Understanding Functional Spaces in Mathematics

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In summary, functional spaces are studied in mathematics, particularly in Sturm-Liouville Theory and Harmonic Analysis. These spaces involve decomposing functions into other functions, such as eigenfunctions, and using orthogonal sets of functions to express functions in terms of other functions. However, one cannot always decompose a function into a finite number of other functions, as is the case with cos(x) and x^2 and x^3.
  • #1
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I've been looking for some decent info on functional space but
could not find anything. Googling gives lots of defenitions, but no explanations as such.

Basically I'd like to understand why a function can be decomposed into other function, e.g. understand a meaning of inner product with respect to functions.

Or why one can or can not say decompose cos(x) into [tex]x^{2}[/tex] and [tex]x^{3}[/tex] with some coefficients.

I don't event clearly know what filed of mathematics studies functional spaces :)

Thanks.
 
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  • #2
a_Vatar said:
I've been looking for some decent info on functional space but
could not find anything. Googling gives lots of defenitions, but no explanations as such.

Basically I'd like to understand why a function can be decomposed into other function, e.g. understand a meaning of inner product with respect to functions.
Given the Sturm-Liouville Problem:
[tex][p(x)y']'+\{q(x)+\lambda r(x)\}y=0 \mbox{ on }(a,b)[/tex]
Satisfing the boundary value problems:
[tex]\alpha_1 y(a)+\beta_1 y'(a)=0[/tex]
[tex]\alpha_2 y(b) + \beta_2 y'(b)=0[/tex]

The question is for what [tex]\lambda[/tex] does the above differential equation has non-trivial solutions. For example, [tex]y(x)=0 \mbox{ on }[a,b][/tex] is certainly a solution but it is trivial. Those [tex]\lambda[/tex] are called "eigenvalues" and those functions are called "eigenfunctions".

Sturm-Liouville Theory says that such [tex]\lambda[/tex] exists (given conditions on [tex]p(x),q(x),r(x)[/tex]).

The solutions corresponding to distinct [tex]\lambda[/tex] are linearly independent. And furthermore if [tex]\phi_1(x)[/tex] corresponds to [tex]\lambda_1[/tex] and [tex]\phi_2(x)[/tex] corresponds to [tex]\lambda_2[/tex] then:
[tex]\int_a^b \phi_1(x)\phi_2(x) dx = 0[/tex].
Meaning the set [tex]\{ \phi_n (x)\}[/tex] is orthogonal.

Say, [tex]f(x) = \sum_{n=1}^{\infty}a_n \phi_n(x)[/tex] can be expressed by these orthogonal functions obtained from the Sturm-Loivuille problem.
Then,
[tex]f(x)\phi_m(x) = \sum_{n=1}^{\infty}a_n \phi_n(x) \phi_m(x)[/tex]
Integrate both sides (we are assuming uniform convergence):
[tex]\int_a^b f(x)\phi_m(x) dx = a_m \int_a^b [\phi_m(x)]^2 dx[/tex] because of orthogonality.
Thus,
[tex]a_m = \frac{1}{||\phi_m(x)||^2}\int_a^b f(x)\phi_m(x) dx[/tex]
Where [tex]||\phi_m(x)||^2=\left( \sqrt{\int_a^b [\phi_m(x)]^2 dx } \right)^2[/tex] called the L2 measure. (just shorthand notation).


The above ideas are taken from an area of mathematicas called Harmonic analysis. The idea is to express functions in terms of other functions, not just Fourier series, hence its the Generalized Fourier Series.
 
  • #3
One cannot decompose cos(x) into "x2 and x3 with some coefficients" because x2 and x3 do not span a space that contains cos(x). Of course, one can decompose cos(x) into an infinite sum of powers of x: it's MacLaurin series.
 

What is a functional space?

A functional space is a mathematical concept that describes a collection of functions that share certain properties. These properties may include having a specific domain and range, being continuous, or being differentiable. Functional spaces are important in mathematics because they provide a framework for studying and understanding families of functions.

Why is understanding functional spaces important?

Understanding functional spaces is important because it allows us to generalize and apply mathematical concepts to a wide range of functions. This can help us solve problems and make predictions in various fields such as physics, engineering, and economics. Additionally, functional spaces provide a way to analyze and compare different types of functions, leading to deeper insights and discoveries.

What are some common examples of functional spaces?

Some common examples of functional spaces include the space of continuous functions, the space of differentiable functions, and the space of integrable functions. Other examples include the space of polynomials, the space of trigonometric functions, and the space of analytic functions. Different functional spaces may have different properties and applications.

How are functional spaces related to other mathematical concepts?

Functional spaces are closely related to other mathematical concepts such as metric spaces, topological spaces, and vector spaces. For example, the space of continuous functions can be viewed as a subset of the space of all functions, which is a vector space. Additionally, functional analysis, which studies functional spaces, is closely connected to other areas of mathematics such as calculus, differential equations, and linear algebra.

What are some applications of functional spaces?

Functional spaces have numerous applications in various fields, including physics, engineering, economics, and statistics. In physics, functional spaces are used to model and analyze physical phenomena, such as wave functions in quantum mechanics. In engineering, they are used to study control systems and signal processing. In economics, functional spaces are used to model economic systems and make predictions. In statistics, functional spaces are used to analyze time series data and make forecasts.

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