Equality of Two Linear Mappings

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In summary, we are trying to prove that two linear mappings, ##L_1## and ##L_2##, are equal. To do this, we assume that ##L_1(h) - L_2(h) = 0## and subtract the two equations from each other. By linearity, we can then show that the limit of this expression is equal to 0. Simplifying, we find that ##L_1(1) = L_2(1)##, which implies that ##L_1(x) = L_2(x)## for all ##x \in \mathbb{R}##.
  • #1
Parmenides
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Let ##L_1## and ##L_2## be two linear mappings from ##R \rightarrow R^n## satisfying the following theorem:
"The mapping ##f: R \rightarrow R^n## is differentiable at ##a \in R## if and only if there exists a linear mapping ##L: R \rightarrow R^n## such that
[tex]\lim_{h \to 0}\frac{f(a + h) - f(a) - L(h)}{h} = {\bf{0}}[/tex]
in which case L is defined by ##L(h) = df_a(h) = hf'(a)##." (From Edwards 'Advanced Calculus').
I am to prove that ##L_1 = L_2##. The text hints that I should first show that:
[tex]lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0[/tex]
My first idea was to use the definition of ##L## from the theorem so that
[tex]L_1(h) - L_2(h) = h\lim_{h \to 0}\Big(\frac{f(a + h) - f(a)}{h}\Big) - h\lim_{h \to 0}\Big(\frac{f(a + h) - f(a}{h}\Big) = h\lim_{h \to 0}\Big(\frac{f(a + h) - f(a) - f(a + h) + f(a)}{h}\Big) = h\lim_{h \to 0}(0) = 0[/tex]
And so [tex]lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = \lim_{h \to 0}\frac{0}{h} = 0[/tex]
But looking back, I think that whole process rested on the assumption that ##L_1 = L_2## in the first place. I am not quite sure what to do.
 
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  • #2
Start by assuming
$$\lim_{h \to 0}\frac{f(a + h) - f(a) - L_1(h)}{h} = 0$$
and
$$\lim_{h \to 0}\frac{f(a + h) - f(a) - L_2(h)}{h} = 0$$
Now subtract one equation from the other. What do you end up with?
 
  • #3
[tex]\lim_{h \to 0}\frac{f(a + h) - f(a) - L_1(h)}{h} - \lim_{h \to 0}\frac{f(a + h) - f(a) - L_2(h)}{h} = 0 - 0 = 0[/tex]
So [tex]\lim_{h \to 0}\frac{f(a + h) - f(a) - f(a +h) + f(a)}{h} - \lim_{h \to 0}\frac{L_2(h) - L_1(h)}{h} = \lim_{h \to 0}\frac{0}{h} + \lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0[/tex]
Thus [tex]\lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0[/tex]
Does the next step require a delta-epsilon proof to establish the validity of this limit? Seems to be pointing in that direction.
 
  • #4
Parmenides said:
Thus [tex]\lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0[/tex]
Does the next step require a delta-epsilon proof to establish the validity of this limit? Seems to be pointing in that direction.
Well what is the next step? Your goal is to show that ##L_1(x) = L_2(x)## for all ##x \in \mathbb{R}##.

Hint: This would be a good time to take advantage of the linearity of ##L_1## and ##L_2##.
 
  • #5
Hm, I'm not yet making the connection from a mapping ##L(h)## to ##L({\bf{x}})##, but my first idea is since the above theorem is satisfied, there exist linear mappings ##L_1## and ##L_2## such that ##L_1(h{\bf{x}})## and ##L_2(h{\bf{x}})## = ##h{\bf{x}}_1## and ##h{\bf{x}}_2##, respectively for all ##{\bf{x}}_1,{\bf{x}}_2 \in R##. If I was allowed to say that ##L(h{\bf{x}}) = {\bf{x}}L(h)## I could see how using what I just showed above might be useful, but that doesn't seem right.
 
  • #6
Parmenides said:
If I was allowed to say that ##L(h{\bf{x}}) = {\bf{x}}L(h)## I could see how using what I just showed above might be useful, but that doesn't seem right.
But that's the nice thing about linear mappings: you CAN say that. Remember that a linear mapping from ##\mathbb{R}## to ##\mathbb{R}^n## satisfies
$$L(x+y) = L(x) + L(y)$$
for any ##x,y \in \mathbb{R}##, and
$$L(ax) = aL(x)$$
for any ##a,x \in \mathbb{R}##.

