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Let \(A\) be a real \(2 \times 2\) matrix with a complex eigenvalue \(\lambda = a - bi\)\(\left( {b \ne 0} \right)\) and an associated eigenvector \({\bf{v}}\) in \({\mathbb{}^2}\).

  1. Show that \(A({\mathop{\rm Re}\nolimits} {\bf{v}}) = a{\mathop{\rm Re}\nolimits} {\bf{v}} + b{\mathop{\rm Im}\nolimits} {\bf{v}}\) and \(A({\mathop{\rm Im}\nolimits} {\bf{v}}) = - b{\mathop{\rm Re}\nolimits} {\bf{v}} + a{\mathop{\rm Im}\nolimits} {\bf{v}}\). (Hint: Write \({\bf{v}} = {\mathop{\rm Re}\nolimits} {\bf{v}} + i{\mathop{\rm Im}\nolimits} {\bf{v}}\), and compute \(A{\bf{v}}\).)
  2. Verify that if \(P\) and \(C\) are given as in Theorem 9, then \(AP = PC\)

Short Answer

Expert verified
  1. \(A\left( {{\mathop{\rm Re}\nolimits} \left( {\bf{v}} \right)} \right) = {\mathop{\rm Re}\nolimits} \left( {A{\bf{v}}} \right) = a{\mathop{\rm Re}\nolimits} \left( {\bf{v}} \right) + b{\mathop{\rm Im}\nolimits} \left( {\bf{v}} \right)\)\(A\left( {{\mathop{\rm Im}\nolimits} \left( {\bf{v}} \right)} \right) = {\mathop{\rm Im}\nolimits} \left( {A{\bf{v}}} \right) = - b{\mathop{\rm Re}\nolimits} \left( {\bf{v}} \right) + aIm\left( {\bf{v}} \right)\)
  2. \(AP = \left( {A\left( {{\mathop{\rm Re}\nolimits} \left( {\bf{v}} \right)} \right)\,\,\,\,\,A\left( {{\mathop{\rm Im}\nolimits} \left( {\bf{v}} \right)} \right)} \right) = \left( {P\left( {\begin{aligned}{}a\\b\end{aligned}} \right)\;\;\;P\left( {\begin{aligned}{}{ - b}\\a\end{aligned}} \right)} \right) = P\left( {\begin{aligned}{}a&{}&{ - b}\\b&{}&a\end{aligned}} \right) = PC\)

Step by step solution

01

Formula of eigenvector 

Remember that an eigenvalue\(\lambda \)and an eigenvector\(x\)for a square matrix A satisfy the equation\(Ax = \lambda x\).

02

Proof

(a)

Given an eigenvector \(v\) and eigenvalue \(\lambda = a - bi\). Consider \(Ax = \lambda x\) and solve it as follows:

\(\begin{aligned}{}Av &= \lambda v\\ &= \left( {a - bi} \right)\left( {{\mathop{\rm Re}\nolimits} \left( v \right) + i{\mathop{\rm Im}\nolimits} \left( v \right)} \right)\\ &= \left( {a{\mathop{\rm Re}\nolimits} \left( v \right) + b{\mathop{\rm Im}\nolimits} \left( v \right)} \right) + i\left( {a{\mathop{\rm Im}\nolimits} \left( v \right) - b{\mathop{\rm Re}\nolimits} \left( v \right)} \right)\end{aligned}\)

So, from the above equation, it can be concluded that

\(\begin{aligned}{}A\left( {{\mathop{\rm Re}\nolimits} \left( v \right)} \right) = {\mathop{\rm Re}\nolimits} \left( {Av} \right) = a{\mathop{\rm Re}\nolimits} \left( v \right) + b{\mathop{\rm Im}\nolimits} \left( v \right)\\A\left( {{\mathop{\rm Re}\nolimits} \left( v \right)} \right) = {\mathop{\rm Im}\nolimits} \left( {Av} \right) = - b{\mathop{\rm Re}\nolimits} \left( v \right) + a{\mathop{\rm Im}\nolimits} \left( v \right)\end{aligned}\)

03

Verify that if P and C are given as in Theorem 9, then AP = PC. 

