Proving F is Not Injective in R^n

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In summary: Jacobian of $F$ will also be zero, making it non-singular. This means that $F$ is not invertible at $p$, which contradicts the assumption that $f$ is injective.In summary, we have proven that if the gradient of a function is zero at a point, then the function cannot be injective. This provides a proof for the statement that was given in the conversation. I hope this helps to clarify any confusion. Let me know if you need any further assistance.
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Lolyta
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Hello. I have the next problem:
Let $f: U\subset R^n\rightarrow R$ a class $C^1$ function in an open subset $U$ of $R^n$. Proff that f can't be injective.There are some idications: suppose that the vector ($\nabla f$)(p) is not zero (if it's zero the function is not injective WHY?) in $p\in U$ and that we can assume that $\frac{\partial f}{\partial x_i}$(p) $\neq$ 0; consider so $F(x_1,x_2,...,x_n) = (f(x),x_2,...x_n)$ with $x=(x_1,x_2,..,x_n)$ and get the result applying the inverse function theorem.

Thank you so much for any help!
 
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Hello there,

Thank you for bringing this problem to our attention. I can provide some clarification and a proof for why a function cannot be injective if the gradient at a point is zero.

Firstly, let's define what it means for a function to be injective. A function is said to be injective if every element in the domain has a unique image in the range. In other words, no two elements in the domain can have the same image in the range.

Now, let's consider the given function $f: U\subset R^n\rightarrow R$, which is a class $C^1$ function in an open subset $U$ of $R^n$. Since $f$ is a $C^1$ function, it is continuously differentiable in $U$. This means that the gradient of $f$ exists at every point in $U$.

Now, suppose that at a point $p\in U$, the gradient of $f$, denoted by ($\nabla f$)(p), is equal to zero. This means that all the partial derivatives of $f$ at the point $p$ are equal to zero. In other words, the function has a flat tangent plane at the point $p$.

Now, let's assume that the function $f$ is injective. This means that for every point $p\in U$, there exists a unique image in the range. However, since the gradient at $p$ is equal to zero, this contradicts the definition of injectivity. This is because if the function has a flat tangent plane at $p$, then any point on this tangent plane will have the same image as $p$. Therefore, $p$ cannot have a unique image in the range, and hence the function cannot be injective.

To prove this formally, we can follow the given indications. Let's consider the function $F(x_1,x_2,...,x_n) = (f(x),x_2,...x_n)$, where $x=(x_1,x_2,..,x_n)$. This function takes $n$ variables and maps them to $n+1$ variables. Now, by the inverse function theorem, if the Jacobian of $F$ is non-singular at a point, then $F$ is invertible at that point. However, since the gradient of $f$ at $p$ is
 

Related to Proving F is Not Injective in R^n

1. How do you prove that a function is not injective in R^n?

To prove that a function F is not injective in R^n, you must show that there exist two different inputs in the domain of F that yield the same output. This can be done by providing a counterexample or by using the formal definition of injectivity, which states that if F(x) = F(y), then x = y.

2. What is the difference between injective and bijective functions in R^n?

An injective function in R^n is one in which each element in the domain is mapped to a unique element in the range. A bijective function, on the other hand, is both injective and surjective, meaning that every element in the range has a corresponding element in the domain and vice versa.

3. Can a function be injective in some subsets of R^n but not in others?

Yes, a function can be injective in some subsets of R^n but not in others. This is because the definition of injectivity depends on the specific inputs and outputs of the function, so it can vary depending on the subset of R^n that is being considered.

4. How does proving that a function is not injective in R^n relate to its graph?

Proving that a function is not injective in R^n can be visualized on its graph by looking for points where the function intersects itself. If there are any such points, then the function is not injective because there exist two different inputs that yield the same output.

5. Are there any real-world applications of proving that a function is not injective in R^n?

Yes, there are several real-world applications of proving that a function is not injective in R^n. For example, in computer science, this concept is used in data compression algorithms to ensure that the original data can be reconstructed accurately. It is also important in cryptography to make sure that different inputs do not yield the same output, which could compromise the security of the system.

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