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Question: Describe how might try to build a solution of a difference equation \({{\rm{x}}_{k + 1}} = A{{\rm{x}}_k}\,\,\,\,\left( {k = 0,1,2,...} \right)\) if you were given the initial \({{\rm{x}}_0}\) and this vector did not happen to be an eigenvector of A.

Short Answer

Expert verified

The solution of a difference equation \(A{x_k} = {x_{k + 1}}\) can be built by using the linear combination of eigenvectors.

Step by step solution

01

Write \({{\rm{x}}_0}\) as a linear combination of eigenvectors

Here, \({x_0}\) can be written as a linear combination of eigenvectors as follows:

\({x_0} = {c_1}{v_1} + ... + {c_p}{v_p}\)

02

Computation of \(A{x_k}\)

Hence, we can define the recursive description of the sequence \(\left\{ {{x_k}} \right\}\), using the above definition, as

\({x_k} = {c_1}{\lambda ^k}{v_1} + ... + {c_p}{\lambda ^k}{v_p}\) for \(k = 0,1,2,...\)

Replace \(k\) by \(k + 1\) in the above equation to get:

\({x_{k + 1}} = {c_1}{\lambda ^{k + 1}}{v_1} + ... + {c_p}{\lambda ^{k + 1}}{v_p}\)

Now, compute \(A{x_k}\) as follows:

\(\begin{array}{c}A{x_k} = A\left( {{c_1}{\lambda ^k}{v_1} + ... + {c_p}{\lambda ^k}{v_p}} \right)\\ = {c_1}{\lambda ^k}A{v_1} + ... + {c_p}{\lambda ^k}A{v_p}\,\,\,{\rm{(using linearity property)}}\\ = {c_1}{\lambda ^k}\lambda {v_1} + ... + {c_p}{\lambda ^k}\lambda {v_p}\,\,({\rm{using definition of eigenvectors}})\\ = {c_1}{\lambda ^{k + 1}}{v_1} + ... + {c_p}{\lambda ^{k + 1}}{v_p}\\ = {x_{k + 1}}\end{array}\)

Hence, \(A{x_k} = {x_{k + 1}}\).

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Most popular questions from this chapter

Question: Find the characteristic polynomial and the eigenvalues of the matrices in Exercises 1-8.

  1. \(\left[ {\begin{array}{*{20}{c}}2&7\\7&2\end{array}} \right]\)

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{array}{*{20}{c}}{ - 1}\\2\end{array}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{array}{*{20}{c}}4\\6\end{array}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{array}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{array}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{array}{*{20}{c}}6\\{ - 2}\\3\end{array}} \right)\)

3. \(\frac{1}{{{\mathop{\rm w}\nolimits} \cdot {\mathop{\rm w}\nolimits} }}{\mathop{\rm w}\nolimits} \)

Show that \(I - A\) is invertible when all the eigenvalues of \(A\) are less than 1 in magnitude. (Hint: What would be true if \(I - A\) were not invertible?)

Consider an invertible n × n matrix A such that the zero state is a stable equilibrium of the dynamical system x(t+1)=Ax(t)What can you say about the stability of the systems

x(t+1)=-Ax(t)

Mark each statement as True or False. Justify each answer.

a. If \(A\) is invertible and 1 is an eigenvalue for \(A\), then \(1\) is also an eigenvalue of \({A^{ - 1}}\)

b. If \(A\) is row equivalent to the identity matrix \(I\), then \(A\) is diagonalizable.

c. If \(A\) contains a row or column of zeros, then 0 is an eigenvalue of \(A\)

d. Each eigenvalue of \(A\) is also an eigenvalue of \({A^2}\).

e. Each eigenvector of \(A\) is also an eigenvector of \({A^2}\)

f. Each eigenvector of an invertible matrix \(A\) is also an eigenvector of \({A^{ - 1}}\)

g. Eigenvalues must be nonzero scalars.

h. Eigenvectors must be nonzero vectors.

i. Two eigenvectors corresponding to the same eigenvalue are always linearly dependent.

j. Similar matrices always have exactly the same eigenvalues.

k. Similar matrices always have exactly the same eigenvectors.

I. The sum of two eigenvectors of a matrix \(A\) is also an eigenvector of \(A\).

m. The eigenvalues of an upper triangular matrix \(A\) are exactly the nonzero entries on the diagonal of \(A\).

n. The matrices \(A\) and \({A^T}\) have the same eigenvalues, counting multiplicities.

o. If a \(5 \times 5\) matrix \(A\) has fewer than 5 distinct eigenvalues, then \(A\) is not diagonalizable.

p. There exists a \(2 \times 2\) matrix that has no eigenvectors in \({A^2}\)

q. If \(A\) is diagonalizable, then the columns of \(A\) are linearly independent.

r. A nonzero vector cannot correspond to two different eigenvalues of \(A\).

s. A (square) matrix \(A\) is invertible if and only if there is a coordinate system in which the transformation \({\bf{x}} \mapsto A{\bf{x}}\) is represented by a diagonal matrix.

t. If each vector \({{\bf{e}}_j}\) in the standard basis for \({A^n}\) is an eigenvector of \(A\), then \(A\) is a diagonal matrix.

u. If \(A\) is similar to a diagonalizable matrix \(B\), then \(A\) is also diagonalizable.

v. If \(A\) and \(B\) are invertible \(n \times n\) matrices, then \(AB\)is similar to \ (BA\ )

w. An \(n \times n\) matrix with \(n\) linearly independent eigenvectors is invertible.

x. If \(A\) is an \(n \times n\) diagonalizable matrix, then each vector in \({A^n}\) can be written as a linear combination of eigenvectors of \(A\).

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