Deck 8: Orthogonality
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Deck 8: Orthogonality
1
Find all values of a so that u and v are orthogonal.



2
Find all values of a so that u and v are orthogonal.



3
Determine if the vectors form an orthogonal set.


The vectors form an orthogonal set.
4
Determine if the vectors form an orthogonal set.


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5
Find all values of a (if any) so that the given vectors form an orthogonal set.


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6
Suppose
and
are orthogonal with
and
. Determine
.





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7
Determine all values of a so that the vector u is orthogonal to the subspace S.


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8
Find a basis for
for the subspace S.



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9
Find a basis for
for the subspace S.



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10
Suppose S is a subspace of
, and
. Determine
.



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11
If
is an orthogonal basis for a subspace S of
, and
then
is a diagonal
matrix.





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12
If
is a linearly independent set of vectors, then
is orthogonal.


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13
If
is an orthogonal set of vectors, and
, then
.



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14
If A is any matrix, then
.

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15
If S is a subspace of
, then
.


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16
Determine
, where
.


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17
Determine
, where
.


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18
Determine
, where
,
.



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19
Find an orthogonal basis for the subspace
.

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20
Find an orthogonal basis for the subspace
.

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21
Find an orthogonal basis for the subspace
.

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22
Find
, where
.


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23
Apply the Gram-Schmidt process to the given basis for
to produce an orthogonal basis for
. Then normalize the vectors to produce an orthonormal basis for
.





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24
Find an orthonormal basis for the subspace
.

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25
Find an orthonormal basis for the subspace
, and use it to to find
, where
.



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26
If S is a nonzero subspace of
, then for every vector u in
,
belongs to S.



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27
If
is a nonzero subspace of
, and u belongs to
, then
.




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28
If
is a nonzero subspace of
, and u belongs to
, then
for every vector
.





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29
If
and
are nonzero subspaces of
, then
for every vector u in
.





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30
If
is an orthonormal set, then
is linearly independent.


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31
Determine if the given matrix is symmetric.


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32
Determine if the given matrix is orthogonal.


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33
The eigenvalues and corresponding eigenvectors for a symmetric matrix A are given. Find matrices D and P of an orthogonal diagonalization of A. 

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34
The eigenvalues and corresponding eigenvectors for a symmetric matrix A are given. Find matrices D and P of an orthogonal diagonalization of A. 

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35
The eigenvalues for the symmetric matrix A are given. Find matrices D and P of an orthogonal diagonalization of A. 

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36
The eigenvalues for the symmetric matrix A are given. Find matrices D and P of an orthogonal diagonalization of A. 

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37
Verify that the eigenvalues of
are nonnegative.



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38
Determine
from the given matrix with orthogonal columns without using row operations.



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39
Find the QR decomposition for the matrix
.

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40
Find the QR decomposition for the matrix
.

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41
If A is any matrix, then
is diagonalizable.

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42
If A is an
diagonalizable matrix, then there exists a diagonal matrix D and an orthogonal matrix P such that
.


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43
If Q is an orthogonal matrix, then
.

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44
If A is symmetric, and
and
with
, then
is orthogonal to
.





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45
If
is a QR factorization of a matrix A, then
.


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46
Find the singular values for the matrix
.

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47
Find the singular values for the matrix
.

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48
Find a singular value decomposition for the matrix
.

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49
Find a singular value decomposition for the matrix
.

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50
Find a singular value decomposition for the matrix
.

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51
Find a singular value decomposition for the matrix
.

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52
Express the given matrix
as an outer product expansion
, where
and
are the singular values of
.







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53
Express the given matrix
as an outer product expansion
, where
and
are the singular values of
.








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54
Determine the numerical rank of a
matrix with singular values
,
,
, and
, if
.






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55
Determine the numerical rank of a
matrix with singular values
,
,
,
, and
, if
.







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56
Every matrix A has a singular value decomposition.
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57
The singular value decomposition of a matrix A is unique.
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58
If D is a diagonal matrix with diagonal entries
, then the singular values of D are given by
.


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59
If
is a singular value decomposition for a matrix
, then
is an orthogonal diagonalizing matrix for
.




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60
If
is an invertible (square) matrix with singular value decomposition
, then a singular value decomposition for
is given by
.




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61
Find the vector in the subspace S that is closest to y.


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62
Find the vector in the subspace S that is closest to y.


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63
Find the normal equations for the given system.


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64
Find the normal equations for the given system.


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65
Find the least squares solution for the given system.


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66
Find the least squares solution for the given system.


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67
Find the least squares solution for the given system.


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68
Find all least squares solutions for the given system.


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69
Find an equation for the plane in
that best fits the given data.
,
,
,






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70
Find an equation for the line in
that best fits the given data.
,
,





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71
Every system of equations has at least one least squares solution.
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72
If a matrix A has linearly independent columns, then for every vector y there exists a unique least squares solution of
.

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73
If a system of equations has more variables than equations, then the system has infinitely many least squares solutions.
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74
If a system of equations has more equations than variables, then the system has a unique least squares solution.
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75
If
is a matrix and
is in
, then
is a least squares solution to
.





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