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In Exercises 1 and 2,find the change of variable \({\rm{x}} = P{\rm{y}}\) that transforms the quadratic form \({{\rm{x}}^T}A{\rm{x}}\) into \({{\rm{y}}^T}D{\rm{y}}\) as shown.

1. \(5x_1^2 + 6x_2^2 + 7x_3^2 + 4x_1^{}x_2^{} - 4x_2^{}x_3^{} = 9y_1^2 + 6y_2^2 + 3y_3^2\)

Short Answer

Expert verified

The requiredchange of variable is:

\(P = \left( {\begin{aligned}{{}}{\frac{1}{3}}&{\frac{2}{3}}&{ - \frac{2}{3}}\\{\frac{2}{3}}&{\frac{1}{3}}&{\frac{2}{3}}\\{ - \frac{2}{3}}&{\frac{2}{3}}&{\frac{1}{3}}\end{aligned}} \right)\).

Step by step solution

01

Symmetric Matrices and Quadratic Forms

When any Symmetric Matrix \(A\) is diagonalized orthogonally as \(PD{P^{ - 1}}\) we have:

\(\begin{aligned}{}{x^T}Ax = {y^T}Dy\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{\rm{as }}x = Py} \right\}\\{\rm{and}}\\\left\| x \right\| = \left\| {Py} \right\| = \left\| y \right\|\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {\forall y \in \mathbb{R}} \right\}\end{aligned}\)

02

Find the Change of Variables

As per the question, we have:

\(5x_1^2 + 6x_2^2 + 7x_3^2 + 4x_1^{}x_2^{} - 4x_2^{}x_3^{} = 9y_1^2 + 6y_2^2 + 3y_3^2\)

The matrix of thequadratic formwill be:

\(A = \left( {\begin{aligned}{{}}5&2&0\\2&6&{ - 2}\\0&{ - 2}&7\end{aligned}} \right)\)

And for each eigenvalue of\(A\)defined in the right-hand side of the equation will be expressed as:

\(\begin{aligned}{}{\lambda _1} = 9 \Rightarrow {P_1} = \left( {\begin{aligned}{{}}{\frac{1}{3}}\\{\frac{2}{3}}\\{ - \frac{2}{3}}\end{aligned}} \right)\\{\lambda _2} = 6 \Rightarrow {P_2} = \left( {\begin{aligned}{{}}{\frac{2}{3}}\\{\frac{1}{3}}\\{\frac{2}{3}}\end{aligned}} \right)\\{\lambda _3} = 3 \Rightarrow {P_3} = \left( {\begin{aligned}{{}}{ - \frac{2}{3}}\\{\frac{2}{3}}\\{\frac{1}{3}}\end{aligned}} \right)\end{aligned}\)

Hence,therequired change of variable is:

\(P = \left( {\begin{aligned}{{}}{\frac{1}{3}}&{\frac{2}{3}}&{ - \frac{2}{3}}\\{\frac{2}{3}}&{\frac{1}{3}}&{\frac{2}{3}}\\{ - \frac{2}{3}}&{\frac{2}{3}}&{\frac{1}{3}}\end{aligned}} \right)\).

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Most popular questions from this chapter

In Exercises 25 and 26, mark each statement True or False. Justify each answer.

26.

  1. There are symmetric matrices that are not orthogonally diagonizable.
  2. b. If \(B = PD{P^T}\), where \({P^T} = {P^{ - {\bf{1}}}}\) and D is a diagonal matrix, then B is a symmetric matrix.
  3. c. An orthogonal matrix is orthogonally diagonizable.
  4. d. The dimension of an eigenspace of a symmetric matrix is sometimes less than the multiplicity of the corresponding eigenvalue.

Find the matrix of the quadratic form. Assume x is in \({\mathbb{R}^{\bf{3}}}\).

a. \(3x_1^2 - 2x_2^2 + 5x_3^2 + 4{x_1}{x_2} - 6{x_1}{x_3}\)

b. \(4x_3^2 - 2{x_1}{x_2} + 4{x_2}{x_3}\)

Question 7: Prove that an \(n \times n\) A is positive definite if and only if A admits a Cholesky factorization, namely, \(A = {R^T}R\) for some invertible upper triangular matrix R whose diagonal entries are all positive. (Hint; Use a QR factorization and Exercise 26 in Section 7.2.)

Question 8: Use Exercise 7 to show that if A is positive definite, then A has a LU factorization, \(A = LU\), where U has positive pivots on its diagonal. (The converse is true, too).

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.

23. Let \(U = \left( {{u_1}...{u_m}} \right)\) and \(V = \left( {{v_1}...{v_n}} \right)\) where the \({{\bf{u}}_i}\) and \({{\bf{v}}_i}\) are in Theorem 10. Show that \(A = {\sigma _1}{u_1}v_1^T + {\sigma _2}{u_2}v_2^T + ... + {\sigma _r}{u_r}v_r^T\).

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