Difference between revisions of "031 Review Problems"
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− | '''12.''' Consider the matrix <math>A= | + | '''12.''' Consider the matrix <math style="vertical-align: -31px">A= |
\begin{bmatrix} | \begin{bmatrix} | ||
1 & -4 & 9 & -7 \\ | 1 & -4 & 9 & -7 \\ | ||
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\end{bmatrix}.</math> | \end{bmatrix}.</math> | ||
− | (a) List rank <math>A</math> and <math>\text{dim Nul }A.</math> | + | (a) List rank <math style="vertical-align: 0px">A</math> and <math style="vertical-align: 0px">\text{dim Nul }A.</math> |
− | (b) Find bases for <math>\text{Col }A</math> and <math>\text{Nul }A.</math> Find an example of a nonzero vector that belongs to <math>\text{Col }A,</math> as well as an example of a nonzero vector that belongs to <math>\text{Nul }A.</math> | + | (b) Find bases for <math style="vertical-align: 0px">\text{Col }A</math> and <math style="vertical-align: 0px">\text{Nul }A.</math> Find an example of a nonzero vector that belongs to <math style="vertical-align: -5px">\text{Col }A,</math> as well as an example of a nonzero vector that belongs to <math style="vertical-align: 0px">\text{Nul }A.</math> |
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</math> | </math> | ||
− | (a) Is <math>B</math> invertible? Explain. | + | (a) Is <math style="vertical-align: 0px">B</math> invertible? Explain. |
− | (b) Define a linear transformation <math>T</math> by the formula <math>T(\vec{x})=B\vec{x}.</math> Is <math>T</math> onto? Explain. | + | (b) Define a linear transformation <math style="vertical-align: 0px">T</math> by the formula <math style="vertical-align: -5px">T(\vec{x})=B\vec{x}.</math> Is <math style="vertical-align: 0px">T</math> onto? Explain. |
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− | '''15.''' Suppose <math>T</math> is a linear transformation given by the formula | + | '''15.''' Suppose <math style="vertical-align: 0px">T</math> is a linear transformation given by the formula |
::<math>T\Bigg( | ::<math>T\Bigg( | ||
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\end{bmatrix}</math> | \end{bmatrix}</math> | ||
− | (a) Find the standard matrix for <math>T.</math> | + | (a) Find the standard matrix for <math style="vertical-align: 0px">T.</math> |
− | (b) Let <math>\vec{u}=7\vec{e_1}-4\vec{e_2}.</math> Find <math>T(\vec{u}).</math> | + | (b) Let <math style="vertical-align: -5px">\vec{u}=7\vec{e_1}-4\vec{e_2}.</math> Find <math style="vertical-align: -6px">T(\vec{u}).</math> |
− | (c) Is <math>\begin{bmatrix} | + | (c) Is <math style="vertical-align: -21px">\begin{bmatrix} |
-1 \\ | -1 \\ | ||
3 | 3 | ||
− | \end{bmatrix}</math> in the range of <math>T?</math> Explain. | + | \end{bmatrix}</math> in the range of <math style="vertical-align: 0px">T?</math> Explain. |
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− | '''16.''' Let <math>A</math> and <math>B</math> be <math>6\times 6</math> matrices with <math>\text{det }A=-10</math> and <math>\text{det }B=5.</math> Use properties of determinants to compute: | + | '''16.''' Let <math style="vertical-align: 0px">A</math> and <math style="vertical-align: 0px">B</math> be <math style="vertical-align: 0px">6\times 6</math> matrices with <math style="vertical-align: -1px">\text{det }A=-10</math> and <math style="vertical-align: 0px">\text{det }B=5.</math> Use properties of determinants to compute: |
− | (a) <math>\text{det }3A</math> | + | (a) <math style="vertical-align: -2px">\text{det }3A</math> |
− | (b) <math>\text{det }(A^TB^{-1})</math> | + | (b) <math style="vertical-align: -7px">\text{det }(A^TB^{-1})</math> |
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− | '''17.''' Let <math>A= | + | '''17.''' Let <math style="vertical-align: -20px">A= |
\begin{bmatrix} | \begin{bmatrix} | ||
5 & 1 \\ | 5 & 1 \\ | ||
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\end{bmatrix}.</math> | \end{bmatrix}.</math> | ||
− | (a) Find a basis for the eigenspace(s) of <math>A.</math> | + | (a) Find a basis for the eigenspace(s) of <math style="vertical-align: 0px">A.</math> |
− | (b) Is the matrix <math>A</math> diagonalizable? Explain. | + | (b) Is the matrix <math style="vertical-align: 0px">A</math> diagonalizable? Explain. |
