Chapter 6: Problem 3
Let \\[ A=\left(\begin{array}{rrrr} 2 & -1 & 0 & 0 \\ -1 & 2 & -1 & 0 \\ 0 & -1 & 2 & -1 \\ 0 & 0 & -1 & 2 \end{array}\right) \\] (a) Compute the \(L U\) factorization of \(A\) (b) Explain why \(A\) must be positive definite.
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Chapter 6: Problem 3
Let \\[ A=\left(\begin{array}{rrrr} 2 & -1 & 0 & 0 \\ -1 & 2 & -1 & 0 \\ 0 & -1 & 2 & -1 \\ 0 & 0 & -1 & 2 \end{array}\right) \\] (a) Compute the \(L U\) factorization of \(A\) (b) Explain why \(A\) must be positive definite.
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Show that if \(A\) is a symmetric positive definite matrix, then \(A\) is nonsingular and \(A^{-1}\) is also positive definite.
We can show that, for an \(n \times n\) stochastic matrix, \(\lambda_{1}=1\) is an eigenvalue and the remaining eigenvalues must satisfy \\[ \left|\lambda_{j}\right| \leq 1 \quad j=2, \ldots, n \\] (See Exercise \(24 \text { of Chapter } 7, \text { Section } 4 .)\) Show that if \(A\) is an \(n \times n\) stochastic matrix with the property that \(A^{k}\) is a positive matrix for some positive integer \(k,\) then \\[ \left|\lambda_{j}\right|<1 \quad j=2, \ldots, n \\]
In each of the following, factor the matrix \(A\) into a product \(X D X^{-1},\) where \(D\) is diagonal: (a) \(A=\left(\begin{array}{ll}0 & 1 \\ 1 & 0\end{array}\right)\) (b) \(A=\left(\begin{array}{rr}5 & 6 \\ -2 & -2\end{array}\right)\) (c) \(A=\left(\begin{array}{ll}2 & -8 \\ 1 & -4\end{array}\right)\) (d) \(A=\left(\begin{array}{rrr}2 & 2 & 1 \\ 0 & 1 & 2 \\ 0 & 0 & -1\end{array}\right)\) (e) \(A=\left(\begin{array}{rrr}1 & 0 & 0 \\ -2 & 1 & 3 \\ 1 & 1 & -1\end{array}\right)\) (f) \(A=\left(\begin{array}{lll}1 & 2 & -1 \\ 2 & 4 & -2 \\ 3 & 6 & -3\end{array}\right)\)
Given that \\[ A=\left(\begin{array}{rrr} 4 & 0 & 0 \\ 0 & 1 & i \\ 0 & -i & 1 \end{array}\right) \\] find a matrix \(B\) such that \(B^{H} B=A\)
Let \(A\) be an \(n \times n\) symmetric positive definite \(\mathrm{ma}-\) trix. For each \(\mathbf{x}, \mathbf{y} \in \mathbb{R}^{n},\) define \\[ \langle\mathbf{x}, \mathbf{y}\rangle=\mathbf{x}^{T} A \mathbf{y} \\] Show that \(\langle,\rangle\) defines an inner product on \(\mathbb{R}^{n}\)
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