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What is the largest possible value of the quadratic form \({\bf{5}}x_{\bf{1}}^{\bf{2}} + {\bf{8}}x_{\bf{2}}^{\bf{2}}\) if \({\bf{x}} = \left( {{x_{\bf{1}}},{x_{\bf{2}}}} \right)\) and \({{\bf{x}}^T}{\bf{x = 1}}\), that is, if \(x_{\bf{1}}^{\bf{2}} + x_{\bf{2}}^{\bf{2}} = {\bf{1}}\)? (Try some examples of \({\bf{x}}\).

Short Answer

Expert verified

The largest possible value of the given quadratic form is \(8\).

Step by step solution

01

Find the matrix form of the given quadratic form

Consider the quadratic form \(5x_1^2 + 8x_2^2\),

\(\begin{aligned}{}5x_1^2 + 8x_2^2 &= \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}}5&0\\0&8\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\\ &= {{\bf{x}}^T}A{\bf{x}}\end{aligned}\)

02

Step 2: Find the eigen vector

As from the diagonal matrix, the eigenvalues are \(5\) and \(8\). Therefore, eigenvectors are shown below:

\(\lambda = 8:\left( {\begin{aligned}{{}}0\\{ \pm 1}\end{aligned}} \right)\)and \(\lambda = 5:\left( {\begin{aligned}{{}}{ \pm 1}\\0\end{aligned}} \right)\)

Since the vectors are in normalized form thus, the largest possible value of the given quadratic form is \(8\).

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Most popular questions from this chapter

Question: In Exercises 15 and 16, construct the pseudo-inverse of \(A\). Begin by using a matrix program to produce the SVD of \(A\), or, if that is not available, begin with an orthogonal diagonalization of \({A^T}A\). Use the pseudo-inverse to solve \(A{\rm{x}} = {\rm{b}}\), for \({\rm{b}} = \left( {6, - 1, - 4,6} \right)\) and let \(\mathop {\rm{x}}\limits^\^ \)be the solution. Make a calculation to verify that \(\mathop {\rm{x}}\limits^\^ \) is in Row \(A\). Find a nonzero vector \({\rm{u}}\) in Nul\(A\), and verify that \(\left\| {\mathop {\rm{x}}\limits^\^ } \right\| < \left\| {\mathop {\rm{x}}\limits^\^ + {\rm{u}}} \right\|\), which must be true by Exercise 13(c).

15. \(A = \left[ {\begin{array}{*{20}{c}}{ - 3}&{ - 3}&{ - 6}&6&{\,\,1}\\{ - 1}&{ - 1}&{ - 1}&1&{ - 2}\\{\,\,\,0}&{\,\,0}&{ - 1}&1&{ - 1}\\{\,\,\,0}&{\,\,0}&{ - 1}&1&{ - 1}\end{array}} \right]\)

Question: Find the principal components of the data for Exercise 2.

Determine which of the matrices in Exercises 7–12 are orthogonal. If orthogonal, find the inverse.

7. \(\left( {\begin{aligned}{{}{}}{.6}&{\,\,\,.8}\\{.8}&{ - .6}\end{aligned}} \right)\)

Orthogonally diagonalize the matrices in Exercises 13–22, giving an orthogonal matrix \(P\) and a diagonal matrix \(D\). To save you time, the eigenvalues in Exercises 17–22 are: (17) \( - {\bf{4}}\), 4, 7; (18) \( - {\bf{3}}\), \( - {\bf{6}}\), 9; (19) \( - {\bf{2}}\), 7; (20) \( - {\bf{3}}\), 15; (21) 1, 5, 9; (22) 3, 5.

16. \(\left( {\begin{aligned}{{}}{\,6}&{ - 2}\\{ - 2}&{\,\,\,9}\end{aligned}} \right)\)

Let u be a unit vector in \({\mathbb{R}^n}\), and let \(B = {\bf{u}}{{\bf{u}}^T}\).

  1. Given any x in \({\mathbb{R}^n}\), compute Bx and show that Bx is the orthogonal projection of x onto u, as described in Section 6.2.
  2. Show that B is a symmetric matrix and \({B^{\bf{2}}} = B\).
  3. Show that u is an eigenvector of B. What is the corresponding eigenvalue?
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