Chapter 6: Problem 10
Prove that a \(2 \times 2\) matrix \(A\) is reducible if and only if \(a_{12} a_{21}=0\)
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Chapter 6: Problem 10
Prove that a \(2 \times 2\) matrix \(A\) is reducible if and only if \(a_{12} a_{21}=0\)
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Let \(A\) be an \(n \times n\) matrix with positive real eigenvalues \(\lambda_{1}>\lambda_{2}>\cdots>\lambda_{n} .\) Let \(\mathbf{x}_{i}\) be an eigenvector belonging to \(\lambda_{i}\) for each \(i,\) and let \(\mathbf{x}=\) \(\alpha_{1} \mathbf{x}_{1}+\cdots+\alpha_{n} \mathbf{x}_{n}\) (a) Show that \(A^{m} \mathbf{x}=\sum_{i=1}^{n} \alpha_{i} \lambda_{i}^{m} \mathbf{x}_{i}\) (b) Show that if \(\lambda_{1}=1,\) then \(\lim _{m \rightarrow \infty} A^{m} \mathbf{x}=\alpha_{1} \mathbf{x}_{1}\)
For each of the following functions, determine whether the given stationary point corresponds to a local minimum, local maximum, or saddle point: (a) \(f(x, y)=3 x^{2}-x y+y^{2} \quad(0,0)\) (b) \(f(x, y)=\sin x+y^{3}+3 x y+2 x-3 y \quad(0,-1)\) (c) \(f(x, y)=\frac{1}{3} x^{3}-\frac{1}{3} y^{3}+3 x y+2 x-2 y \quad(1,-1)\) (d) \(f(x, y)=\frac{y}{x^{2}}+\frac{x}{y^{2}}+x y \quad(1,1)\) (e) \(f(x, y, z)=x^{3}+x y z+y^{2}-3 x \quad(1,0,0)\) (f) \(f(x, y, z)=-\frac{1}{4}\left(x^{-4}+y^{-4}+z^{-4}\right)+y z-x-\) \(2 y-2 z \quad(1,1,1)\)
Solve the initial value problem $$\begin{array}{c} y_{1}^{\prime \prime}=-2 y_{2}+y_{1}^{\prime}+2 y_{2}^{\prime} \\ y_{2}^{\prime \prime}=2 y_{1}+2 y_{1}^{\prime}-y_{2}^{\prime} \\ y_{1}(0)=1, y_{2}(0)=0, y_{1}^{\prime}(0)=-3, y_{2}^{\prime}(0)=2 \end{array}$$
Let \(A\) be a symmetric positive definite matrix. Show that the diagonal elements of \(A\) must all be positive.
Let \(A\) be an \(m \times n\) matrix of rank \(n\) with singular value decomposition \(U \Sigma V^{T}\). Let \(\Sigma^{+}\) denote the \(n \times m\) matrix $$\left(\begin{array}{ccccc} \frac{1}{\sigma_{1}} & & & \\ & \frac{1}{\sigma_{2}} & & \\ & & \ddots & \\ & & & \frac{1}{\sigma_{n}} \end{array}\right)$$ and define \(A^{+}=V \Sigma^{+} U^{T} .\) Show that \(\hat{\mathbf{x}}=A^{+} \mathbf{b}\) satisfies the normal equations \(A^{T} A \mathbf{x}=A^{T} \mathbf{b}\)
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