23.07.2012 Views

Linear Algebra

Linear Algebra

Linear Algebra

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

Section III. Basis and Dimension 127<br />

So the instructions for the prior example are “transpose, reduce, and transpose<br />

back”.<br />

We can even, at the price of tolerating the as-yet-vague idea of vector spaces<br />

being “the same”, use Gauss’ method to find bases for spans in other types of<br />

vector spaces.<br />

3.9 Example To get a basis for the span of {x 2 + x 4 , 2x 2 +3x 4 , −x 2 − 3x 4 }<br />

in the space P4, think of these three polynomials as “the same” as the row<br />

vectors � 0 0 1 0 1 � , � 0 0 2 0 3 � , and � 0 0 −1 0 −3 � , apply<br />

Gauss’ method<br />

⎛<br />

0<br />

⎝0 0<br />

0<br />

0<br />

0<br />

1<br />

2<br />

−1<br />

0<br />

0<br />

0<br />

⎞<br />

1<br />

3 ⎠<br />

−3<br />

−2ρ1+ρ2 2ρ2+ρ3<br />

−→ −→<br />

ρ1+ρ3<br />

⎛<br />

0<br />

⎝0 0<br />

0<br />

1<br />

0<br />

0<br />

0<br />

⎞<br />

1<br />

1⎠<br />

0 0 0 0 0<br />

and translate back to get the basis 〈x 2 + x 4 ,x 4 〉. (As mentioned earlier, we will<br />

make the phrase “the same” precise at the start of the next chapter.)<br />

Thus, our first point in this subsection is that the tools of this chapter give<br />

us a more conceptual understanding of Gaussian reduction.<br />

For the second point of this subsection, consider the effect on the column<br />

space of this row reduction.<br />

� �<br />

1 2 −2ρ1+ρ2<br />

−→<br />

2 4<br />

� �<br />

1 2<br />

0 0<br />

The column space of the left-hand matrix contains vectors with a second component<br />

that is nonzero. But the column space of the right-hand matrix is different<br />

because it contains only vectors whose second component is zero. It is this<br />

knowledge that row operations can change the column space that makes next<br />

result surprising.<br />

3.10 Lemma Row operations do not change the column rank.<br />

Proof. Restated, if A reduces to B then the column rank of B equals the<br />

column rank of A.<br />

We will be done if we can show that row operations do not affect linear relationships<br />

among columns (e.g., if the fifth column is twice the second plus the<br />

fourth before a row operation then that relationship still holds afterwards), because<br />

the column rank is just the size of the largest set of unrelated columns. But<br />

this is exactly the first theorem of this book: in a relationship among columns,<br />

⎛ ⎞<br />

a1,1<br />

⎜ a2,1 ⎟<br />

⎜ ⎟<br />

c1 · ⎜ .<br />

⎝ .<br />

⎟<br />

. ⎠ + ···+ cn<br />

⎛ ⎞<br />

a1,n<br />

⎜ a2,n ⎟<br />

⎜ ⎟<br />

· ⎜ .<br />

⎝ .<br />

⎟<br />

. ⎠ =<br />

⎛ ⎞<br />

0<br />

⎜<br />

⎜0<br />

⎟<br />

⎜.<br />

⎝.<br />

⎟<br />

. ⎠<br />

0<br />

am,1<br />

am,n<br />

row operations leave unchanged the set of solutions (c1,... ,cn). QED

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!