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\end{bmatrix}.</math> | \end{bmatrix}.</math> | ||
− | (a) Find a unit vector in the direction of <math>\vec{v}.</math> | + | (a) Find a unit vector in the direction of <math style="vertical-align: 0px">\vec{v}.</math> |
− | (b) Find the distance between <math>\vec{v}</math> and <math>\vec{y}.</math> | + | (b) Find the distance between <math style="vertical-align: 0px">\vec{v}</math> and <math style="vertical-align: -3px">\vec{y}.</math> |
− | (c) Let <math>L=\text{Span }\{\vec{v}\}.</math> Compute the orthogonal projection of <math>\vec{y}</math> onto <math>L.</math> | + | (c) Let <math style="vertical-align: -5px">L=\text{Span }\{\vec{v}\}.</math> Compute the orthogonal projection of <math style="vertical-align: -3px">\vec{y}</math> onto <math style="vertical-align: 0px">L.</math> |
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4 \\ | 4 \\ | ||
0 | 0 | ||
− | \end{bmatrix}</math> in <math>W^\perp?</math> Explain. | + | \end{bmatrix}</math> in <math style="vertical-align: 0px">W^\perp?</math> Explain. |
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'''20.''' | '''20.''' | ||
− | (a) Let <math>T:\mathbb{R}^2\rightarrow \mathbb{R}^2</math> be a transformation given by | + | (a) Let <math style="vertical-align: -2px">T:\mathbb{R}^2\rightarrow \mathbb{R}^2</math> be a transformation given by |
::<math>T\bigg( | ::<math>T\bigg( | ||
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\end{bmatrix}.</math> | \end{bmatrix}.</math> | ||
− | Determine whether <math>T</math> is a linear transformation. Explain. | + | Determine whether <math style="vertical-align: 0px">T</math> is a linear transformation. Explain. |
− | (b) Let <math>A= | + | (b) Let <math style="vertical-align: -19px">A= |
\begin{bmatrix} | \begin{bmatrix} | ||
1 & -3 & 0 \\ | 1 & -3 & 0 \\ | ||
-4 & 1 &1 | -4 & 1 &1 | ||
− | \end{bmatrix}</math> and <math>B= | + | \end{bmatrix}</math> and <math style="vertical-align: -32px">B= |
\begin{bmatrix} | \begin{bmatrix} | ||
2 & 1\\ | 2 & 1\\ | ||
1 & 0 \\ | 1 & 0 \\ | ||
-1 & 1 | -1 & 1 | ||
− | \end{bmatrix}.</math> Find <math>AB, | + | \end{bmatrix}.</math> Find <math style="vertical-align: -4px">AB,~BA^T</math> and <math style="vertical-align: 0px">A-B^T.</math> |
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− | '''21.''' Let <math>A= | + | '''21.''' Let <math style="vertical-align: -31px">A= |
\begin{bmatrix} | \begin{bmatrix} | ||
1 & 3 & 8 \\ | 1 & 3 & 8 \\ | ||
2 & 4 &11\\ | 2 & 4 &11\\ | ||
1 & 2 & 5 | 1 & 2 & 5 | ||
− | \end{bmatrix}.</math> Find <math>A^{-1}</math> if possible. | + | \end{bmatrix}.</math> Find <math style="vertical-align: 0px">A^{-1}</math> if possible. |
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'''23.''' | '''23.''' | ||
− | (a) Show that if <math>\vec{x}</math> is an eigenvector of the matrix <math>A</math> corresponding to the eigenvalue 2, then <math>\vec{x}</math> is an eigenvector of <math>A^3-A^2+I.</math> What is the corresponding eigenvalue? | + | (a) Show that if <math style="vertical-align: 0px">\vec{x}</math> is an eigenvector of the matrix <math style="vertical-align: 0px">A</math> corresponding to the eigenvalue 2, then <math style="vertical-align: 0px">\vec{x}</math> is an eigenvector of <math style="vertical-align: -2px">A^3-A^2+I.</math> What is the corresponding eigenvalue? |
− | (b) Show that if <math>\vec{y}</math> is an eigenvector of the matrix <math>A</math> corresponding to the eigenvalue 3 and <math>A</math> is invertible, then <math>\vec{y}</math> is an eigenvector of <math>A^{-1}.</math> What is the corresponding eigenvalue? | + | (b) Show that if <math style="vertical-align: -3px">\vec{y}</math> is an eigenvector of the matrix <math style="vertical-align: 0px">A</math> corresponding to the eigenvalue 3 and <math style="vertical-align: 0px">A</math> is invertible, then <math style="vertical-align: -3px">\vec{y}</math> is an eigenvector of <math style="vertical-align: 0px">A^{-1}.</math> What is the corresponding eigenvalue? |
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\end{bmatrix}.</math> | \end{bmatrix}.</math> | ||
− | Use the Diagonalization Theorem to find the eigenvalues of <math>A</math> and a basis for each eigenspace. | + | Use the Diagonalization Theorem to find the eigenvalues of <math style="vertical-align: 0px">A</math> and a basis for each eigenspace. |
