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228 Eigenvalues and Eigenvectors<br />

It was picked at random by choosing a pair of vectors L(e 1 ) and L(e 2 ) as<br />

the outputs of L acting on the canonical basis vectors. Notice how the unit<br />

square with a corner at the origin is mapped to a parallelogram. The second<br />

line of the picture shows these superimposed on one another. Now look at the<br />

second picture on that line. There, two vectors f 1 and f 2 have been carefully<br />

chosen such that if the inputs into L are in the parallelogram spanned by f 1<br />

and f 2 , the outputs also form a parallelogram with edges lying along the same<br />

two directions. Clearly this is a very special situation that should correspond<br />

to interesting properties of L.<br />

Now lets try an explicit example to see if we can achieve the last picture:<br />

Example 126 Consider the <strong>linear</strong> transformation L such that<br />

( ( ) ( ( 1 −4 0 3<br />

L = and L = ,<br />

0)<br />

−10 1)<br />

7)<br />

so that the matrix of L in the standard basis is<br />

( ) −4 3<br />

.<br />

−10 7<br />

( ( 1 0<br />

Recall that a vector is a direction and a magnitude; L applied to or changes<br />

0)<br />

1)<br />

both the direction and the magnitude of the vectors given to it.<br />

Notice that ( ( ) ( 3 −4 · 3 + 3 · 5 3<br />

L =<br />

= .<br />

5)<br />

−10 · 3 + 7 · 5 5)<br />

228

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