Deck 11: Additional Topics and Applications
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Deck 11: Additional Topics and Applications
1
Find a polynomial formula for the quadratic form
with the matrix
.


.

2
Find a polynomial formula for the quadratic form
with the matrix
.


.

3
Find the
matrix A of the quadratic form
.


.


4
Find the
matrix A of the quadratic form
.


.
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5
Find the
matrix A of the quadratic form
.


.
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6
Find an
orthogonal matrix P and diagonal matrix D such that the change of variables
transforms the given quadratic form
into the form
.





.

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7
Find a
orthogonal matrix P and diagonal matrix D such that the change of variables
transforms the given quadratic form
into the form
.





.

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8
Determine if the quadratic form
is positive definite, negative definite, indefinite, or none of these.



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9
Determine if the quadratic form
is positive definite, negative definite, indefinite, or none of these.



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10
Determine if the quadratic form
is positive definite, negative definite, indefinite, or none of these.



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11
If B is any
matrix, then the formula
defines a positive semidefinite quadratic form on
.



.
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12
If the quadratic form
is positive definite, then A is invertible.

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13
If
, and
, then the graph of the equation
is an ellipse.

, and

, then the graph of the equation

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14
If
is a quadratic form, then
for every scalar


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15
If
is a positive definite quadratic form and
is a negative definite quadratic form, then
is indefinite.



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16
Determine if the matrix A is positive definite.


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17
Determine if the matrix A is positive definite.


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18
Determine if the matrix A is positive definite.


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19
Show that the given symmetric matrix A is positive definite by finding the determinants of its leading principal submatrices. Then find the LDU-factorization of A. 

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20
Show that the given matrix is positive definite, and then find the LDU-factorization.


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21
Show that the given matrix is positive definite, and then find the LDU-factorization.


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22
Show that the given matrix is positive definite, and then find the LDU-factorization of the matrix.


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23
Find the Cholesky decomposition
of the positive definite matrix
.


.
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24
Find the Cholesky decomposition
of the positive definite matrix
.


.
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25
Find the Cholesky decomposition
of the positive definite matrix
.


.
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26
If A is positive definite, then
exists, and
is positive definite.


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27
If A is positive definite, every entry along the diagonal of A is positive.
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28
If A has an LDU-factorization, then A is positive definite.
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29
If A is positive definite with factorization
, then the diagonal entries of D are the eigenvalues of A.

, then the diagonal entries of D are the eigenvalues of A.
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30
A symmetric matrix
is positive definite if and only if
is negative definite.


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31
Find the maximum and minimum values of the quadratic form
subject to the constraint
.


.
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32
Find the maximum and minimum values of the quadratic form
subject to the constraint
.


.
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33
Find the maximum and minimum values of the quadratic form
subject to the constraint
.


.
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34
Use the technique of constrained optimization (not calculus) to find the absolute maximum and minimum values of the function
, for
.

, for

.
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35
Use the technique of constrained optimization (not calculus) to find the absolute maximum and minimum values of the function
, for
.

, for

.
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36
Find the maximum and minimum values of the quadratic form
subject to the constraint
.


.
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37
Find the maximum and minimum values of the quadratic form
subject to the constraint
.


.
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38
Find the maximum and minimum values of the quadratic form
subject to the constraint
.


.
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39
Find the maximum and minimum values of the quadratic form
subject to the constraints
and
where
.




.
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40
Find the maximum and minimum values of the quadratic form
subject to the constraints
and
, where
.



, where

.
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41
If m is the minimum value of a quadratic form
subject to the constraint
, then cm is the minimum value of
subject to the constraint
.


, then cm is the minimum value of


.
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42
If 2 is the maximum value of a quadratic form
subject to the constraint
, then 1 is the maximum value of
subject to the constraint
.


, then 1 is the maximum value of


.
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43
Suppose A is a positive definite matrix and subject to the constraint
the quadratic form
has maximum value M and minimum value m. Then subject to the constraint
the quadratic form
has maximum value
and minimum value
.






.
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44
Let
, where A is a symmetric
matrix with eigenvalues
and associated orthonormal eigenvectors
. Then subject to the constraints
and
, the minimum value of
is
, attained at
.

, where A is a symmetric



. Then subject to the constraints


, the minimum value of


, attained at

.
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45
Suppose
and
are quadratic forms on
. If subject to the constraint
the maximum values of
and
are
and
respectively, then subject to the constraint
the maximum value of
is
.



. If subject to the constraint








.
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46
Evaluate
.

.
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47
Determine if
is in
.


.
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48
Evaluate
.

.
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49
Evaluate
.

.
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50
Normalize
using the complex dot product.

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51
Evaluate
for the matrices
.


.
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52
Evaluate
for the matrices
, where the inner product is defined by
.


, where the inner product is defined by

.
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53
Evaluate
for the functions
and
where the inner product is defined by
.




.
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54
Evaluate
for
, where the inner product is defined by
.


, where the inner product is defined by

.
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55
In
, using the complex dot product, find a basis for
, where
.

, using the complex dot product, find a basis for

, where

.
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56
In a complex inner product space,
for all vectors u and
.


.
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57
The dimension of the complex vector space
is n.

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58
For vectors u, v in a complex inner product space, we have
if and only if
.


.
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59
If
is an orthonormal set in a complex inner product space V, then the set
is also an orthonormal set in V.


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60
If
and
, we have
, where the inner product is defined by
.


, we have

, where the inner product is defined by

.
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61
Find
for the matrix
.


.
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62
Find
for the matrix
.


.
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63
Find
for the matrix
.


.
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64
Determine if the given matrix A is Hermitian.


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65
Determine if the given matrix A is Hermitian.


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66
Determine if the given matrix A is normal.


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67
Determine if the given matrix A is normal.


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68
Verify that the given matrix A is unitary, then diagonalize A by finding a unitary matrix P and diagonal matrix D such that
.

.

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69
Verify that the given matrix A is Hermitian, where a, b denote real numbers, then diagonalize A by finding a unitary matrix P and diagonal matrix D such that
.


.

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70
Verify that the given matrix A is normal, then diagonalize A by finding a unitary matrix P and diagonal matrix D such that
.


.

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71
If A is a unitary matrix, and
is an eigenvalue of A, then
.


.
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72
If A is an
matrix with complex entries, then the matrix
is Hermitian.


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73
If A and B are
matrices that are both normal, and
, then both
and AB are normal.


, then both

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74
The formula
defines an inner product on the complex vector space
of all
complex matrices.



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75
An
matrix A is Hermitian if and only if it is normal and all of its eigenvalues are real.

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