# Sequence Converges to the Largest Eigenvalue of a Matrix

## Problem 403

Let $A$ be an $n\times n$ matrix. Suppose that $A$ has real eigenvalues $\lambda_1, \lambda_2, \dots, \lambda_n$ with corresponding eigenvectors $\mathbf{u}_1, \mathbf{u}_2, \dots, \mathbf{u}_n$.
Furthermore, suppose that
$|\lambda_1| > |\lambda_2| \geq \cdots \geq |\lambda_n|.$ Let
$\mathbf{x}_0=c_1\mathbf{u}_1+c_2\mathbf{u}_2+\cdots+c_n\mathbf{u}_n$ for some real numbers $c_1, c_2, \dots, c_n$ and $c_1\neq 0$.

Define
$\mathbf{x}_{k+1}=A\mathbf{x}_k \text{ for } k=0, 1, 2,\dots$ and let
$\beta_k=\frac{\mathbf{x}_k\cdot \mathbf{x}_{k+1}}{\mathbf{x}_k \cdot \mathbf{x}_k}=\frac{\mathbf{x}_k^{\trans} \mathbf{x}_{k+1}}{\mathbf{x}_k^{\trans} \mathbf{x}_k}.$

Prove that
$\lim_{k\to \infty} \beta_k=\lambda_1.$

## Proof.

We have
\begin{align*}
x_k=A\mathbf{x}_{k-1}=A^2\mathbf{x}_{k-2}=\cdots=A^k\mathbf{x}_0
\end{align*}
by applying the definition of $\mathbf{x}_k$ successively.
Then we have
\begin{align*}
\mathbf{x}_k&=A^k\mathbf{x}_0\\
&=A^k(c_1\mathbf{u}_1+c_2\mathbf{u}_2+\cdots+c_n\mathbf{u}_n)\\
&=c_1A^k\mathbf{u}_1+c_2A^k\mathbf{u}_2+\cdots+c_nA^k\mathbf{u}_n\\
&=c_1\lambda_1^k\mathbf{u}_1+c_2\lambda_2^k\mathbf{u}_2+\cdots+c_n\lambda_n^k\mathbf{u}_n,
\end{align*}
where the last step follows from the equalities $A^k\mathbf{u}_i=\lambda_i^k \mathbf{u}_i$, which in turn follows from the definitions of eigenvalues and eigenvectors $A\mathbf{u}_i=\lambda_i\mathbf{u}_i$.
Using the sum notation, we simply write it as
$\mathbf{x}_k=\sum_{i=1}^nc_i\lambda_i^k\mathbf{u}_i.$ This formula is true for any nonnegative integer $k$.

Let us next compute the dot product $\mathbf{x}_k\cdot \mathbf{x}_k$. We have
\begin{align*}
\mathbf{x}_k\cdot \mathbf{x}_k&=\left(\, \sum_{i=1}^nc_i\lambda_i^k\mathbf{u}_i \,\right)\cdot \left(\, \sum_{j=1}^nc_j\lambda_j^k\mathbf{u}_j \,\right)\\
&=\sum_{i=1}^n\sum_{j=1}^nc_ic_j\lambda_i^k\lambda_j^k\mathbf{u}_i \cdot \mathbf{u}_j\\
&=\lambda_1^{2k}\sum_{i=1}^n\sum_{j=1}^nc_ic_j \left(\, \frac{\lambda_i}{\lambda_1} \,\right)^k\left(\, \frac{\lambda_j}{\lambda_1} \,\right)^k\mathbf{u}_i \cdot \mathbf{u}_j.
\end{align*}
Since $\lambda_1$ is strictly larger than any other eigenvalues, we have
\begin{align*}
\lim_{k \to \infty} \left(\, \frac{\lambda_i}{\lambda_1} \,\right)^k
=\begin{cases} 1 & \text{ if } i=1\\
0 & \text{ if } i=2, 3,\dots, n.
\end{cases}
\end{align*}
It follows that
\begin{align*}
\lim_{k \to \infty}\mathbf{x}_k\cdot \mathbf{x}_k=\lambda_1^{2k}c_1^2 \mathbf{u}_1\cdot \mathbf{u}_1.
\end{align*}

