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==Boris== |
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==Boris== |
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====Try to Avoid the Einstellung Effect==== |
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====Be Efficient==== |
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By this point in the course, we become good at solving systems of linear equations. However, we should not use this same |
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By this point in the course, we become good at solving systems of linear equations. However, we should not use this same old problem-solving strategy over and over if a more efficient one exists. Consider the following problems: |
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old problem-solving strategy over and over if a more efficient one exists. Consider the following problems: |
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Yet there is a less time-consuming approach that relies on two observations: |
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Yet there is a less time-consuming approach that relies on two observations: |
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:(1) The dimension of <math>R^3</math> is <math>3</math> so the size of a basis is also <math>3</math>. |
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:(2) No linearly independent set can have more vectors than a generating set (by the Replacement Theorem). |
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Since a basis is a generating set and the size of <math>S</math> is <math>4</math>, then the Replacement Theorem tells us that |
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<math>S</math> cannot be linearly independent. Hence, the problem can be solved without solving any linear equations. |
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::(1) The dimension of <math>R^3</math> is <math>3</math> so the size of a basis is also <math>3</math>. |
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::(2) No linearly independent set can have more vectors than a generating set (by the Replacement Theorem). |
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'''Q2''': Determine if the polynomials <math>x^3 - 2x ^2 + 1, 4x^2-x+3, 3x-2 </math> generate <math>P_3(R)</math>. |
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Since a basis is a generating set and the size of <math>S</math> is <math>4</math>, then the Replacement Theorem tells us that |
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<math>S</math> cannot be linearly |
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Once again, we can solve a linear equation but we do not have to. Observe: |
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independent. Hence, the problem can be solved without solving any linear equations. |
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:(1) The dimension <math>P_3(R)</math> is <math>4</math> so the size of a basis is also <math>4</math>. |
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:(2) No generating set can have fewer vectors than a basis (by a Corollary to the Replacement Theorem). |
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Since there are only <math>3</math> polynomials, then the Corollary tells us that it cannot generate <math>P_3(R)</math> . Once again, we used a more efficient strategy. |
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====Extending a Linearly Independent Set to a Basis==== |
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'''Boris's tip (for concrete sets and vector spaces only)''': |
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'''Q2''': Determine if the polynomials <math>x^3 - 2x ^2 + 1, 4x^2-x+3, 3x-2 </math> generate <math>P_3(R)</math>. |
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Once again, we can solve a linear linear equation but we do not have to. Observe: |
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If a problem requires us to extend a linearly independent set to a basis, then the easiest approach is to add vectors from the |
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::(1) The dimension <math>P_3(R)</math> is <math>4</math> so the size of a basis is also <math>4</math>. |
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standard ordered basis. Here is an example: |
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::(2) No generating set can have fewer vectors than a basis (by a Corollary to the Replacement Theorem). |
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Let <math>S = \{(-3, -6, 0), (0, 7, 0)\}</math> be a linearly independent subset of <math>R^3</math>. To extend <math>S</math> to a basis, add vectors from <math>\{(1, 0, 0), (0, 1, 0), (0, 0, 1)\}</math>. The only question is which vector(s) should we add? |
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Since there are only <math>3</math> polynomials, then the Corollary tells us that there is no way that it generates <math>P_3(R)</math>. |
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====Don't be Too Lazy==== |
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We see that both vectors in <math>S</math> have a <math>0</math> as the third component so a safe choice is to add <math>(0, 0, 1)</math>. Since <math>R^3</math> has a dimension of <math>3</math>, then <math>\{(-3, -6, 0), (0, 7, 0), (0, 0, 1)\}</math> is a basis of <math>R^3</math>. |
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====Extending Linearly Independent Sets==== |
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==Nikita== |
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==Nikita== |
Welcome to Math 240! (additions to this web site no longer count towards good deed points)
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#
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Week of...
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Notes and Links
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1
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Sep 8
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About This Class, What is this class about? (PDF, HTML), Monday, Wednesday
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2
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Sep 15
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HW1, Monday, Wednesday, TheComplexField.pdf,HW1_solutions.pdf
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3
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Sep 22
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HW2, Class Photo, Monday, Wednesday, HW2_solutions.pdf
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4
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Sep 29
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HW3, Wednesday, Tutorial, HW3_solutions.pdf
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5
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Oct 6
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HW4, Monday, Wednesday, Tutorial, HW4_solutions.pdf
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6
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Oct 13
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No Monday class (Thanksgiving), Wednesday, Tutorial
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7
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Oct 20
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HW5, Term Test at tutorials on Tuesday, Wednesday
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8
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Oct 27
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HW6, Monday, Why LinAlg?, Wednesday, Tutorial
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9
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Nov 3
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Monday is the last day to drop this class, HW7, Monday, Wednesday, Tutorial
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10
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Nov 10
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HW8, Monday, Tutorial
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11
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Nov 17
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Monday-Tuesday is UofT November break
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12
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Nov 24
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HW9
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13
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Dec 1
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Wednesday is a "makeup Monday"! End-of-Course Schedule, Tutorial
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F
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Dec 8
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The Final Exam
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Register of Good Deeds
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Add your name / see who's in!
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Boris
Be Efficient
By this point in the course, we become good at solving systems of linear equations. However, we should not use this same old problem-solving strategy over and over if a more efficient one exists. Consider the following problems:
Q1: Determine if is linearly independent in .
We can solve this linear equation to find the answer:
- where .
Yet there is a less time-consuming approach that relies on two observations:
- (1) The dimension of is so the size of a basis is also .
- (2) No linearly independent set can have more vectors than a generating set (by the Replacement Theorem).
Since a basis is a generating set and the size of is , then the Replacement Theorem tells us that
cannot be linearly independent. Hence, the problem can be solved without solving any linear equations.
Q2: Determine if the polynomials generate .
Once again, we can solve a linear equation but we do not have to. Observe:
- (1) The dimension is so the size of a basis is also .
- (2) No generating set can have fewer vectors than a basis (by a Corollary to the Replacement Theorem).
Since there are only polynomials, then the Corollary tells us that it cannot generate . Once again, we used a more efficient strategy.
Extending a Linearly Independent Set to a Basis
Boris's tip (for concrete sets and vector spaces only):
If a problem requires us to extend a linearly independent set to a basis, then the easiest approach is to add vectors from the
standard ordered basis. Here is an example:
Let be a linearly independent subset of . To extend to a basis, add vectors from . The only question is which vector(s) should we add?
We see that both vectors in have a as the third component so a safe choice is to add . Since has a dimension of , then is a basis of .
Nikita