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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: [M] A Landsat image with three spectral components was made of Homestead Air Force Base in Florida (after the base was hit by Hurricane Andrew in 1992). The covariance matrix of the data is shown below. Find the first principal component of the data, and compute the percentage of the total variance that is contained in this component.

\[S = \left[ {\begin{array}{*{20}{c}}{164.12}&{32.73}&{81.04}\\{32.73}&{539.44}&{249.13}\\{81.04}&{246.13}&{189.11}\end{array}} \right]\]

(M) Compute an SVD of each matrix in Exercises 26 and 27. Report the final matrix entries accurate to two decimal places. Use the method of Examples 3 and 4.

26. \(A{\bf{ = }}\left( {\begin{array}{*{20}{c}}{ - {\bf{18}}}&{{\bf{13}}}&{ - {\bf{4}}}&{\bf{4}}\\{\bf{2}}&{{\bf{19}}}&{ - {\bf{4}}}&{{\bf{12}}}\\{ - {\bf{14}}}&{{\bf{11}}}&{ - {\bf{12}}}&{\bf{8}}\\{ - {\bf{2}}}&{{\bf{21}}}&{\bf{4}}&{\bf{8}}\end{array}} \right)\)

Question: Let \({\bf{X}}\) denote a vector that varies over the columns of a \(p \times N\) matrix of observations, and let \(P\) be a \(p \times p\) orthogonal matrix. Show that the change of variable \({\bf{X}} = P{\bf{Y}}\) does not change the total variance of the data. (Hint: By Exercise 11, it suffices to show that \(tr\left( {{P^T}SP} \right) = tr\left( S \right)\). Use a property of the trace mentioned in Exercise 25 in Section 5.4.)

Classify the quadratic forms in Exercises 9–18. Then make a change of variable, \({\bf{x}} = P{\bf{y}}\), that transforms the quadratic form into one with no cross-product term. Write the new quadratic form. Construct \(P\) using the methods of Section 7.1.

11. \({\bf{2}}x_{\bf{1}}^{\bf{2}} - {\bf{4}}{x_{\bf{1}}}{x_{\bf{2}}} - x_{\bf{2}}^{\bf{2}}\)

Question: 4. Let A be an \(n \times n\) symmetric matrix.

a. Show that \({({\rm{Col}}A)^ \bot } = {\rm{Nul}}A\). (Hint: See Section 6.1.)

b. Show that each y in \({\mathbb{R}^n}\) can be written in the form \(y = \hat y + z\), with \(\hat y\) in \({\rm{Col}}A\) and z in \({\rm{Nul}}A\).

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