Approximation of ##f\in L_p## with simple function ##f_n\in L_p##

In summary, Kolmogorov-Fomin's statement that any function can be approximated arbitrarily well by simple functions in L_2 is true, provided that the approximations are taken with respect to the norm.
  • #1
DavideGenoa
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5
Dear friends, let us use the definition of Lebesgue integral on ##X,\mu(X)<\infty## as the limit

##\int_X fd\mu:=\lim_{n\to\infty}\int_Xf_nd\mu=\lim_{n\to\infty}\sum_{k=1}^\infty y_{n,k}\mu(A_{n,k})##
where ##\{f_n\}## is a sequence of simple, i.e. taking countably many values ##y_{n,k}## for ##k=1,2,\ldots##, functions ##f_n:X\to\mathbb{C}## uniformly converging to ##f##, and ##\{y_{n,k}\}=f_n(A_{n,k})## where ##\forall i\ne j\quad A_{n,i}\cap A_{n,j}=\emptyset##.I know that if any sequence ##\{f_n\}\subset L_p(X,\mu)##, ##p\geq 1## uniformly converges to ##f## then [it also converges][1] with respect to the norm ##\|\cdot\|_p## to the same limit, which is therefore an element of ##L_p(X,\mu)##.I read in Kolmogorov-Fomin's Elements of the Theory of Functions and Functional Analysis (1963 Graylock edition, p.85) that an arbitrary function ##f\in L_2## can be approximated [with respect to norm ##\|\cdot\|_2##, I suspect] with arbitrary simple functions belonging to ##L_2##.I do not understand how the last statement is deduced. If it were true, I would find the general case for ##L_p##, ##p\geq 1##, even more interesting. I understand that if ##f\in L_p\subset L_1## then for all ##\varepsilon>0## there exists a simple function ##f_n\in L_1## such that ##\forall x\in A\quad |f(x)-f_n(x)|<\varepsilon## and then, for all ##p\geq 1##, ##\|f-f_n\|_p<\varepsilon##, but how to find ##f_n\in L_p## (if ##p=2## or in general ##p>1##)?Can anybody explain such a statement? Thank you so much!
 
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  • #3
Thank you so much!
Fredrik said:
Friedman is a good place to start
Very interesting book! As to the above said fact, I knew Friedman's theorem 2.2.5, but I am not able to prove what I said...:(
 
  • #4
I'm not sure I understand. How you you define ##L_p##? Is that not a set of measurable functions? (Did you mean ##L^p##? For example, ##L^2## is the set of square-integrable functions.)

Friedman 2.2.5 tells you how to approximate an arbitrary measurable positive real-valued function by simple functions. The standard rewrite ##f=f_+-f_-## immediately generalizes this result to arbitrary measurable real-valued functions.
 
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  • #5
Oh, sorry, I intended the set of measurable functions such that ##\int_X fd\mu## exists by ##L_p##, i.e. the same thing as ##L^p##. Thank you again!
 
  • #6
Don't you mean the set of measurable functions with ## \int_X f^pd\mu ## exists?
 
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  • #7
WWGD said:
Don't you mean the set of measurable functions with ## \int_X f^pd\mu ## exists?
Oh, yes, I mean by ##f\in L_p(X,\mu)## that ##\int_X|f|^p d\mu## exists. Excuse me: I omitted the ##p##!
 

1. What is the definition of a simple function?

A simple function is a function that takes on a finite number of values within a given interval. It can also be expressed as a linear combination of indicator functions, where each indicator function is defined by a single interval.

2. How is a simple function used to approximate a function in Lp?

In order to approximate a function f in Lp, a simple function fn is constructed by dividing the domain of f into smaller intervals, and then taking the average value of f over each interval. The resulting simple function fn will have a finite number of values and will converge to f in the Lp norm as the intervals become smaller and smaller.

3. What is the importance of approximating a function with a simple function in Lp?

Approximating a function with a simple function is important because it allows us to work with a more manageable and finite set of values, while still maintaining the key properties of the original function. This is especially useful in analysis and integration, as it allows us to approximate complicated functions with simpler ones that are easier to integrate and work with.

4. Is there a difference in the way we approximate a function in Lp compared to other function spaces?

The general approach of using simple functions to approximate functions is similar in all function spaces. However, there are some technical differences in the construction of simple functions and the convergence properties in different function spaces. For example, in Lp, we use the Lp norm to measure convergence, while in other function spaces, different norms may be used.

5. Can any function in Lp be approximated by a simple function?

Yes, any function in Lp can be approximated by a simple function. This is because the set of simple functions is dense in Lp, meaning that any function in Lp can be approximated arbitrarily closely by a simple function. However, the construction of the simple function may be more complicated for certain functions, and the convergence may be slower for some functions compared to others.

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