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MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Find a singular value decomposition of the matrix A.
A = 1 0
0 1 –9 0
0 51 0
0 1
A =–1 0
0 1 9 0
0 5 1 0
0 1
A =–9 0
0 59 0
0 5 1 0
0 1
A =–9 0
0 59 0
0 5 –1 0
0 1
Find a unit vector at which the quadratic form xTAx is maximized, subject to the constraint xTx = 1.
A = 8 –6 4
–6 9 –2
4 –2 4
Determine whether the matrix is symmetric.
Find the maximum value of Q(x) subject to the constraint xTx = 1.
Q(x) = 14x2
1+ 14x2
2+ 18x2
3+ 26x1x2+ 18x1x3+ 18x2x3
Find the singular values of the matrix.
Find the matrix of the quadratic form.
3x 2
1+5x 2
2+2x 2
3–12x1x2+x2x3
Make a change of variable, x = Py, that transforms the given quadratic form into a quadratic form with no cross–product
term. Give P and the new quadratic form.
Q(x) =4 x 2
1+7x 2
2+4x1x2
P =1/ 5–2/ 5
2/ 5 1/ 5; 8 y 2
1+3y 2
2
P =–1/ 5–2/ 5
2/ 5 1/ 5; –8y 2
1–3y 2
2
P =1–2
2 1 ; 8 y 2
1+3 y 2
2
Use the given covariance matrix to compute the percentage of the total variance that is contained in the first principal
component. Round to one decimal place.
Find the maximum value of Q(x) subject to the constraint xTx = 1.
Q(x) =2 x 2
1+7x 2
2+5x 2
3
Find a singular value decomposition of the matrix A.
A =0–1
1 0 4 3 0
0 4 2
1 1 1
–1 0 1
1 –2 1
A =0–1
1 0 48 0 0
032 0
1/ 3 1/ 3 1/ 3
–1/ 2 0 1/ 2
1/ 6–2/ 6 1/ 6
A =0–1
1 0 4 3 0 0
0 4 2 0
1/ 3–1/ 2 1/ 6
1/ 3 0 –2/ 6
1/ 3 1/ 2 1/ 6
A =0–1
1 0 4 3 0 0
0 4 2 0
1/ 3 1/ 3 1/ 3
–1/ 2 0 1/ 2
1/ 6–2/ 6 1/ 6
Compute the quadratic form xTAx for the given matrix A and vector x.
A =9 3 0
3 1 5
0 5 –2, x=x1
x2
x3
9x 2
1+x2
2– 2x2
3+3x1+8x2+5x3
9x 2
1+x2
2– 2x2
3+6x1x2+10x2x3
9x 2
1–x2
2+ 2x2
3–6x1x2–10x2x3
Find a unit vector at which the quadratic form xTAx is maximized, subject to the constraint xTx = 1.
Orthogonally diagonalize the matrix, giving an orthogonal matrix P and a diagonal matrix D.
P =2/3 –2/3 1/3
2/3 1/3 –2/3
1/3 2/3 2/3 , D =1 0 0
0 4 0
0 0 25
P =2/3 –2/3 1/3
2/3 1/3 –2/3
1/3 2/3 2/3 , D =25 0 0
0 4 0
0 0 1
P =2–2 1
2 1 –2
1 2 2 , D =25 0 0
0 4 0
0 0 1
P =2–2 1
2 1 –2
1 2 2 , D =1 0 0
0 4 0
0 0 25
Find the maximum value of Q(x) subject to the constraints xTx = 1 and xTu = 0, where u is a unit eigenvector
corresponding to the greatest eigenvalue of the matrix of the quadratic form.
Q(x) =2 x 2
1+7x 2
2+4x 2
3
Orthogonally diagonalize the matrix, giving an orthogonal matrix P and a diagonal matrix D.
P = 1/ 3 1/ 2 1/ 6
1/ 3–1/ 2 1/ 6
–1/ 3 0 2/ 6
, D =25 0 0
0 25 0
0 0 4
P =1/ 3–1/ 2–1/ 6
1/ 3 1/ 2–1/ 6
1/ 3 0 2/ 6
, D =4 0 0
0 4 0
0 0 25
P =1/ 3–1/ 2–1/ 6
1/ 3 1/ 2–1/ 6
1/ 3 0 2/ 6
, D =25 0 0
0 4 0
0 0 4
P =1–1–1
1 1 –1
1 0 2 , D =25 0 0
0 4 0
0 0 4
Convert the matrix of observations to mean–deviation form, and construct the sample covariance matrix.
22 27 22 27 727
13 43 58 28 53 33
S =
50 –455
6
–455
6 2331
3
Orthogonally diagonalize the matrix, giving an orthogonal matrix P and a diagonal matrix D.
P = 3/ 13 2/ 13
–2/ 13 3/ 13 , D =16 0
0 3
P = 3/ 13 –2/ 13
–2/ 13 3/ 13 , D =3 0
016
P = 3 –2
–2 3 , D =16 0
0 3
P = 3 2
–2 3 , D =16 0
0 3
Find the maximum value of Q(x) subject to the constraints xTx = 1 and xTu = 0, where u is a unit eigenvector
corresponding to the greatest eigenvalue of the matrix of the quadratic form.
Q(x) = 14x2
1+ 14x2
2+ 18x2
3+ 26x1x2+ 18x1x3+ 18x2x3
Find the singular values of the matrix.
Find the matrix of the quadratic form.
Orthogonally diagonalize the matrix, giving an orthogonal matrix P and a diagonal matrix D.
P = 1/ 5–2/ 5
–2/ 5 1/ 5, D =1 0
0 6
P =1/ 5–2/ 5
2/ 5 1/ 5, D =6 0
0 1
Make a change of variable, x = Py, that transforms the given quadratic form into a quadratic form with no cross–product
term. Give P and the new quadratic form.
Q(x) = 13x2
1+ 12x2
2+ 5x2
3+ 20x1x2+ 8x1x3+ 12x2x3
P =2–2 1
2 1 –2
1 2 2 ; y2
1+ 4y2
2+ 25y2
3
P =2/3 –2/3 1/3
2/3 1/3 –2/3
1/3 2/3 2/3 ; 25y2
1+ 4y2
2+y2
3
P =2–2 1
2 1 –2
1 2 2 ; 25y2
1+ 4y2
2+y2
3
P =–2/3 –2/3 1/3
–2/3 1/3 –2/3
1/3 –2/3 2/3 ; 5y2
1+ 2y2
2+y2
3
Determine whether the matrix is symmetric.
Find a singular value decomposition of the matrix A.
A =–1/ 2 1/ 2
1/ 2 1/ 29 0
0 9 –1/ 2 1/ 2
1/ 2 1/ 2
A =–1/ 2 1/ 2
1/ 2 1/ 25 0
0 3 –1/ 2 1/ 2
1/ 2 1/ 2
A =–1 1
1 1 5 0
0 3 –1 1
1 1
A =–1 1
1 1 4–1
–1 4 –1 1
1 1
Compute the quadratic form xTAx for the given matrix A and vector x.
Convert the matrix of observations to mean–deviation form, and construct the sample covariance matrix.
912 0 3
7 4 10 7
13 1 1 1
S =
30 –3–15 –12
–3 18 –12 –3
–15 –12 18 9
–12 –3 9 2
S = 30 –12 12
–12 6 0
12 0 36
S =–30 12 –12
12 –6 0
–12 0 –36
S = 90 –36 36
–36 18 0
36 0 108
Use the given covariance matrix to compute the percentage of the total variance that is contained in the first principal
component. Round to one decimal place.
S = 30 –12 12
–12 6 0
12 0 36