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− | '''25.''' Give an example of a <math>3\times 3</math> matrix <math>A</math> with eigenvalues 5,-1 and 3. | + | '''25.''' Give an example of a <math style="vertical-align: 0px">3\times 3</math> matrix <math style="vertical-align: 0px">A</math> with eigenvalues 5,-1 and 3. |
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− | '''26.''' Assume <math>A^2=I.</math> Find <math>\text{Nul }A.</math> | + | '''26.''' Assume <math style="vertical-align: 0px">A^2=I.</math> Find <math style="vertical-align: -1px">\text{Nul }A.</math> |
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− | '''27.''' If <math>A</math> is an <math>n\times n</math> matrix such that <math>AA^T=I,</math> what are the possible values of <math>\text{det }A?</math> | + | '''27.''' If <math style="vertical-align: 0px">A</math> is an <math style="vertical-align: 0px">n\times n</math> matrix such that <math style="vertical-align: -4px">AA^T=I,</math> what are the possible values of <math style="vertical-align: 0px">\text{det }A?</math> |
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− | '''28.''' Show that if <math>\vec{x}</math> is an eigenvector of the matrix product <math>AB</math> and <math>B\vec{x}\ne \vec{0},</math> then <math>B\vec{x}</math> is an eigenvector of <math>BA.</math> | + | '''28.''' Show that if <math style="vertical-align: 0px">\vec{x}</math> is an eigenvector of the matrix product <math style="vertical-align: 0px">AB</math> and <math style="vertical-align: -5px">B\vec{x}\ne \vec{0},</math> then <math style="vertical-align: 0px">B\vec{x}</math> is an eigenvector of <math style="vertical-align: 0px">BA.</math> |
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'''29.''' | '''29.''' | ||
− | (a) Suppose a <math>6\times 8</math> matrix <math>A</math> has 4 pivot columns. What is <math>\text{dim Nul }A?</math> Is <math>\text{Col }A=\mathbb{R}^4?</math> Why or why not? | + | (a) Suppose a <math style="vertical-align: 0px">6\times 8</math> matrix <math style="vertical-align: 0px">A</math> has 4 pivot columns. What is <math style="vertical-align: -1px">\text{dim Nul }A?</math> Is <math style="vertical-align: -1px">\text{Col }A=\mathbb{R}^4?</math> Why or why not? |
− | (b) If <math>A</math> is a <math>7\times 5</math> matrix, what is the smallest possible dimension of <math>\text{Nul }A?</math> | + | (b) If <math style="vertical-align: 0px">A</math> is a <math style="vertical-align: 0px">7\times 5</math> matrix, what is the smallest possible dimension of <math style="vertical-align: -1px">\text{Nul }A?</math> |
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::<math>3x_1+5x_2=2k</math> | ::<math>3x_1+5x_2=2k</math> | ||
− | Find all real values of <math>k</math> such that the system has only one solution. | + | Find all real values of <math style="vertical-align: 0px">k</math> such that the system has only one solution. |
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− | '''31.''' Suppose <math>\{\vec{u},\vec{v}\}</math> is a basis of the eigenspace corresponding to the eigenvalue 0 of a <math>5\times 5</math> matrix <math>A.</math> | + | '''31.''' Suppose <math style="vertical-align: -5px">\{\vec{u},\vec{v}\}</math> is a basis of the eigenspace corresponding to the eigenvalue 0 of a <math style="vertical-align: 0px">5\times 5</math> matrix <math style="vertical-align: 0px">A.</math> |
− | (a) Is <math>\vec{w}=\vec{u}-2\vec{v}</math> an eigenvector of <math>A?</math> If so, find the corresponding eigenvalue. | + | (a) Is <math style="vertical-align: 0px">\vec{w}=\vec{u}-2\vec{v}</math> an eigenvector of <math style="vertical-align: 0px">A?</math> If so, find the corresponding eigenvalue. |
If not, explain why. | If not, explain why. | ||
− | (b) Find the dimension of <math>\text{Col }A.</math> | + | (b) Find the dimension of <math style="vertical-align: -1px">\text{Col }A.</math> |
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Latest revision as of 13:11, 25 August 2017
This is a list of sample problems and is meant to represent the material usually covered in Math 31. An actual test may or may not be similar.
