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In Exercies 7-12, assume the signals listed are solutions of the given difference equation. Determine if the signals form a basis for the solution space of the equation. Justify your answers using appropriate theorems.

\({{\bf{2}}^k}\), \({{\bf{4}}^k}\), \({\left( { - {\bf{5}}} \right)^k}\), \({y_{k + {\bf{3}}}} - {y_{k + {\bf{2}}}} - {\bf{22}}{y_{k + {\bf{1}}}} + {\bf{40}}{y_k} = {\bf{0}}\)

Short Answer

Expert verified

The dimension of H is 3, so the three linearly independent signals form a basis of H.

Step by step solution

01

Write the Casorati matrix

The Casorati matrix of the solution is

\({A_k} = \left[ {\begin{array}{*{20}{c}}{{2^k}}&{{4^k}}&{{{\left( { - 5} \right)}^k}}\\{{2^{k + 1}}}&{{4^{k + 1}}}&{{{\left( { - 5} \right)}^{k + 1}}}\\{{2^{k + 2}}}&{{4^{k + 2}}}&{{{\left( { - 5} \right)}^{k + 2}}}\end{array}} \right]\).

02

Check the Casorati matrix for \(k = {\bf{0}}\)

Substitute 0 for k in the Casorati matrix.

\(\begin{aligned}{A_0} &= \left[ {\begin{array}{*{20}{c}}{{2^0}}&{{4^0}}&{{{\left( { - 5} \right)}^0}}\\{{2^{0 + 1}}}&{{4^{0 + 1}}}&{{{\left( { - 5} \right)}^{0 + 1}}}\\{{2^{0 + 2}}}&{{4^{0 + 2}}}&{{{\left( { - 5} \right)}^{0 + 2}}}\end{array}} \right]\\ &= \left[ {\begin{array}{*{20}{c}}1&1&1\\2&4&{ - 5}\\4&{16}&{25}\end{array}} \right]\\ &\sim \left[ {\begin{array}{*{20}{c}}1&0&0\\0&1&0\\0&0&1\end{array}} \right]\end{aligned}\)

The Casorati matrix is row equivalent to theidentity matrix. Therefore, it is invertible.

Hence, the set of signals \(\left\{ {{1^k},\,{2^k},\;{{\left( { - 2} \right)}^k}} \right\}\) is linearly independent. The dimension of H is 3, so the three linearly independent signals form a basis of H.

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Most popular questions from this chapter

Let V be a vector space that contains a linearly independent set \(\left\{ {{u_{\bf{1}}},{u_{\bf{2}}},{u_{\bf{3}}},{u_{\bf{4}}}} \right\}\). Describe how to construct a set of vectors \(\left\{ {{v_{\bf{1}}},{v_{\bf{2}}},{v_{\bf{3}}},{v_{\bf{4}}}} \right\}\) in V such that \(\left\{ {{v_{\bf{1}}},{v_{\bf{3}}}} \right\}\) is a basis for Span\(\left\{ {{v_{\bf{1}}},{v_{\bf{2}}},{v_{\bf{3}}},{v_{\bf{4}}}} \right\}\).

Let \(A\) be an \(m \times n\) matrix of rank \(r > 0\) and let \(U\) be an echelon form of \(A\). Explain why there exists an invertible matrix \(E\) such that \(A = EU\), and use this factorization to write \(A\) as the sum of \(r\) rank 1 matrices. [Hint: See Theorem 10 in Section 2.4.]

In statistical theory, a common requirement is that a matrix be of full rank. That is, the rank should be as large as possible. Explain why an m n matrix with more rows than columns has full rank if and only if its columns are linearly independent.

Let \(A\) be any \(2 \times 3\) matrix such that \({\mathop{\rm rank}\nolimits} A = 1\), let u be the first column of \(A\), and suppose \({\mathop{\rm u}\nolimits} \ne 0\). Explain why there is a vector v in \({\mathbb{R}^3}\) such that \(A = {{\mathop{\rm uv}\nolimits} ^T}\). How could this construction be modified if the first column of \(A\) were zero?

Question: Exercises 12-17 develop properties of rank that are sometimes needed in applications. Assume the matrix \(A\) is \(m \times n\).

  1. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} A\). (Hint: Explain why every vector in the column space of \(AB\) is in the column space of \(A\).
  2. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} B\). (Hint: Use part (a) to study rank\({\left( {AB} \right)^T}\).)
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