R-Linear and C-Linear Mappings.... Another Question ....

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In summary, Peter is trying to understand what linearity means and what are the two conditions for a function to be linear. He is able to apply linearity to a function on its complex conjugate, and is able to answer a question about linearity.
  • #1
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I am reading Reinhold Remmert's book "Theory of Complex Functions" ...

I am focused on Chapter 0: Complex Numbers and Continuous Functions ... and in particular on Section 1.2:##\mathbb{R}##-linear and ##\mathbb{C}##]-linear mappings of ##\mathbb{C}## into ##\mathbb{C}## ... ...

I need help in order to fully understand one of Remmert's results regarding ##\mathbb{C}##-linear mappings of ##\mathbb{C}## into ##\mathbb{C}## ... ...

Remmert's section on ##\mathbb{R}##-linear and ##\mathbb{C}##-linear mappings of ##\mathbb{C}## into ##\mathbb{C}## reads as follows:
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In the above text by Remmert we read the following: (... fairly near the start of the text ...)

" ... ... An ## \mathbb{R}##-linear mapping ##T : \mathbb{C} \to \mathbb{C}## is then ##\mathbb{C}##-linear when ##T(i) = i T(1)##; in this case it has the form ##T(z) = T(1) z##. ... ... "My questions are as follows:Question 1

How/why exactly is an ## \mathbb{R}##-linear mapping ##T : \mathbb{C} \to \mathbb{C}## also ##\mathbb{C}##-linear when ##T(i) = i T(1)## ... ... ?
Question 2

Why/how exactly does a ##\mathbb{C}##-linear mapping have the form ##T(z) = T(1) z## ... ...
Hope someone can help ...

Peter
 

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  • #2
Hi Peter!

What does linearity mean? Can you write down the two conditions for a function ##T## to be, say ##\mathbb{F}-##linear?

If so, can you apply it in the case ##\mathbb{F}=\mathbb{C}## on ##T(z\cdot 1)\,?##
This should answer your second question.
Next apply the case ##\mathbb{F}=\mathbb{R}## on ##T(a\cdot 1 + b \cdot i)\,.##
This should answer your first question.
 
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  • #3
The condition for a function ##T : \mathbb{C} \to \mathbb{C}## to be ##\mathbb{F}##]-linear are as follows:

Additivity

##T(w + z) = T(w) + T(z)## for all ##w,z \in \mathbb{C}## ... ... ... (1)

Homogeneity

##T( \lambda w ) = \lambda T(w)## for all ##\lambda \in \mathbb{F}## and ##w \in \mathbb{C}## ... ... ... (2)Now apply the above to ##\mathbb{F} = \mathbb{C}## on ##T( z \cdot 1 )##

Then ... we have ... ##T( z \cdot 1 ) = z T(1)##

But ... ##T( z \cdot 1 ) = T(z)## ...

So ... ##T(z) = z T(1) = T(1) z## ... so, question 2 above is answered ... ...Now try to get answer to question 1 ...

Let ##z = a + b i## ... and ##\mathbb{F} = \mathbb{R}## ...

Then ##T(z) = T(a \cdot 1) = a T(1) + b T(i)## ... ...

BUT ... where to from here ...?Can you help further ...

Peter
 
  • #4
Math Amateur said:
The condition for a function ##T : \mathbb{C} \to \mathbb{C}## to be ##\mathbb{F}##]-linear are as follows:

Additivity

##T(w + z) = T(w) + T(z)## for all ##w,z \in \mathbb{C}## ... ... ... (1)

Homogeneity

##T( \lambda w ) = \lambda T(w)## for all ##\lambda \in \mathbb{F}## and ##w \in \mathbb{C}## ... ... ... (2)

Now try to get answer to question 1 ...

Let ##z = a + b i## ... and ##\mathbb{F} = \mathbb{R}## ...

Then ##T(z) = T(a \cdot 1) = a T(1) + b T(i)## ... ...
Firstly, here is a typo: ##T(z) = T(z \cdot 1)## but this isn't of interest in the first step.

We have for ##z=a+ib \in \mathbb{C}## that ##a,b\in \mathbb{R}##. Now ##T## is ##\mathbb{R}-##linear, so we apply first additivity and get ##T(z)=T(a+ib)=T(a)+T(ib)##. This might be confusing, as one of the summands isn't real. However, we have the ##\mathbb{R}-##linearity on the vector space ##\mathbb{R}^2\cong \mathbb{C}##, that is ##T((a,b))=T((a,0))+T((0,b))## and the difference between ##(a,b)## and ##a+ib## is merely a notation one.

Next we apply homogeneity which is allowed, because ##a,b \in \mathbb{R}##. Thus we get ##T(z)=\ldots = T(a \cdot 1)+ T(b \cdot i)=a\cdot T(1) + b\cdot T(i)##. Here we are stuck, but we have an additional condition from question 1
Question 1

How/why exactly is an ## \mathbb{R}##-linear mapping ##T : \mathbb{C} \to \mathbb{C}## also ##\mathbb{C}##-linear when ##T(i) = i T(1)## ... ... ?
With that, we have ##T(z)=\ldots =a\cdot T(1) + b\cdot T(i) = a \cdot T(1) + b\cdot i\cdot T(1)= (a+bi)\cdot T(1)= z\cdot T(1)## which is ##\mathbb{C}-##homogeneity. From this we also get ##T(z_1+z_2)=T(z_1)+T(z_2)## since the complex addition goes componentwise: the ##a_j## add and the ##b_j## do, and we can proceed in the same way.

Both together is ##\mathbb{C}-##linearity.

And from the answer to question 1 we see, that it also holds in the opposite direction: If ##T## is ##\mathbb{C}-##linear, then ##T(i)=i \cdot T(1)##, simply use ##z=i\,.##
 
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  • #5
Thanks fresh_42 ...

I appreciate your help ...

Peter
 

1. What is the difference between R-Linear and C-Linear mappings?

R-Linear mappings are mappings between two real vector spaces, while C-Linear mappings are mappings between two complex vector spaces. The main difference is that C-Linear mappings preserve both addition and scalar multiplication with complex numbers, while R-Linear mappings only preserve addition and scalar multiplication with real numbers.

2. How do I determine if a mapping is R-Linear or C-Linear?

To determine if a mapping is R-Linear, you would need to check if it preserves addition and scalar multiplication with real numbers. To determine if a mapping is C-Linear, you would need to check if it preserves addition and scalar multiplication with both real and complex numbers.

3. Are R-Linear and C-Linear mappings the same as linear transformations?

Yes, R-Linear and C-Linear mappings are types of linear transformations. Linear transformations are mappings between vector spaces that preserve addition and scalar multiplication.

4. Can a mapping be both R-Linear and C-Linear?

Yes, a mapping can be both R-Linear and C-Linear. This means that the mapping preserves addition and scalar multiplication with both real and complex numbers.

5. What are some applications of R-Linear and C-Linear mappings?

R-Linear and C-Linear mappings have many applications in mathematics, physics, and engineering. They are used in fields such as quantum mechanics, signal processing, and computer graphics. They are also fundamental in understanding and solving systems of linear equations and matrices.

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