(b)

By theorem 9, let \(P = \left( {{\mathop{\rm Re}\nolimits} \left( v \right)\,\,\,\,\,\,\,\,{\mathop{\rm Im}\nolimits} \left( v \right)} \right)\)

From part (a),

\(\begin{aligned}{}A\left( {{\mathop{\rm Re}\nolimits} \left( v \right)} \right) &= P\left( {\begin{aligned}{}a\\b\end{aligned}} \right)\\A\left( {{\mathop{\rm Im}\nolimits} \left( v \right)} \right) &= P\left( {\begin{aligned}{}{ - b}\\a\end{aligned}} \right)\end{aligned}\)

Now, Solve AP as follows:

\(\begin{aligned}{}AP &= \left( {A\left( {{\mathop{\rm Re}\nolimits} \left( v \right)} \right)\,\,\,\,\,A\left( {{\mathop{\rm Im}\nolimits} \left( v \right)} \right)} \right)\\ &= \left( {P\left( {\begin{aligned}{}a\\b\end{aligned}} \right)\;\;\;P\left( {\begin{aligned}{}{ - b}\\a\end{aligned}} \right)} \right)\\ &= P\left( {\begin{aligned}{}a&{}&{ - b}\\b&{}&a\end{aligned}} \right)\\ &= PC\end{aligned}\)

Hence, it proved that \(AP = PC\).

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Most popular questions from this chapter

Let\(\varepsilon = \left\{ {{{\bf{e}}_1},{{\bf{e}}_2},{{\bf{e}}_3}} \right\}\) be the standard basis for \({\mathbb{R}^3}\),\(B = \left\{ {{{\bf{b}}_1},{{\bf{b}}_2},{{\bf{b}}_3}} \right\}\) be a basis for a vector space \(V\) and\(T:{\mathbb{R}^3} \to V\) be a linear transformation with the property that

\(T\left( {{x_1},{x_2},{x_3}} \right) = \left( {{x_3} - {x_2}} \right){{\bf{b}}_1} - \left( {{x_1} - {x_3}} \right){{\bf{b}}_2} + \left( {{x_1} - {x_2}} \right){{\bf{b}}_3}\)

  1. Compute\(T\left( {{{\bf{e}}_1}} \right)\), \(T\left( {{{\bf{e}}_2}} \right)\) and \(T\left( {{{\bf{e}}_3}} \right)\).
  2. Compute \({\left( {T\left( {{{\bf{e}}_1}} \right)} \right)_B}\), \({\left( {T\left( {{{\bf{e}}_2}} \right)} \right)_B}\) and \({\left( {T\left( {{{\bf{e}}_3}} \right)} \right)_B}\).
  3. Find the matrix for \(T\) relative to \(\varepsilon \), and\(B\).

For the matrices Afind real closed formulas for the trajectoryxโ†’(t+1)=Axโ†’(t)wherexโ†’(0)=[01]. Draw a rough sketchA=[1-31.2-2.6]

Question: Construct a random integer-valued \(4 \times 4\) matrix \(A\).

  1. Reduce \(A\) to echelon form \(U\) with no row scaling, and use \(U\) in formula (1) (before Example 2) to compute \(\det A\). (If \(A\) happens to be singular, start over with a new random matrix.)
  2. Compute the eigenvalues of \(A\) and the product of these eigenvalues (as accurately as possible).
  3. List the matrix \(A\), and, to four decimal places, list the pivots in \(U\) and the eigenvalues of \(A\). Compute \(\det A\) with your matrix program, and compare it with the products you found in (a) and (b).

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

2. \(\left[ {\begin{array}{*{20}{c}}5&3\\3&5\end{array}} \right]\)

For the matrix A, find real closed formulas for the trajectory xโ†’(t+1)=Axยฏ(t) where xโ†’=[01]. Draw a rough sketchA=[7-156-11]

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