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Find the matrix of the quadratic form. Assume x is in \({\mathbb{R}^2}\).

a. \(5x_1^2 + 16{x_1}{x_2} - 5x_2^2\)

b. \(2{x_1}{x_2}\)

Short Answer

Expert verified
  1. The matrix for the quadratic form \(5x_1^2 + 16{x_1}{x_2} - 5x_2^2\) is \(\left( {\begin{aligned}{{}}5&8\\8&{ - 5}\end{aligned}} \right)\).
  1. The matrix for the quadratic form \(2{x_1}{x_2}\) is \(\left( {\begin{aligned}{{}}0&1\\1&0\end{aligned}} \right)\).

Step by step solution

01

Matrix of the quadratic form

The coefficients of the square of variables, that is \(x_i^2\), are to be divided on the main diagonal as per the order, and the coefficient of term \({x_i}{x_j}{\rm{ }}\left( {i \ne j} \right)\), divided by 2 to split properly between the entries \(\left( {i,j} \right)\) and \(\left( {j,i} \right)\).

02

Find the corresponding matrix of \(5x_1^2 + 16{x_1}{x_2} - 5x_2^2\)

(a)

The given quadratic expression is \(5x_1^2 + 16{x_1}{x_2} - 5x_2^2\).

For this expression, the order of the square matrix is 2.

So, let \(A = \left( {\begin{aligned}{{}}{{a_{11}}}&{{a_{12}}}\\{{a_{21}}}&{{a_{22}}}\end{aligned}} \right)\)

According to the rule in steps (1), 5 and \( - 5\), will be on the main diagonal of the matrix, like \({a_{11}} = 5\) and \({a_{22}} = - 5\).

And \({a_{12}} = \frac{{16}}{2}\), \({a_{21}} = \frac{{16}}{2}\) .

Substitute the required value into \(A = \left( {\begin{aligned}{{}}{{a_{11}}}&{{a_{12}}}\\{{a_{21}}}&{{a_{22}}}\end{aligned}} \right)\).

\(A = \left( {\begin{aligned}{{}}5&8\\8&{ - 5}\end{aligned}} \right)\)

So, the required matrix is \(\left( {\begin{aligned}{{}}5&8\\8&{ - 5}\end{aligned}} \right)\).

03

Find the corresponding matrix of \(2{x_1}{x_2}\)

(b)

The given quadratic expression is \(2{x_1}{x_2}\), which can be written as \(0x_1^2 + 2{x_1}{x_2} + 0x_2^2\).

For this expression, the order of the square matrix is 2.

So, let \(A = \left( {\begin{aligned}{{}}{{a_{11}}}&{{a_{12}}}\\{{a_{21}}}&{{a_{22}}}\end{aligned}} \right)\)

According to the rule in steps (1), 3 and 0 will be on the main diagonal of the matrix, like \({a_{11}} = 0\) and \({a_{22}} = 0\).

And \({a_{12}} = \frac{2}{2}\), \({a_{21}} = \frac{2}{2}\) .

Substitute the required value into \(A = \left( {\begin{aligned}{{}}{{a_{11}}}&{{a_{12}}}\\{{a_{21}}}&{{a_{22}}}\end{aligned}} \right)\).

\(A = \left( {\begin{aligned}{{}}0&1\\1&0\end{aligned}} \right)\)

So, the required matrix is \(\left( {\begin{aligned}{{}}0&1\\1&0\end{aligned}} \right)\).

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Most popular questions from this chapter

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 \(A = PD{P^{ - {\bf{1}}}}\), where P is orthogonal and D is diagonal, and let \(\lambda \) be an eigenvalue of A of multiplicity k. Then \(\lambda \) appears k times on the diagonal of D.Explain why the dimension of the eigenspace for \(\lambda \) is k.

In Exercises 17–24, \(A\) is an \(m \times n\) matrix with a singular value decomposition \(A = U\Sigma {V^T}\) , where \(U\) is an \(m \times m\) orthogonal matrix, \({\bf{\Sigma }}\) is an \(m \times n\) “diagonal” matrix with \(r\) positive entries and no negative entries, and \(V\) is an \(n \times n\) orthogonal matrix. Justify each answer.

19. Show that the columns of\(V\)are eigenvectors of\({A^T}A\), the columns of\(U\)are eigenvectors of\(A{A^T}\), and the diagonal entries of\({\bf{\Sigma }}\)are the singular values of \(A\). (Hint: Use the SVD to compute \({A^T}A\) and \(A{A^T}\).)

Question: 3. Let A be an \(n \times n\) symmetric matrix of rank r. Explain why the spectral decomposition of A represents A as the sum of r rank 1 matrices.

Question: 13. Exercises 12–14 concern an \(m \times n\) matrix \(A\) with a reduced singular value decomposition, \(A = {U_r}D{V_r}^T\), and the pseudoinverse \({A^ + } = {U_r}{D^{ - 1}}{V_r}^T\).

Suppose the equation\(A{\rm{x}} = {\rm{b}}\)is consistent, and let\({{\rm{x}}^ + } = {A^ + }{\rm{b}}\). By Exercise 23 in Section 6.3, there is exactly one vector\({\rm{p}}\)in Row\(A\)such that\(A{\rm{p}} = {\rm{b}}\). The following steps prove that\({{\rm{x}}^ + } = {\rm{p}}\)and\({{\rm{x}}^ + }\)is the minimum length solution of\(A{\rm{x}} = {\rm{b}}\).

  1. Show that \({{\rm{x}}^ + }\) is in Row \(A\). (Hint: Write \({\rm{b}}\) as \(A{\rm{x}}\) for some \({\rm{x}}\), and use Exercise 12.)
  2. Show that\({{\rm{x}}^ + }\)is a solution of\(A{\rm{x}} = {\rm{b}}\).
  3. Show that if \({\rm{u}}\) is any solution of \(A{\rm{x}} = {\rm{b}}\), then \(\left\| {{{\rm{x}}^ + }} \right\| \le \left\| {\rm{u}} \right\|\), with equality only if \({\rm{u}} = {{\rm{x}}^ + }\).
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