Similarly, we compute
\begin{align*}
\mathbf{x}_k\cdot \mathbf{x}_{k+1}&=\left(\, \sum_{i=1}^nc_i\lambda_i^k\mathbf{u}_i \,\right)\cdot \left(\, \sum_{j=1}^nc_j\lambda_j^{k+1}\mathbf{u}_j \,\right)\\
&=\sum_{i=1}^n\sum_{j=1}^nc_ic_j\lambda_i^k\lambda_j^{k+1}\mathbf{u}_i \cdot \mathbf{u}_j\\
&=\lambda_1^{2k+1}\sum_{i=1}^n\sum_{j=1}^nc_ic_j \left(\, \frac{\lambda_i}{\lambda_1} \,\right)^k\left(\, \frac{\lambda_j}{\lambda_1} \,\right)^{k+1}\mathbf{u}_i \cdot \mathbf{u}_j.
\end{align*}

Thus we obtain
\begin{align*}
\lim_{k\to \infty} \mathbf{x}_k\cdot \mathbf{x}_{k+1}=\lambda_1^{2k+1}c_1^2\mathbf{u}_1\cdot \mathbf{u}_1.
\end{align*}

Combining these computations, we have
\begin{align*}
\lim_{k\to \infty}\beta_k&=\frac{\lim_{k\to \infty}\mathbf{x}_k\cdot \mathbf{x}_{k+1}}{\lim_{k\to \infty}\mathbf{x}_k \cdot \mathbf{x}_k}\6pt] &=\frac{\lambda_1^{2k+1}c_1^2 \mathbf{u}_1\cdot \mathbf{u}_1}{\lambda_1^{2k}c_1^2\mathbf{u}_1\cdot \mathbf{u}_1}\\[6pt] &=\lambda_1 \end{align*} as required. This completes the proof. Sponsored Links ### More from my site • Eigenvalues of Real Skew-Symmetric Matrix are Zero or Purely Imaginary and the Rank is Even Let A be a real skew-symmetric matrix, that is, A^{\trans}=-A. Then prove the following statements. (a) Each eigenvalue of the real skew-symmetric matrix A is either 0 or a purely imaginary number. (b) The rank of A is even. Proof. (a) Each […] • Orthogonality of Eigenvectors of a Symmetric Matrix Corresponding to Distinct Eigenvalues Suppose that a real symmetric matrix A has two distinct eigenvalues \alpha and \beta. Show that any eigenvector corresponding to \alpha is orthogonal to any eigenvector corresponding to \beta. (Nagoya University, Linear Algebra Final Exam Problem) Hint. Two […] • Unit Vectors and Idempotent Matrices A square matrix A is called idempotent if A^2=A. (a) Let \mathbf{u} be a vector in \R^n with length 1. Define the matrix P to be P=\mathbf{u}\mathbf{u}^{\trans}. Prove that P is an idempotent matrix. (b) Suppose that \mathbf{u} and \mathbf{v} be […] • Eigenvalues of a Hermitian Matrix are Real Numbers Show that eigenvalues of a Hermitian matrix A are real numbers. (The Ohio State University Linear Algebra Exam Problem) We give two proofs. These two proofs are essentially the same. The second proof is a bit simpler and concise compared to the first one. […] • There is at Least One Real Eigenvalue of an Odd Real Matrix Let n be an odd integer and let A be an n\times n real matrix. Prove that the matrix A has at least one real eigenvalue. We give two proofs. Proof 1. Let p(t)=\det(A-tI) be the characteristic polynomial of the matrix A. It is a degree n […] • Find the Limit of a Matrix Let \[A=\begin{bmatrix} \frac{1}{7} & \frac{3}{7} & \frac{3}{7} \\ \frac{3}{7} &\frac{1}{7} &\frac{3}{7} \\ \frac{3}{7} & \frac{3}{7} & \frac{1}{7} \end{bmatrix} be $3 \times 3$ matrix. Find $\lim_{n \to \infty} A^n.$ (Nagoya University Linear […]
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