14-240/Tutorial-October28: Difference between revisions

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more efficient strategy to solve a problem.
more efficient strategy to solve a problem.




====Extending a Linearly Independent Set to Basis====
====Extending a Linearly Independent Set to Basis====
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'''Consider the following strategy only if you are working with concrete sets and vector spaces:'''
'''Consider the following strategy only if you are working with concrete sets and vector spaces:'''



If a problem requires us to extend a linearly independent subset of a vector space to a basis, then the easiest approach is to
If a problem requires us to extend a linearly independent subset of a vector space to a basis, then the easiest approach is to
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Let <math>S = \{(-3, -6, 0), (0, 7, 0)\}</math> be a subset of <math>R^3</math>. To extend <math>S</math> to a basis of
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>


<math>R^3</math>, add vectors from <math>\{(1, 0, 0), (0, 1, 0), (0, 0, 1)\}</math> that preserve linear independence.
to a basis of <math>R^3</math>, add vectors from <math>\{(1, 0, 0), (0, 1, 0), (0, 0, 1)\}</math> that preserve linear independence.




'''Adding the vectors''':
Adding the vectors:


We see that both vectors in <math>S</math> have a <math>0</math> as the third component so a safe choice of a vector to add is <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>.
We see that both vectors in <math>S</math> have a <math>0</math> as the third component so a safe choice of a vector to add is <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>.

Revision as of 14:04, 7 November 2014

Boris

Try to Avoid the Einstellung Effect

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 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 to solve a problem.


Extending a Linearly Independent Set to Basis

Consider the following strategy only if you are working with concrete sets and vector spaces:

If a problem requires us to extend a linearly independent subset of a vector space 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 of , add vectors from that preserve linear independence.


Adding the vectors:

We see that both vectors in have a as the third component so a safe choice of a vector to add is . Since has a dimension of , then is a basis of .

Nikita