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Let \(A\) be a real \(n \times n\) matrix, and let \(x\) be a vector in \({\mathbb{C}^n}\). Show that \({\mathop{\rm Re}\nolimits} \left( {Ax} \right) = A\left( {{\mathop{\rm Re}\nolimits} x} \right)\) and \({\mathop{\rm Im}\nolimits} \left( {Ax} \right) = A\left( {{\mathop{\rm Im}\nolimits} x} \right)\).

Short Answer

Expert verified

Since \({\bf{x}}\) is some nonzero vector in \({\bf{C}}\), so we can write it as \({\bf{x}} = {\mathop{\rm Re}\nolimits} {\bf{x}} + i \cdot {\mathop{\rm lm}\nolimits} {\bf{x}}\).

Spiting it in real and imaginary parts, although both \(\{ Re\} {\bf{x}}\) and \(\{ Im\} {\bf{x}}\) are real.

Step by step solution

01

Define Non zero vector

A vector with a magnitude not equal to zero is nonzero vector.

02

Prove the condition

Since \({\bf{x}}\) is some nonzero vector in \({\bf{C}}\) , so we can write it as \({\bf{x}} = {\mathop{\rm Re}\nolimits} {\bf{x}} + i \cdot {\mathop{\rm lm}\nolimits} {\bf{x}}\).

Splitting it in real and imaginary parts, although both \(\{ Re\} {\bf{x}}\) and \(\{ Im\} {\bf{x}}\) are real.

Now we have:

\(\begin{aligned}{}A{\bf{x}} &= A({\mathop{\rm Re}\nolimits} {\bf{x}} + i \cdot {\mathop{\rm lm}\nolimits} {\bf{x}})\\ &= A({\mathop{\rm Re}\nolimits} {\bf{x}}) + i \cdot A({\mathop{\rm lm}\nolimits} {\bf{x}})\end{aligned}\)

Since \(A\) is real. Then we have \(A({\mathop{\rm Re}\nolimits} {\bf{x}})\) and \(A({\mathop{\rm Im}\nolimits} {\bf{x}})\) both real, and hence

\({\mathop{\rm Re}\nolimits} (A{\bf{x}}) = A({\mathop{\rm Re}\nolimits} {\bf{x}}){\rm{ and }}{\mathop{\rm Im}\nolimits} (A{\bf{x}}) = A({\mathop{\rm lm}\nolimits} {\bf{x}})\).

We could think of \({\mathop{\rm Re}\nolimits} (A{\bf{x}})\) as taking the real part of \(A{\bf{x}}\).

Since,

\(\begin{aligned}{}Ax &= A\left( {{\mathop{\rm Re}\nolimits} {\bf{x}} + i \cdot {\mathop{\rm lm}\nolimits} {\bf{x}}} \right)\\ &= A\left( {{\mathop{\rm Re}\nolimits} {\bf{x}}} \right) + i \cdot A\left( {{\mathop{\rm Im}\nolimits} {\bf{x}}} \right)\end{aligned}\)

So, from the above, it is obvious that \({\mathop{\rm Re}\nolimits} \left( {A{\bf{x}}} \right) = A({\mathop{\rm Re}\nolimits} {\bf{x}})\).

The same goes for the imaginary part of \(A \to {\mathop{\rm lm}\nolimits} \left( {A{\bf{x}}} \right) = A\left( {{\mathop{\rm lm}\nolimits} {\bf{x}}} \right)\)

Since \({\bf{x}}\) is some nonzero vector in \({\bf{C}}\) we can write it as \({\bf{x}} = {\mathop{\rm Re}\nolimits} {\bf{x}} + i \cdot {\mathop{\rm lm}\nolimits} {\bf{x}}\).

Splitting it in real and imaginary part, although both \(\{ Re\} {\bf{x}}\) and \(\{ Im\} {\bf{x}}\) are real.

