Is there a contradiction in assuming that $J$ is linear over $C^1([a,b])$?

In summary, the conversation discusses the linearity of a functional $J$ over the space $C^1([a,b])$, which is defined as $J(y)= \int_a^b (y')^2 dx+ G(y(b))$. The participants discuss the necessary constraints on the function $G$ for $J$ to be linear, and eventually find a contradiction by picking different values for $b$. It is concluded that the functional is not linear and there must be constraints on $G$.
  • #1
evinda
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Hello! (Wave)

Is the following functional over $C^1([a,b])$ linear?

$$J(y)= \int_a^b (y')^2 dx+ G(y(b))$$

That's what I have thought:if $J$ would be linear it would have to hold:

$\forall y \in C^1([a,b]), \forall \lambda \in \mathbb{R}$

$J(\lambda y)=\lambda J(y)$ or equivalently

$\int_a^b (\lambda y')^2 dx+ G(\lambda y(b))=\lambda \left( \int_a^b (y')^2 dx+ G(y(b))\right)\\ \Rightarrow \lambda^2 \int_a^b (y')^2 dx+ G(\lambda y(b))= \lambda \int_a^b (y')^2 dx+ \lambda G(y(b)) $

For $\lambda=2, y=x$ the equality becomes:$4 \int_a^b 1 dx+ G(2b)=2 \int_a^b 1 dx+ 2 G(b) \Rightarrow 2(b-a)=2G(b)-G(2b) $

Can we take a specific function $G$ ? Or how else could we find a contradiction? (Thinking)
 
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  • #2
evinda said:
Hello! (Wave)

Is the following functional over $C^1([a,b])$ linear?

$$J(y)= \int_a^b (y')^2 dx+ G(y(b))$$

That's what I have thought:if $J$ would be linear it would have to hold:

$\forall y \in C^1([a,b]), \forall \lambda \in \mathbb{R}$

$J(\lambda y)=\lambda J(y)$ or equivalently

$\int_a^b (\lambda y')^2 dx+ G(\lambda y(b))=\lambda \left( \int_a^b (y')^2 dx+ G(y(b))\right)\\ \Rightarrow \lambda^2 \int_a^b (y')^2 dx+ G(\lambda y(b))= \lambda \int_a^b (y')^2 dx+ \lambda G(y(b)) $

For $\lambda=2, y=x$ the equality becomes:$4 \int_a^b 1 dx+ G(2b)=2 \int_a^b 1 dx+ 2 G(b) \Rightarrow 2(b-a)=2G(b)-G(2b) $

Can we take a specific function $G$ ? Or how else could we find a contradiction? (Thinking)

Hey! (Smile)

If it is a linear functional, there will have to be constraints on $G$.
Suppose we prove that whatever we pick for $G$, it won't be good enough?

Say, what if we pick the same example with y=2x.
Then what will we find as constraint for $G$? (Wondering)
 
  • #3
I like Serena said:
Hey! (Smile)

If it is a linear functional, there will have to be constraints on $G$.
Suppose we prove that whatever we pick for $G$, it won't be good enough?

Say, what if we pick the same example with y=2x.
Then what will we find as constraint for $G$? (Wondering)

Then it will be as follows, right?

$$J(\lambda y)= \lambda J(y) \Rightarrow \int_a^b (\lambda y)' dx+ G(\lambda y(b))= \lambda \int_a^b (y')^2 dx+ \lambda G(y(b))$$

For $\lambda=2$ and $y(x)=2x$ we have:

$$4 \int_a^b 1 dx+ G(4b)=8 \int_a^b 1 dx+ 2 G(b) \Rightarrow G(4b)-2G(b)=4(b-a)$$

But how can we find constraint for $G$? (Thinking)
 
  • #4
evinda said:
For $\lambda=2$ and $y(x)=2x$ we have:

$$4 \int_a^b 1 dx+ G(4b)=8 \int_a^b 1 dx+ 2 G(b) \Rightarrow G(4b)-2G(b)=4(b-a)$$

Shouldn't that be:
$$2^2 \int_a^b (2)^2 dx+ G(2\cdot 2b)=2 \int_a^b (2)^2 dx+ 2 G(2b) \Rightarrow 2G(2b)-G(4b)=8(b-a)$$
(Wondering)

But how can we find constraint for $G$? (Thinking)

From your first example, if we pick $\lambda=2, y=x, a=0, b=1$, we find $2G(b)-G(2b)=2(b-a) \Rightarrow \boxed{2G(1)-G(2)=2}$.

