Note: the two proofs are not the same!

In summary, a linear transformation L: V -> W satisfies the conditions L(u+v) = L(u) + L(v) and L(ku) = kL(u) for all vectors u and v in V and scalars k. This is equivalent to the condition L(au + bv) = aL(u) + bL(v) for any scalars a and b and vectors u and v in V. Proving one of these conditions implies the other.
  • #1
franz32
133
0
How will I prove that...
Show that L: V -> W is a linear transformation if and only if
L(au + bv) = aL(u) + bL(v) for any scalars a and b and and any
vectors u and v in V.

For L(au +bv), this is my proof. (Is this wrong?)

L(au + bv) = L [ a(a', b', c') + b(a'', b'', c'')]
= L [ aa' + ba'', ab' + bb'', ac' + bc'' ]
= (aa' + ab' + ac') + ( ba" + bb" +bc")
= a(a' + b' +c') =b(a" + b" + c")
= aL(u) + bL(v)
 
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  • #2
I've never studied what you're doing formally, but I've always thought that what you're "proving" is the definition of a linear transformation. Why would you have to prove an arbitrary definition?

cookiemonster
 
  • #3
Here's...

Yeah that's right. It's an arbitrary definition of the linear transformation. My professor wants me to do it...

In the textbook I'm using, it looks like this

1. L(u+v) = L(u) + L(v)
2. L(ku) = kL(u)
 
  • #4
Well, since it's an arbitrary definition, I don't really see the point of "proving" it.

The only thing I can imagine him doing is asking you to combine the two conditions as it's usually stated. I usually see it in this form:

[tex]L[\boldsymbol{v_1}+\boldsymbol{v_2}] = L[\boldsymbol{v_1}]+L[\boldsymbol{v_2}][/tex]
[tex]L[a\boldsymbol{v}] = aL[\boldsymbol{v}][/tex]

My guess is that he wants to see you combine these.

Edit: Just noticed you typed the form yourself. Guess I should read a little more slowly next time...

cookiemonster
 
  • #5
I'm with cookiemonster. There is no such thing as proving a definition aside from showing the entry in a dictionary.
 
  • #6
Show that L: V -> W is a linear transformation if and only if
L(au + bv) = aL(u) + bL(v) for any scalars a and b and and any
vectors u and v in V.

Well, as has been pointed out,

"L: V -> W is a linear transformation" means
1. L(u+v) = L(u) + L(v)
2. L(ku) = kL(u)
for all vectors u and v in V and scalars k.

The problem is asking you to prove

L: V -> W is a linear transformation

if and only if

L(au + bv) = aL(u) + bL(v) for any scalars a and b and and any
vectors u and v in V.


So, you start with the assumption that "L: V -> W is a linear transformation" then prove "L(au + bv) = aL(u) + bL(v) for any scalars a and b and and any vectors u and v in V."

Then, (as a separate piece of work!) you start with the assumption "L(au + bv) = aL(u) + bL(v) for any scalars a and b and and any vectors u and v in V." and prove "L: V -> W is a linear transformation".
 

FAQ: Note: the two proofs are not the same!

What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space into another, preserving the basic structure of the space. It is represented by a matrix and follows the rules of linearity, such as preserving addition and scalar multiplication.

How do you prove that a transformation is linear?

To prove that a transformation is linear, you need to show that it follows the rules of linearity. This means that the transformation must preserve addition and scalar multiplication, and the transformation of any linear combination of vectors must equal the same linear combination of the transformed vectors.

What are the steps to proving a linear transformation?

The steps to proving a linear transformation are as follows:

  1. State the transformation and its properties (e.g. addition and scalar multiplication).
  2. Show that the transformation preserves addition by plugging in two vectors and showing that the transformation of their sum equals the sum of their transformations.
  3. Show that the transformation preserves scalar multiplication by plugging in a vector and a scalar, and showing that the transformation of the scaled vector equals the scalar multiplied by the transformation of the original vector.
  4. Show that the transformation of a linear combination of vectors equals the same linear combination of the transformed vectors.
  5. Conclude that the transformation is indeed linear.

What are some common examples of linear transformations?

Some common examples of linear transformations include rotations, reflections, shear, and scaling. These can be represented by matrices and follow the rules of linearity.

How are linear transformations used in real life?

Linear transformations are used in many fields, such as computer graphics, physics, economics, and engineering. They are used to model and solve problems that involve linear relationships, such as predicting the growth of a population or calculating the trajectory of a projectile.

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