Try using the second fact to simplify
$$\frac{L_1(h)}{h}$$
and
$$\frac{L_2(h)}{h}$$
 
  • #7
[tex]\lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0[/tex]
Since our theorem is satisfied, there exist linear mappings ##L_1## and ##L_2## such that ##L_1(h{\bf{x}})## and ##L_2(h{\bf{x}})## = ##h{\bf{x}}_1## and ##h{\bf{x}}_2##, respectively for all ##{\bf{x}}_1,{\bf{x}}_2 \in R##. By linearity, we have that ##L_1({\bf{x}}_1h) = {\bf{x}}_1L_1(h)## and ##L_2({\bf{x}}_2h) = {\bf{x}}_2L_2(h)##. Then,
[tex] = \lim_{h \to 0}\frac{L_1(h{\bf{x}}_1) - L_2(h{\bf{x}}_2)}{h} = 0[/tex]
And, by linearity:
[tex] = \lim_{h \to 0}\frac{hL_1({\bf{x}}_1) - hL_2({\bf{x}}_2)}{h} = \lim_{h \to 0}\frac{h}{h}L_1({\bf{x}}_1) - \lim_{h \to 0}\frac{h}{h}L_2({\bf{x}}_2) =0 \rightarrow L_1({\bf{x}}_1) = L_2({\bf{x}}_2)[/tex]
If I didn't need to make a distinction between ##{\bf{x}}## then perhaps this is plausible. ><
 
  • #8
Parmenides said:
[tex]\lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0[/tex]
Since our theorem is satisfied, there exist linear mappings ##L_1## and ##L_2## such that ##L_1(h{\bf{x}})## and ##L_2(h{\bf{x}})## = ##h{\bf{x}}_1## and ##h{\bf{x}}_2##, respectively for all ##{\bf{x}}_1,{\bf{x}}_2 \in R##.
Wait, isn't that what you are trying to prove? I don't see how you can say that at this point.

What you have is
$$\lim_{h \to 0}\frac{L_1(h) - L_2(h)}{h} = 0$$
We can rewrite this as follows:
$$\lim_{h \to 0}\left(\frac{1}{h}L_1(h) - \frac{1}{h}L_2(h)\right) = 0$$
and by linearity,
$$\lim_{h \to 0}\left(L_1\left(\frac{1}{h}h\right) - L_2\left(\frac{1}{h}h\right)\right) = 0$$
which is simply
$$\lim_{h \to 0}\left(L_1\left(1\right) - L_2\left(1\right)\right) = 0$$
What does this imply?
 
  • #9
Implying that:
[tex]\lim_{h \to 0}L_1(1) = \lim_{h \to 0}L_2(1)[/tex]
But as ##h \rightarrow 0##, doesn't this imply that ##L_1 = L_2##?
 
  • #10
No, there is no dependency on ##h## in ##L_1(1)## or ##L_2(1)##. So after taking limits the equation simply becomes
$$L_1(1) = L_2(1)$$
Now, can you see how this actually implies that ##L_1(x) = L_2(x)## for all ##x \in \mathbb{R}##? Use the linearity of ##L_1## and ##L_2## to show this.
 

What is meant by "Equality of Two Linear Mappings"?

"Equality of Two Linear Mappings" refers to the concept of two linear transformations having the same output for every possible input. In other words, if two linear mappings have the same domain and range, and they produce the same results for all inputs, then they are considered to be equal.

How is the equality of two linear mappings determined?

The equality of two linear mappings can be determined by comparing their respective matrices. If the matrices are identical, then the mappings are equal. This method is known as the matrix equality method. Alternatively, one can also compare the outputs of the two mappings for several different inputs to determine if they are equal.

What are some examples of linear mappings that are equal?

One example of two linear mappings that are equal would be the identity mapping and the zero mapping, where both mappings produce the same output for every input. Another example would be two linear mappings that have the same transformation matrix, such as scaling and rotation matrices with the same scaling factor or angle.

How does the equality of two linear mappings relate to vector spaces?

The equality of two linear mappings is closely related to vector spaces, as linear mappings are functions that operate on vector spaces. In order for two linear mappings to be equal, they must operate on the same vector space and produce the same results for all inputs in that space.

Why is the equality of two linear mappings important in mathematics?

The equality of two linear mappings is important in mathematics because it allows us to compare and analyze different linear transformations. It also helps us understand the properties of vector spaces and how different linear mappings can be equivalent or different. Additionally, it is a fundamental concept in linear algebra and plays a crucial role in many areas of mathematics such as geometry, physics, and engineering.

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