When we pick $\lambda=2, y=2x, a=0, b=\frac 12$, we find $2G(2b)-G(4b)=8(b-a) \Rightarrow \boxed{2G(1)-G(2)=4}$.

This is a contradiction. (Nerd)
 
  • #5
I like Serena said:
Shouldn't that be:
$$2^2 \int_a^b (2)^2 dx+ G(2\cdot 2b)=2 \int_a^b (2)^2 dx+ 2 G(2b) \Rightarrow 2G(2b)-G(4b)=8(b-a)$$
(Wondering)

Oh yes, right... (Nod)

I like Serena said:
From your first example, if we pick $\lambda=2, y=x, a=0, b=1$, we find $2G(b)-G(2b)=2(b-a) \Rightarrow \boxed{2G(1)-G(2)=2}$.

When we pick $\lambda=2, y=2x, a=0, b=\frac 12$, we find $2G(2b)-G(4b)=8(b-a) \Rightarrow \boxed{2G(1)-G(2)=4}$.

This is a contradiction. (Nerd)

I see... But can we take two different $b$s, although the functional is defined over $C^1([a,b])$ where $a,b$ are fixed? (Thinking) :confused:
 
  • #6
evinda said:
Oh yes, right... (Nod)
I see... But can we take two different $b$s, although the functional is defined over $C^1([a,b])$ where $a,b$ are fixed? (Thinking) :confused:

Oh yes, you're right... (Blush)

Then let's pick $\lambda = 2$ and $y=2x - b$.

Then we get:
$$2g(y(b)) - g(2y(b)) = 8(b-a) \Rightarrow \boxed{2g(b) - g(2b) = 8(b-a)}$$

This is a contradiction, assuming that $a\ne b$. (Thinking)
 
  • #7
I like Serena said:
Oh yes, you're right... (Blush)

Then let's pick $\lambda = 2$ and $y=2x - b$.

Then we get:
$$2g(y(b)) - g(2y(b)) = 8(b-a) \Rightarrow \boxed{2g(b) - g(2b) = 8(b-a)}$$

This is a contradiction, assuming that $a\ne b$. (Thinking)

I see... Thanks a lot! (Mmm) (Smirk)
 
  • #8
I like Serena said:
Oh yes, you're right... (Blush)

Then let's pick $\lambda = 2$ and $y=2x - b$.

Would we also get a contradiction picking an other $y$ for $\lambda=2$ or is it the only possibility? (Thinking)
 
  • #9
evinda said:
Would we also get a contradiction picking an other $y$ for $\lambda=2$ or is it the only possibility? (Thinking)

Almost any other $y$ will also work, as long as $y(b)=b$ as you pointed out.
For instance $y=b$. (Wasntme)
 

FAQ: Is there a contradiction in assuming that $J$ is linear over $C^1([a,b])$?

Is the functional linear?

The answer to this question depends on the context and the definition of "functional linear." In general, a functional linear is a function that is both linear and continuous. It is often used in the context of functional analysis, a branch of mathematics that studies vector spaces and their transformations. So, if a function satisfies the criteria of being both linear and continuous, then it can be considered a functional linear.

How do I determine if a function is functional linear?

To determine if a function is functional linear, you need to check if it satisfies the criteria of being both linear and continuous. This can be done by evaluating the function at different points and checking if it follows the rules of linearity (such as satisfying the properties of additivity and homogeneity). Additionally, you can also use mathematical techniques such as differentiation and integration to check for continuity.

What is the difference between functional linear and ordinary linear?

Functional linear and ordinary linear are different types of functions. Ordinary linear functions are defined on a finite-dimensional vector space, while functional linear functions are defined on an infinite-dimensional vector space. Additionally, functional linear functions can have more complex behavior, such as being unbounded or having infinite derivatives, while ordinary linear functions behave in a simpler manner.

What are the applications of functional linear functions?

Functional linear functions have various applications in mathematics, physics, and engineering. They are used in functional analysis to study vector spaces and their transformations. In physics, they are used to describe the behavior of systems with infinite degrees of freedom, such as vibrating strings or quantum mechanical systems. In engineering, functional linear functions are used to model and analyze complex systems, such as control systems and signal processing.

Are there any limitations to functional linear functions?

Like any mathematical concept, functional linear functions have limitations. One of the main limitations is that they can only be defined on infinite-dimensional vector spaces, which can make their analysis and computation more complex. Additionally, functional linear functions may not always accurately describe real-world phenomena, as they are idealized mathematical constructs. It is important to carefully consider the context and assumptions when using functional linear functions in practical applications.

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