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Section 16.6 More examples

Before concluding this chapter, we’ll illustrate the uses of Proposition 16.5.6 with two examples.

Example 16.6.1. Recognizing when two subspaces are the same.

Consider the sets of vectors S={(1,0,0),(0,1,0)} and S={(1,1,0),(1,0,0),(1,1,0)} in R3. It should be clear that SpanS is the xy-plane in R3. Does SpanS generate the same subspace?
To answer this question, we use Statement 2 of Proposition 16.5.6, which gives us two new questions to answer.
  • Can each vector in S be expressed as a linear combination of the vectors in S? Yes, because
    [100]=0[110]+1[100]+0[110],[010]=12[110]+0[100]+(12)[110].
  • Can each vector in S be expressed as a linear combination of the vectors in S? Yes, because
    [110]=1[100]+1[010],[100]=1[100]+0[010],[110]=1[100]+(1)[010].
Since both questions have been answered in the affirmative, Statement 2 of Proposition 16.5.6, tells us that SpanS and SpanS are the same space.

Example 16.6.2. Determining if a spanning set generates the whole vector space.

Consider the set of vectors S={A1,A2,A3,A4} in M2(R), where
A1=[0121],A3=[0120],A2=[1241],A4=[0012].
Is this set a spanning set for all of M2(R)? That is, is M2(R)=SpanS? We already know a spanning set for M2(R) — the set of standard basis vectors B={E11,E12,E21,E22}, where
E11=[1000],E12=[0100],E21=[0010],E22=[0001].
That is, we already know that M2(R)=SpanB. So we can turn our question into: is SpanS=SpanB? With this new version of our problem, we can use the same method as in the previous example. However, we don’t need to explicitly verify that each vector in S can be expressed as a linear combination of the vectors in B. Besides being obvious, this fact is already implied by our assertion that M2(R)=SpanB, since clearly each vector in S is a vector in M2(R). So it just remains to verify that each vector in B can be expressed as a linear combination of the vectors in S. Let’s begin with vector E11. We use the same strategy as in the examples in Subsection 16.4.3: express E11 as a linear combination of the vectors in S with unknown scalar coefficients, set up equations in those unknown scalars, and determine whether the resulting linear system is consistent.
[1000]=k1[0121]+k2[1241]+k3[0120]+k4[0012]=[k2k1+2k2+k32k1+4k22k3+k4k1k22k4]
Comparing entries on left and right sides leads to the system of equations
{k2=1,k1+2k2+k3=0,2k1+4k22k3+k4=0,k1k22k4=0,
which can be put in an augmented matrix and reduced.
[01001121002421011020]reducerow[1000150100100101700018]
The reduced augmented matrix above tells us that
k1=15,k2=1,k3=17,k4=8,
and so
[1000]=15[0121]+[1241]17[0120]8[0012],
though we only cared about the existence of a solution, not the actual solution itself.
In a similar manner, one can calculate that
[0100]=4[0121]+5[0120]+2[0012],[0100]=4[0121]+5[0120]+2[0012],[0010]=2[0121]+2[0120]+[0012],[0001]=[0121]+[0120].
We have now verified that each vector in B can be expressed as a linear combination of the vectors in S. As discussed above, we already knew that each vector in S can be expressed as a linear combination of the vectors in B. Therefore, Statement 2 of Proposition 16.5.6 tells us that SpanS=SpanB. Since we already knew that SpanB is equal to the entire space M2(R), we must also have SpanS equal to this entire space.