Chapter 6: Determinants
I will show several problems where I find the determinant, illustrating the several methods of doing this.
6.1
2. For any 2x2 matrix A, det(A) = ad - bc, so det(A) = (2)(5) - (3)(4) = -2 . Since this is not 0, A is invertible.
5. Let's use Laplace Expansion and expand across the first column. Remember to alternate signs.
det(A) = (1)(2)(det(A11) + (-1)(5)(det(A12) + (1)(7)(det(A13)
= (2 * 55) + (-5 * 0) + (7 * 0) = 110 The matrix is invertible
6. Determinant of a upper- or lower-triangular matrix is simply the product of the diagonal entries.
det(A) = (6)(4)(1) = 24 The matrix is invertible
8. For any 3x3 matrix A with column vectors u, v, w, determinant of A is u ·(v x w)
det(A) = [1 1 3] · ([2 1 2] x [3 1 1])
= [1 1 3] · [-1 4 -1]
= 0 The matrix is not invertible
41. Remember, det(A) = Σ (sgn P)(prod P). In this matrix, two nonzero patterns exist: (2 -> 3 -> 1 -> 2 -> 4), with 5 inversions, and (2 -> 3 -> 3 -> 2 -> 2), with 8 inversions.
det(A) = (-1)5(2 * 3 * 1 * 2 * 4) + (-1)8(2 * 3 * 3 * 2 * 2)
= (-48) + (72) = 24
6.3
22. Cramer's Rule states that in the system Ax = b , where A is an invertible n x n matrix, the components xi of the solution vector are xi = det(Ab,i)/det(A), where Ab,i is the matrix obtained by replacing the ith column of A with b
x1 = det([[1 7][3 11]])/det(A) = (-10) / (5) = -2
x2 = det([[3 1][4 3]])/det(A) = (5) / (5) = 1
x = [-2 1]
Chapter 7: Eigenvalues and Eigenvectors
<u</u>7.1
<u</u>16. Since this transformation takes a vector and rotates it 180 degrees (reflects it about the origin), -1 is the only eigenvalue, and all vectors in R2 are eigenvectors.
