Functionla Analysis separability

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In summary, to prove that K=L(l^2,l^2) is not separable, we can use the fact that if a space contains an uncountable number of non-intersecting open balls, it is not separable. We can also construct an isometric embedding of the space into the bounded linear operators, and show that the underlying space is not separable.
  • #1
Funky1981
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Let T: l^2 -> l^2 be bounded linear operators. K=L(l^2,l^2) be the space of T, Prove that K=L(l^2,l^2) is not separable

I know that if a space contains an uncountable number of non intersecting open balls then it is not separable. But how can I apply this statement here ( I mean how to construct such open balls) And are there any easier way to do it ?
 
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  • #2
I don't know a direct proof of the result, but I have to ask which topology you're considering on K.
 
  • #3
A diagonal operator has the form

[tex]T(x_1,x_2,x_3,...) = (\alpha_1 x_1, \alpha_2 x_2, \alpha_3 x_3, ...)[/tex]

where ##\alpha_k\in \mathbb{C}##.

It can easily be checked that ##T## is bounded if and only if the sequence ##(\alpha_k)_k## is bounded. Furthermore, we have ##\|T\|= \|(\alpha_k)_k\|_\infty##. This yields an isometric embedding ##\ell^\infty\rightarrow \mathcal{B}(\ell^2)##. Thus it suffices to show ##\ell^\infty## is not separable, which is well-known.
 

FAQ: Functionla Analysis separability

1. What is the definition of separability in functional analysis?

Separability in functional analysis refers to the property of a function space where it contains a countable dense subset. In simpler terms, it means that the space is "small enough" to be approximated by a countable set of functions.

2. Why is separability an important concept in functional analysis?

Separability is important in functional analysis because it allows for a more manageable and concrete understanding of the underlying function space. It also allows for easier construction and approximation of functions within the space.

3. How is separability related to convergence in functional analysis?

In functional analysis, convergence is often defined in terms of the distance between functions. Separability ensures that there is always a "close enough" function in the space to approximate the desired function, leading to convergence.

4. Can a function space be separable and infinite-dimensional at the same time?

Yes, a function space can be both separable and infinite-dimensional. The two concepts are not mutually exclusive, and in fact, many commonly used function spaces in functional analysis, such as L^p spaces, are both separable and infinite-dimensional.

5. How does separability impact the study of functional analysis in practical applications?

In practical applications, separability allows for a more efficient and effective approach to solving problems involving functions. It allows for the use of numerical methods and algorithms to approximate functions in the space, making it a valuable concept in areas such as signal processing, data analysis, and control theory.

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