Norm equivalence between Sobolev space and L_2

In summary, the paper discusses the reverse Poincaré inequality, which provides a norm equivalence between the norm of a vector and the gradient of its gradient. This is expressed as \|v\|_{L^2(\Omega)} \leq C(\Omega) \|\nabla v\|_{L^2(\Omega)}. The paper then goes on to state that the Sobolev norm \|u\|_{H_0^1(\Omega)} is equivalent to the L^2-norm of the gradient, \|u\|_{H_0^1(\Omega)} = \|\nabla u\|_{L^2(\Omega)}. This is due to the fact that L^2 is also
  • #1
kisengue
12
0
Hello! I've found this paper, wherein page 33 states that the reverse Poincaré inequality gives

[tex]\forall v \in H^1_0(\Omega) , \|v\|_{L^2(\Omega)} \leq C(\Omega) \|\nabla v\|_{L^2(\Omega)} [/tex]

This I can follow - it gives a norm equivalence between the norm of a vector and the gradient of its gradient (clumsily expressed, I know). However, just a little bit later the paper states that

[tex]\|u\|_{H_0^1(\Omega)} = \|\nabla u\|_{L^2(\Omega)}[/tex]

That is, those two norms are equivalent. This is what I don't understand - I don't understand the jump from the L^2-norms in the first statement to the Sobolev norm in the second. Any help in understanding this would be helpful.
 
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  • #2
I think L2 is also in a Hilbert space. And if you take the gradient of a function, the result resides in a lower Hilbert Space.
So I think the problem reduces to finding out which H corresponds to L2.
 

1. What is the difference between a Sobolev space and an L2 space?

A Sobolev space is a function space that contains functions with certain smoothness properties, while an L2 space is a function space that contains square-integrable functions. This means that functions in a Sobolev space have derivatives that are also in the space, while functions in an L2 space do not necessarily have derivatives.

2. Why is it important to study the norm equivalence between Sobolev spaces and L2 spaces?

Studying the norm equivalence between these spaces allows us to understand the relationship between smoothness and integrability of functions. It also helps us to determine which space is more appropriate for certain types of problems.

3. What is the definition of the norm in a Sobolev space?

The norm in a Sobolev space is defined as the sum of the squared norm of the function and its derivatives up to a certain order. This norm measures the smoothness of a function and is denoted as ||f||k, where k is the maximum order of derivatives included in the norm.

4. How do we prove norm equivalence between Sobolev spaces and L2 spaces?

Norm equivalence can be proven by showing that the norm in a Sobolev space is bounded by a constant multiple of the norm in an L2 space, and vice versa. This involves using mathematical techniques such as inequalities and convergence theorems.

5. Can norm equivalence be extended to other function spaces?

Yes, norm equivalence can be extended to other function spaces such as Lp spaces and Hölder spaces. However, the techniques used to prove norm equivalence may differ from those used for Sobolev spaces and L2 spaces.

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