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Most popular questions from this chapter

Apply the results of Exercise \({\bf{15}}\) to find the eigenvalues of the matrices \(\left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{2}}&{\bf{2}}\\{\bf{2}}&{\bf{1}}&{\bf{2}}\\{\bf{2}}&{\bf{2}}&{\bf{1}}\end{aligned}} \right)\) and \(\left( {\begin{aligned}{*{20}{c}}{\bf{7}}&{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{3}}\\{\bf{3}}&{\bf{7}}&{\bf{3}}&{\bf{3}}&{\bf{3}}\\{\bf{3}}&{\bf{3}}&{\bf{7}}&{\bf{3}}&{\bf{3}}\\{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{7}}&{\bf{3}}\\{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{7}}\end{aligned}} \right)\).

Define\(T:{{\rm P}_3} \to {\mathbb{R}^4}\)by\(T\left( {\bf{p}} \right) = \left( {\begin{aligned}{{\bf{p}}\left( { - 3} \right)}\\{{\bf{p}}\left( { - 1} \right)}\\{{\bf{p}}\left( 1 \right)}\\{{\bf{p}}\left( 3 \right)}\end{aligned}} \right)\).

  1. Show that \(T\) is a linear transformation.
  2. Find the matrix for \(T\) relative to the basis \(\left\{ {1,t,{t^2},{t^3}} \right\}\)for \({{\rm P}_3}\)and the standard basis for \({\mathbb{R}^4}\).

Question: Diagonalize the matrices in Exercises \({\bf{7--20}}\), if possible. The eigenvalues for Exercises \({\bf{11--16}}\) are as follows:\(\left( {{\bf{11}}} \right)\lambda {\bf{ = 1,2,3}}\); \(\left( {{\bf{12}}} \right)\lambda {\bf{ = 2,8}}\); \(\left( {{\bf{13}}} \right)\lambda {\bf{ = 5,1}}\); \(\left( {{\bf{14}}} \right)\lambda {\bf{ = 5,4}}\); \(\left( {{\bf{15}}} \right)\lambda {\bf{ = 3,1}}\); \(\left( {{\bf{16}}} \right)\lambda {\bf{ = 2,1}}\). For exercise \({\bf{18}}\), one eigenvalue is \(\lambda {\bf{ = 5}}\) and one eigenvector is \(\left( {{\bf{ - 2,}}\;{\bf{1,}}\;{\bf{2}}} \right)\).

15. \(\left( {\begin{array}{*{20}{c}}{\bf{7}}&{\bf{4}}&{{\bf{16}}}\\{\bf{2}}&{\bf{5}}&{\bf{8}}\\{{\bf{ - 2}}}&{{\bf{ - 2}}}&{{\bf{ - 5}}}\end{array}} \right)\)

Define \(T:{{\rm P}_2} \to {\mathbb{R}^3}\) by \(T\left( {\bf{p}} \right) = \left( {\begin{aligned}{{\bf{p}}\left( { - 1} \right)}\\{{\bf{p}}\left( 0 \right)}\\{{\bf{p}}\left( 1 \right)}\end{aligned}} \right)\).

  1. Find the image under\(T\)of\({\bf{p}}\left( t \right) = 5 + 3t\).
  2. Show that \(T\) is a linear transformation.
  3. Find the matrix for \(T\) relative to the basis \(\left\{ {1,t,{t^2}} \right\}\)for \({{\rm P}_2}\)and the standard basis for \({\mathbb{R}^3}\).

Let \(A{\bf{ = }}\left( {\begin{aligned}{*{20}{c}}{{a_{{\bf{11}}}}}&{{a_{{\bf{12}}}}}\\{{a_{{\bf{21}}}}}&{{a_{{\bf{22}}}}}\end{aligned}} \right)\). Recall from Exercise \({\bf{25}}\) in Section \({\bf{5}}{\bf{.4}}\) that \({\rm{tr}}\;A\) (the trace of \(A\)) is the sum of the diagonal entries in \(A\). Show that the characteristic polynomial of \(A\) is \({\lambda ^2} - \left( {{\rm{tr}}A} \right)\lambda + \det A\). Then show that the eigenvalues of a \({\bf{2 \times 2}}\) matrix \(A\) are both real if and only if \(\det A \le {\left( {\frac{{{\rm{tr}}A}}{2}} \right)^2}\).

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