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Exercises 23 and 24 show how to classify a quadratic form \(Q\left( {\bf{x}} \right) = {{\bf{x}}^T}A{\bf{x}}\) when \(A = \left( {\begin{aligned}{{}}a&b\\b&d\end{aligned}} \right)\)and\(detA \ne {\bf{0}}\), without finding the eigenvalues of \(A\).

24. Verify the following statements.

  1. \(Q\)is positive definite if\(detA{\bf{ > 0}}\)and\(a{\bf{ > 0}}\).
  2. \(Q\)is negative definite if\(detA{\bf{ > 0}}\)and\(a < {\bf{0}}\).
  3. \(Q\) is indefinite if \(detA{\bf{ < 0}}\).

Short Answer

Expert verified
  1. It is verified that \(Q\) is positive definite if \(\det A > 0\) and \(a > 0\).
  2. It is verified that \(Q\) is negative definite if \(\det A > 0\) and \(a < 0\).
  3. It is verified that \(Q\) is indefinite if \(\det A < 0\).

Step by step solution

01

(a) Step 1: Find the characteristic polynomial

Assume \(A = \left( {\begin{aligned}{{}}a&b\\b&d\end{aligned}} \right)\).

To show that \(Q\)is positive definite we need to prove that all the eigenvalues of the matrix\(A\)are positive.

Find the characteristic polynomial.

\(\begin{aligned}{}\det \left( {A - \lambda I} \right) &= \det \left( {\begin{aligned}{{}}{a - \lambda }&b\\b&{d - \lambda }\end{aligned}} \right)\\ &= \left( {a - \lambda } \right)\left( {d - \lambda } \right) - bd\\ &= {\lambda ^2} - \left( {a + d} \right)\lambda + ad - {b^2}........\left( 1 \right)\end{aligned}\)

02

Show that \(Q\) is positive definite if \(detA{\bf{ > 0}}\) and \(a{\bf{ > 0}}\)

Also, the expansion can be written as:

\(\left( {\lambda - {\lambda _1}} \right)\left( {\lambda - {\lambda _2}} \right) = {\lambda ^2} - \left( {{\lambda _1} + {\lambda _2}} \right)\lambda + {\lambda _1} \cdot {\lambda _2}......\left( 2 \right)\)

On equating both equations we get,

\({\lambda _1} + {\lambda _2} = a + d\)

\(\begin{aligned}{}{\lambda _1}{\lambda _2} &= ad - {b^2}\\ &= \det A\end{aligned}\)

Since, determinant of A is positive,\({\lambda _1}{\lambda _2} > 0\), hence\({\lambda _1}\)and\({\lambda _2}\)has the same sign. Also\({\lambda _1} + {\lambda _2} = a + d > 0\)which proves that both eigenvalues are positive. Therefore,\(Q\)is positive definite

It is verified that \(Q\) is positive definite if \(\det A > 0\) and \(a > 0\).

03

(b) Step 3: Show that \(Q\) is negative definite if \(detA{\bf{ > 0}}\) and \(a{\bf{ > 0}}\)

As the determinant \(A\) is positive, \({\lambda _1}{\lambda _2} > 0\)hence\({\lambda _1}\)and\({\lambda _2}\)have the same sign. Also\({\lambda _1} + {\lambda _2} = a + d < 0\)which proves that both eigenvalues are negative. Therefore,\(Q\)is a negative definite.

It is verified that \(Q\) is negative definite if \(\det A > 0\) and \(a < 0\).

04

(c) Step 4: Show that \(Q\) is indefinite if \(detA{\bf{ < 0}}\)

As the determinant \(A\) is negative, \({\lambda _1}{\lambda _2} < 0\)hence\({\lambda _1}\)and\({\lambda _2}\)have the opposite sign. Therefore,\(Q\)is indefinite.

It is verified that \(Q\) is indefinite if \(\det A < 0\).

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Most popular questions from this chapter

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 \(A\).

Suppose\(A = PR{P^{ - {\bf{1}}}}\), where P is orthogonal and R is upper triangular. Show that if A is symmetric, then R is symmetric and hence is actually a diagonal matrix.

Construct a spectral decomposition of A from Example 2.

Question 11: Prove that any \(n \times n\) matrix A admits a polar decomposition of the form \(A = PQ\), where P is a \(n \times n\) positive semidefinite matrix with the same rank as A and where Q is an \(n \times n\) orthogonal matrix. (Hint: Use a singular value decomposition, \(A = U\sum {V^T}\), and observe that \(A = \left( {U\sum {U^T}} \right)\left( {U{V^T}} \right)\).) This decomposition is used, for instance, in mechanical engineering to model the deformation of a material. The matrix P describe the stretching or compression of the material (in the directions of the eigenvectors of P), and Q describes the rotation of the material in space.

Orthogonally diagonalize the matrices in Exercises 13โ€“22, giving an orthogonal matrix\(P\)and a diagonal matrix\(D\). To save you time, the eigenvalues in Exercises 17โ€“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.

19. \(\left( {\begin{aligned}{{}}3&{ - 2}&4\\{ - 2}&6&2\\4&2&3\end{aligned}} \right)\)

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