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In Exercises 3-6, find (a) the maximum value of \(Q\left( {\rm{x}} \right)\) subject to the constraint \({{\rm{x}}^T}{\rm{x}} = 1\), (b) a unit vector \({\rm{u}}\) where this maximum is attained, and (c) the maximum of \(Q\left( {\rm{x}} \right)\) subject to the constraints \({{\rm{x}}^T}{\rm{x}} = 1{\rm{ and }}{{\rm{x}}^T}{\rm{u}} = 0\).

4. \(Q\left( x \right) = 3x_1^2 + 3x_2^2 + 5x_3^2 + 6x_1^{}x_2^{} + 2x_1^{}x_3^{} + 2x_2^{}x_3^{}\).

Short Answer

Expert verified

The required values are:

  1. The maximum value of\(Q\left( {\rm{x}} \right)\)the subject to the constraint\({{\rm{x}}^T}{\rm{x}} = 1\)is\({\lambda _1} = 7\).
  2. A unit vector\({\rm{u}}\)where this maximum is attained is\({\rm{u}} = \pm \left[ {\begin{array}{*{20}{c}}{{1 \mathord{\left/
  3. {\vphantom {1 {\sqrt 3 }}} \right.
  4. \kern-\nulldelimiterspace} {\sqrt 3 }}}\\{{1 \mathord{\left/
  5. {\vphantom {1 {\sqrt 3 }}} \right.
  6. \kern-\nulldelimiterspace} {\sqrt 3 }}}\\{{1 \mathord{\left/
  7. {\vphantom {1 {\sqrt 3 }}} \right.
  8. \kern-\nulldelimiterspace} {\sqrt 3 }}}\end{array}} \right]\).
  9. The maximum of\(Q\left( {\rm{x}} \right)\)subject to the constraints\({{\rm{x}}^T}{\rm{x}} = 1\)and \({{\rm{x}}^T}{\rm{u}} = 0\) is\({\lambda _2} = 4\).

Step by step solution

01

Find the greatest eigenvalue

As per the question, we have:

\(Q\left( {\rm{x}} \right) = 3x_1^2 + 3x_2^2 + 5x_3^2 + 6x_1^{}x_2^{} + 2x_1^{}x_3^{} + 2x_2^{}x_3^{}\)

The maximum value of the given function subjected to constraints\({{\rm{x}}^T}{\rm{x}} = 1\)will be the greatest eigenvalue.

So, from exercise 2, the eigenvalues are 7, 4, and 0. This implies that the greatest eigenvalue is 7.

\({\lambda _1} = 7\)

Hence, the maximum value of \(Q\left( {\rm{x}} \right)\) the subject to the constraint \({{\rm{x}}^T}{\rm{x}} = 1\) is \({\lambda _1} = 7\).

02

Find the vector for this greatest eigenvalue. 

Apply the theorem which states that the value of\({{\rm{x}}^T}A{\rm{x}}\) is maximum when \({\rm{x}}\) is a unit eigenvector \({{\rm{u}}_1}\) corresponding to the greatest eigenvalue \({\lambda _1}\).

The eigenvector that corresponds to this eigenvalue is:

\({\rm{u}} = \pm \left[ {\begin{array}{*{20}{c}}{{1 \mathord{\left/

{\vphantom {1 {\sqrt 3 }}} \right.

\kern-\nulldelimiterspace} {\sqrt 3 }}}\\{{1 \mathord{\left/

{\vphantom {1 {\sqrt 3 }}} \right.

\kern-\nulldelimiterspace} {\sqrt 3 }}}\\{{1 \mathord{\left/

{\vphantom {1 {\sqrt 3 }}} \right.

\kern-\nulldelimiterspace} {\sqrt 3 }}}\end{array}} \right]\).

Hence,the unit vector\({\rm{u}}\)where this maximum is attained is\({\rm{u}} = \pm \left[ {\begin{array}{*{20}{c}}{{1 \mathord{\left/

{\vphantom {1 {\sqrt 3 }}} \right.

