Functions as Vectors: Understanding Fourier Series and Orthogonal Functions

In summary, the book suggests that functions can be thought of as vectors and that this concept is integral to physics beyond the freshman/sophomore level.
  • #1
futurebird
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Functions as "vectors"

I'm reading a book called "Fourier series and orthogonal functions" by Davis since it seemed pretty readable (at first) and since I don't really know what is going on with these Fourier series, yet.

The book suggests that one can think of functions as vectors. After all a sequence is a function whose domain is the set of real numbers. And a vectors is a list of coordinates (x, y, z) or as many dimensions as you like.

This "functions can be vectors" idea seems pretty central to the ideas in the book, and I'm getting confused about how ... for example ... a continuous function could be a vector. A continuous function is defined at every singe point and there's nothing discrete about the domain.

Is the idea that through use of series we create a list-like correspondence that allows us to think of the function as a vector? I'm really confused. What kind of dimensional space would contain continuous functions as vectors? Wouldn't it need to have infinite dimensions?
 
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  • #2
The concept of a function as a vector is integral to physics beyond the freshman/sophomore level. You are correct that the space is infinite. The key thing that is needed to think of a function as a vector is to define an inner product, typically by means of an integral over some interval.
 
  • #3
D H said:
The concept of a function as a vector is integral to physics beyond the freshman/sophomore level. You are correct that the space is infinite. The key thing that is needed to think of a function as a vector is to define an inner product, typically by means of an integral over some interval.

Okay the inner product is like a dot porduct and then we use a differnt integral to take the magnitude. I was having a hard time believing that the space was really infinite dimensional, but now that I'm just going with that idea this chapter is starting to make more sense.
 
  • #4
Once you get past that mental block of an infinite-dimensional space things become both easier and a lot harder. Things can get really, really ugly in infinite dimensions.

Fortunately, physicists don't look at the really ugly stuff. Some examples from a physics perspective include Fourier series, Sturm-Liouville systems of orthogonal polynomials, and spherical harmonics. These are all well-defined, well-behaved, and avoid most of the ugly stuff (e.g., anything that involves the axiom of choice.)
 

FAQ: Functions as Vectors: Understanding Fourier Series and Orthogonal Functions

What are functions as vectors?

Functions as vectors are mathematical objects that can be represented as a vector with a finite number of components. These components are typically coefficients or variables that can be multiplied by a set of basis functions to create a larger function.

How are functions represented as vectors?

Functions can be represented as vectors by assigning a basis set of functions to the vector's components. For example, a polynomial function can be represented as a vector with each component representing a coefficient for a different power of x.

What are the advantages of representing functions as vectors?

Representing functions as vectors allows for easier manipulation and analysis of mathematical functions. It also allows for the application of vector operations such as scalar multiplication, addition, and dot product to functions.

Can all functions be represented as vectors?

No, not all functions can be represented as vectors. Only functions with a finite number of components can be represented as vectors using a basis set of functions. Functions with infinite or continuously varying components cannot be represented as vectors.

How are functions as vectors used in scientific research?

Functions as vectors are used in a variety of scientific fields, including physics, engineering, and statistics. They are particularly useful in data analysis and modeling, as they allow for the representation and manipulation of complex functions in a more streamlined and efficient manner.

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