{x}=\overrightarrow{0}$$ has a nontrivial solution. By the IMT, $$A\overrightarrow{x}=\overrightarrow{0}$$ has a nontrivial solution iff $$A$$ is not invertible.

Some useful results:

Theorem. Let $$A$$ be an $$n\times n$$ matrix with eigenvalues $$\lambda_1,\lambda_2,\ldots,\lambda_n$$. Then $$\det A=\lambda_1\lambda_2\cdots\lambda_n.$$

Note that $$\det(\lambda I-A)=(\lambda-\lambda_1)(\lambda-\lambda_2)\cdots(\lambda-\lambda_n)$$. Plugging $$\lambda=0$$, we get $$(-1)^n\det A=(-1)^n\lambda_1\lambda_2\cdots\lambda_n \implies \det A=\lambda_1\lambda_2\cdots\lambda_n$$.

Theorem. The eigenvalues of a triangular matrix (e.g., diagonal matrix) are the entries on its main diagonal.

Consider an upper-triangular matrix $$A=\left[\begin{array}{rrrrr}d_1&&&&\\0&d_2&&*&\\0&0&d_3&&\\ \vdots&\vdots&&\ddots&\\0&0&0&\cdots&d_n\end{array} \right].$$ Its characteristic polynomial is $$\det(\lambda I-A)=(\lambda-d_1)(\lambda-d_2)\cdots(\lambda-d_n)$$. So $$\det(\lambda I-A)=0\implies \lambda=d_1,\ldots,d_n$$.

Theorem. Let $$A$$ be a square matrix. If $$\lambda$$ is an eigenvalue of $$A$$, then $$\lambda^k$$ is an eigenvalue of $$A^k$$.

Suppose $$A\overrightarrow{v}=\lambda \overrightarrow{v}$$, $$\overrightarrow{v}\neq \overrightarrow{0}$$. Then $$A(A\overrightarrow{v})=A(\lambda \overrightarrow{v})$$. So $A^2\overrightarrow{v}=\lambda (A\overrightarrow{v})=\lambda (\lambda \overrightarrow{v})=\lambda^2\overrightarrow{v}.$ Continuing this process, we get $$A^k\overrightarrow{v}=\lambda^k \overrightarrow{v}$$.

Theorem. Let $$A$$ be an invertible matrix. Then $$\lambda$$ is an eigenvalue of $$A$$ if and only if $$\frac{1}{\lambda}$$ is an eigenvalue of $$A^{-1}$$.

Suppose $$A\overrightarrow{v}=\lambda \overrightarrow{v}$$, $$\overrightarrow{v}\neq \overrightarrow{0}$$. Since $$A$$ is invertible, $$\lambda\neq 0$$. $\begin{eqnarray*} A^{-1}(A\overrightarrow{v})&=&A^{-1}(\lambda \overrightarrow{v})\\ I\overrightarrow{v}=\overrightarrow{v}&=&\lambda(A^{-1} \overrightarrow{v})\\ \frac{1}{\lambda}\overrightarrow{v}&=&A^{-1} \overrightarrow{v} \end{eqnarray*}$ So $$\frac{1}{\lambda}$$ is an eigenvalue of $$A^{-1}$$. The converse follows by a similar argument.

Theorem. Let $$A$$ be a square matrix. If $$\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}$$ are eigenvectors of $$A$$ corresponding to distinct eigenvalues $$\lambda_1,\lambda_2,\ldots,\lambda_k$$ of $$A$$ respectively, then $$\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}\}$$ is linearly independent.

Let $$\lambda_1,\lambda_2,\ldots,\lambda_k$$ be distinct and $$A\overrightarrow{v_i}=\lambda_i\overrightarrow{v_i},\;\overrightarrow{v_i}\neq\overrightarrow{0}$$ for $$i=1,\ldots,k$$. Suppose $$\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}\}$$ is linearly dependent. WLOG let $$\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_p}\}$$ be a maximal linearly independent subset of $$\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}\}$$ for some $$p < k$$. Then $$\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_p},\overrightarrow{v_{p+1}}\}$$ is linearly dependent and consequently $$$\overrightarrow{v_{p+1}}=c_1\overrightarrow{v_1}+\cdots+c_p\overrightarrow{v_p}, \tag{1}$$$ for some scalars $$c_1,\ldots,c_p$$, not all zero (since $$\overrightarrow{v_{p+1}}\neq \overrightarrow{0}$$). $\begin{eqnarray} A\overrightarrow{v_{p+1}}&=&A(c_1\overrightarrow{v_1}+\cdots+c_p\overrightarrow{v_p})\nonumber\\ \lambda_{p+1}\overrightarrow{v_{p+1}}&=&c_1A\overrightarrow{v_1}+\cdots+c_pA\overrightarrow{v_p}\nonumber\\ \lambda_{p+1}\overrightarrow{v_{p+1}}&=&c_1\lambda_1\overrightarrow{v_1}+\cdots+c_p\lambda_p\overrightarrow{v_p} \tag{2} \end{eqnarray}$ $$\lambda_{p+1}(1)-(2)$$ gives $$$\overrightarrow{0}=c_1(\lambda_{p+1}-\lambda_1)\overrightarrow{v_1}+\cdots+c_p(\lambda_{p+1}-\lambda_p)\overrightarrow{v_p} \tag{3}$$$ Since $$\lambda_{p+1}-\lambda_i\neq 0$$ for $$i=1,\ldots,p$$ and $$c_1,\ldots,c_p$$ are not all zero, $$c_1(\lambda_{p+1}-\lambda_1),\ldots,c_p(\lambda_{p+1}-\lambda_p)$$ are not all zero. So (3) implies $$\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_p}\}$$ is linearly dependent, a contradiction.

Remark. The converse of the preceding theorem is not true. Consider $$A=\left[\begin{array}{rrr}3&0&0\\0&4&1\\0&-2&1\end{array} \right]$$ in the last example. $$\left[\begin{array}{r}1\\0\\0\end{array}\right]$$ and $$\left[\begin{array}{r}0\\-1\\1\end{array}\right]$$ are linearly independent eigenvectors of $$A$$ and they are eigenvectors corresponding to the same eigenvalue $$3$$ of $$A$$.

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