\kern-\nulldelimiterspace} {\sqrt 3 }}}\\{{1 \mathord{\left/

{\vphantom {1 {\sqrt 3 }}} \right.

\kern-\nulldelimiterspace} {\sqrt 3 }}}\\{{1 \mathord{\left/

{\vphantom {1 {\sqrt 3 }}} \right.

\kern-\nulldelimiterspace} {\sqrt 3 }}}\end{array}} \right]\).

03

Find the second greatest eigenvalue. 

The maximum value of the given function subjected to constraints\({{\rm{x}}^T}{\rm{x}} = 1\)and\({{\rm{x}}^T}{\rm{u}} = 0\)will be the second largest eigenvalue. That is:

\({\lambda _2} = 4\)

Hence, the maximum of \(Q\left( {\rm{x}} \right)\) subject to the constraints \({{\rm{x}}^T}{\rm{x}} = 1\) and \({{\rm{x}}^T}{\rm{u}} = 0\) is \({\lambda _2} = 4\).

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Most popular questions from this chapter

Question: In Exercises 1 and 2, convert the matrix of observations to mean deviation form, and construct the sample covariance matrix.

\(1.\,\,\left( {\begin{array}{*{20}{c}}{19}&{22}&6&3&2&{20}\\{12}&6&9&{15}&{13}&5\end{array}} \right)\)

(M) Orhtogonally diagonalize the matrices in Exercises 37-40. To practice the methods of this section, do not use an eigenvector routine from your matrix program. Instead, use the program to find the eigenvalues, and for each eigenvalue \(\lambda \), find an orthogonal basis for \({\bf{Nul}}\left( {A - \lambda I} \right)\), as in Examples 2 and 3.

38. \(\left( {\begin{aligned}{{}}{.{\bf{63}}}&{ - .{\bf{18}}}&{ - .{\bf{06}}}&{ - .{\bf{04}}}\\{ - .{\bf{18}}}&{.{\bf{84}}}&{ - .{\bf{04}}}&{.{\bf{12}}}\\{ - .{\bf{06}}}&{ - .{\bf{04}}}&{.{\bf{72}}}&{ - .{\bf{12}}}\\{ - .{\bf{04}}}&{.{\bf{12}}}&{ - .{\bf{12}}}&{.{\bf{66}}}\end{aligned}} \right)\)

Question: 11. Given multivariate data \({X_1},................,{X_N}\) (in \({\mathbb{R}^p}\)) in mean deviation form, let \(P\) be a \(p \times p\) matrix, and define \({Y_k} = {P^T}{X_k}{\rm{ for }}k = 1,......,N\).

  1. Show that \({Y_1},................,{Y_N}\) are in mean-deviation form. (Hint: Let \(w\) be the vector in \({\mathbb{R}^N}\) with a 1 in each entry. Then \(\left( {{X_1},................,{X_N}} \right)w = 0\) (the zero vector in \({\mathbb{R}^p}\)).)
  2. Show that if the covariance matrix of \({X_1},................,{X_N}\) is \(S\), then the covariance matrix of \({Y_1},................,{Y_N}\) is \({P^T}SP\).

Question: 6. Let A be an \(n \times n\) symmetric matrix. Use Exercise 5 and an eigenvector basis for \({\mathbb{R}^n}\) to give a second proof of the decomposition in Exercise 4(b).

Let \(A = \left( {\begin{aligned}{{}}{\,\,\,4}&{ - 1}&{ - 1}\\{ - 1}&{\,\,\,4}&{ - 1}\\{ - 1}&{ - 1}&{\,\,\,4}\end{aligned}} \right)\), and\({\rm{v}} = \left( {\begin{aligned}{{}}1\\1\\1\end{aligned}} \right)\). Verify that 5 is an eigenvalue of \(A\) and \({\rm{v}}\)is an eigenvector. Then orthogonally diagonalize \(A\).

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