Proving a transformation is not linear

In summary, the conversation discusses the requirement to determine the linearity of a certain transformation T, given the known property that T(x+y) = T(x) + T(y). It is mentioned that this transformation is not linear due to not satisfying the degree-1 homogeneity property of all linear maps. Various counterexamples are proposed, including one involving a map from the complex field to the real field and one involving a map from a real vector space to itself. The conversation concludes that the latter example is valid for determining non-linearity.
  • #1
Bipolarity
776
2
For a certain transformation T, it is known that [itex] T(x+y) = T(x) + T(y) [/itex]

It is required to determine whether this transformation is linear. Obviously it is not, since it need not satisfy the degree-1 homogeneity property of all linear maps.

I'm just having trouble cooking up the counterexample. Any ideas? Thanks!

BiP
 
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  • #2
Is ##T:\mathbb{R}\rightarrow \mathbb{R}##?
And are there other things known about ##T## such as continuity?

If ##T:\mathbb{R}\rightarrow \mathbb{R}## and it is indeed true that the only thing you know is ##T(x + y) = T(x) + T(y)## then there is a counterexample for linearity. This is not an easy counterexample however. It consist in looking at ##\mathbb{R}## as a ##\mathbb{Q}##-vector space. As such, we have a basis ##\{e_i\}_{i\in I}## of ##\mathbb{R}##. So any element ##x\in \mathbb{R}## can be written uniquely as

[tex]x = \sum_{i\in I}\alpha_i e_i[/tex]

for some rational numbers ##\alpha_i\in \mathbb{Q}## such that only finitely many ##\alpha_i## are nonzero. Now take ##j\in I## fixed an consider

[tex]T(x) = \alpha_j[/tex]

this map satisfies ##T(x+y) = T(x) + T(y)##, but not ##T(\lambda x) = \lambda T(x)## for all ##\lambda \in \mathbb{R}## (it does satisfy it for ##\lambda in \mathbb{Q}## though).
 
  • #3
I see. What about the following transformation [itex] T: ℂ → ℝ [/itex] where [itex] T(x) = Re(x) [/itex]. If the field is the complex field, then the scalar [itex] i [/itex] causes homogeneity to fail. Additivity is trivially shown.

Is this valid?

BiP
 
  • #4
If you have a map ##T:V\rightarrow W## then it's assumed that both maps have the same scalar field (otherwise linear makes no sense). So I assume that both ##\mathbb{C}## and ##\mathbb{R}## are ##\mathbb{R}##-vector spaces. But then the map you mention is linear.
 
  • #5
micromass said:
If you have a map ##T:V\rightarrow W## then it's assumed that both maps have the same scalar field (otherwise linear makes no sense). So I assume that both ##\mathbb{C}## and ##\mathbb{R}## are ##\mathbb{R}##-vector spaces. But then the map you mention is linear.

I'm sorry, if I redefined the transformation as:
##T:ℂ\rightarrow ℂ## then is my example valid?

BiP
 
  • #6
Bipolarity said:
I'm sorry, if I redefined the transformation as:
##T:ℂ\rightarrow ℂ## then is my example valid?

BiP

Yes, that would be a valid example.
 

1. What is the definition of a linear transformation?

A linear transformation is a mathematical function that maps vectors from one vector space to another while preserving the operations of vector addition and scalar multiplication.

2. How can you prove that a transformation is not linear?

A transformation can be proven to be not linear by showing that it does not satisfy one or more of the properties of a linear transformation. These properties include preserving vector addition, preserving scalar multiplication, and preserving the zero vector.

3. What does it mean if a transformation does not preserve vector addition?

If a transformation does not preserve vector addition, it means that the output of the transformation for the sum of two vectors is not equal to the sum of the outputs of the transformation for each individual vector.

4. How do you show that a transformation does not preserve scalar multiplication?

To show that a transformation does not preserve scalar multiplication, you can provide a counterexample where the output of the transformation for a scaled vector is not equal to the scaled output of the transformation for the original vector.

5. What is an example of a transformation that is not linear?

An example of a transformation that is not linear is a squaring function, where the output of the transformation for the sum of two vectors is not equal to the sum of the outputs of the transformation for each individual vector. For example, squaring the sum of 2 and 3 results in 25, but squaring 2 and 3 individually and then summing the results results in 13.

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