1. True or false: If all the entries of a matrix are then must be
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2. True or false: If a matrix is diagonalizable, then the matrix must be diagonalizable as well.
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3. True or false: If is a matrix with characteristic equation then is diagonalizable.
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4. True or false: If is invertible, then is diagonalizable.
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5. True or false: If and are invertible matrices, then so is
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6. True or false: If is a matrix and then is consistent for all in
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7. True or false: Let for matrices and If is invertible, then is invertible.
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8. True or false: Let be a subspace of and be a vector in If and then
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9. True or false: If is an invertible matrix, and and are matrices such that then
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10.
(a) Is the matrix diagonalizable? If so, explain why and diagonalize it. If not, explain why not.
(b) Is the matrix diagonalizable? If so, explain why and diagonalize it. If not, explain why not.
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11. Find the eigenvalues and eigenvectors of the matrix
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12. Consider the matrix and assume that it is row equivalent to the matrix
(a) List rank and
(b) Find bases for and Find an example of a nonzero vector that belongs to as well as an example of a nonzero vector that belongs to
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13. Find the dimension of the subspace spanned by the given vectors. Are these vectors linearly independent?
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14. Let
(a) Is invertible? Explain.
(b) Define a linear transformation by the formula Is onto? Explain.
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15. Suppose is a linear transformation given by the formula
(a) Find the standard matrix for
(b) Let Find
(c) Is in the range of Explain.
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16. Let and be matrices with and Use properties of determinants to compute:
(a)
(b)
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17. Let
(a) Find a basis for the eigenspace(s) of
(b) Is the matrix diagonalizable? Explain.
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18. Let and
(a) Find a unit vector in the direction of
(b) Find the distance between and
(c) Let Compute the orthogonal projection of onto
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19. Let Is in Explain.
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20.
(a) Let be a transformation given by
Determine whether is a linear transformation. Explain.
(b) Let and Find and
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21. Let Find if possible.
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22. Find a formula for by diagonalizing the matrix.
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23.
(a) Show that if is an eigenvector of the matrix corresponding to the eigenvalue 2, then is an eigenvector of What is the corresponding eigenvalue?
(b) Show that if is an eigenvector of the matrix corresponding to the eigenvalue 3 and is invertible, then is an eigenvector of What is the corresponding eigenvalue?
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24. Let
Use the Diagonalization Theorem to find the eigenvalues of and a basis for each eigenspace.
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25. Give an example of a matrix with eigenvalues 5,-1 and 3.
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26. Assume Find
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27. If is an matrix such that what are the possible values of
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28. Show that if is an eigenvector of the matrix product and then is an eigenvector of
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29.
(a) Suppose a matrix has 4 pivot columns. What is Is Why or why not?
(b) If is a matrix, what is the smallest possible dimension of
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30. Consider the following system of equations.
Find all real values of such that the system has only one solution.
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31. Suppose is a basis of the eigenspace corresponding to the eigenvalue 0 of a matrix
(a) Is an eigenvector of If so, find the corresponding eigenvalue.
If not, explain why.
(b) Find the dimension of
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