Chapter 2: Q23Q (page 93)
If an \(n \times n\) matrix K cannot be row reduced to \({I_n}\), what can you say about the columns of K? Why?
Short Answer
The columns of K are linearly dependent and do not span \({\mathbb{R}^n}\).
Chapter 2: Q23Q (page 93)
If an \(n \times n\) matrix K cannot be row reduced to \({I_n}\), what can you say about the columns of K? Why?
The columns of K are linearly dependent and do not span \({\mathbb{R}^n}\).
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Get started for free(M) Read the documentation for your matrix program, and write the commands that will produce the following matrices (without keying in each entry of the matrix).
Suppose \(AD = {I_m}\) (the \(m \times m\) identity matrix). Show that for any b in \({\mathbb{R}^m}\), the equation \(A{\mathop{\rm x}\nolimits} = {\mathop{\rm b}\nolimits} \) has a solution. (Hint: Think about the equation \(AD{\mathop{\rm b}\nolimits} = {\mathop{\rm b}\nolimits} \).) Explain why Acannot have more rows than columns.
Show that block upper triangular matrix \(A\) in Example 5is invertible if and only if both \({A_{{\bf{11}}}}\) and \({A_{{\bf{12}}}}\) are invertible. [Hint: If \({A_{{\bf{11}}}}\) and \({A_{{\bf{12}}}}\) are invertible, the formula for \({A^{ - {\bf{1}}}}\) given in Example 5 actually works as the inverse of \(A\).] This fact about \(A\) is an important part of several computer algorithims that estimates eigenvalues of matrices. Eigenvalues are discussed in chapter 5.
When a deep space probe launched, corrections may be necessary to place the probe on a precisely calculated trajectory. Radio elementary provides a stream of vectors, \({{\bf{x}}_{\bf{1}}},....,{{\bf{x}}_k}\), giving information at different times about how the probe’s position compares with its planned trajectory. Let \({X_k}\) be the matrix \(\left[ {{x_{\bf{1}}}.....{x_k}} \right]\). The matrix \({G_k} = {X_k}X_k^T\) is computed as the radar data are analyzed. When \({x_{k + {\bf{1}}}}\) arrives, a new \({G_{k + {\bf{1}}}}\) must be computed. Since the data vector arrive at high speed, the computational burden could be serve. But partitioned matrix multiplication helps tremendously. Compute the column-row expansions of \({G_k}\) and \({G_{k + {\bf{1}}}}\) and describe what must be computed in order to update \({G_k}\) to \({G_{k + {\bf{1}}}}\).
Suppose A, B, and Care \(n \times n\) matrices with A, X, and \(A - AX\) invertible, and suppose
\({\left( {A - AX} \right)^{ - 1}} = {X^{ - 1}}B\) …(3)
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