I actually designed a digital equalizer using an IIR filter this semester, which actually does theoretically work on sequences of numbers, which constitutes an infinite dimensional vector space. The actual math was just algebra and programming, but it was an implementation of a Z-transform transfer function which is a sequence operator (maps input sequence to output sequence).
IMO infinite-dimensional stuff shows up in two types of problems:
For some reason, you need to solve the partial differential equation you started with, i.e. you can’t use symmetry or approximations to simplify it into an ordinary differential equation.
When you’re dealing with signals that change in time or space, you have to decompose those signals into simpler signals that are easier to analyze.
Infinite-dimensional vector spaces also show up in another context: functional analysis.
If you stretch your imagination a bit, then you can think of vectors as functions. A (real) n-dimensional vector is a list of numbers (v1, v2, …, vn), which can be thought of as a function {1, 2, …, n} → ℝ, where k ∊ {1, …, n} gets sent to vk. So, an n-dimensional (real) vector space is a collection of functions {1, 2, …, n} -> ℝ, where you can add two functions together and multiply functions by a real number.
Under this interpretation, the idea of “infinite-dimensional” vector spaces becomes much more reasonable (in my opinion anyway), since it’s not too hard to imagine that there are situations where you want to look at functions with an infinite domain. For example, you can think of an infinite sequence of numbers as a function with infinite domain. (i.e., an infinite sequence (v1, v2, …) is a function ℕ → ℝ, where k ∊ ℕ gets sent to vk.)
and this idea works for both “countable” and “uncountable” “vectors”. i.e., you can use this framework to study a vector space where each “vector” is a function f: ℝ → ℝ. why would you want do this? because in this setting, integration and differentiation are linear maps. (e.g., if f, g: ℝ → ℝ are “vectors”, then D(f + g) = Df + Dg, and ∫*(f+g) = ∫f + ∫g, where D denotes taking the derivative.)
Infinite-dimensional vector spaces also show up in another context: functional analysis.
From an engineering perspective, functional analysis is the main mathematical framework behind (1) and (2) in my previous comment. Although they didn’t teach functional analysis for real in any of my coursework, I kinda picked up that it was going to be an important topic for what I want to do when I kept seeing textbooks for it cited in PDE and “signals and systems” books. I’ve been learning it on my own since I finished Calc III like four years ago.
Such an incredibly interesting and deep topic IMO.
I actually designed a digital equalizer using an IIR filter this semester, which actually does theoretically work on sequences of numbers, which constitutes an infinite dimensional vector space. The actual math was just algebra and programming, but it was an implementation of a Z-transform transfer function which is a sequence operator (maps input sequence to output sequence).
IMO infinite-dimensional stuff shows up in two types of problems:
For some reason, you need to solve the partial differential equation you started with, i.e. you can’t use symmetry or approximations to simplify it into an ordinary differential equation.
When you’re dealing with signals that change in time or space, you have to decompose those signals into simpler signals that are easier to analyze.
Infinite-dimensional vector spaces also show up in another context: functional analysis.
If you stretch your imagination a bit, then you can think of vectors as functions. A (real) n-dimensional vector is a list of numbers (v1, v2, …, vn), which can be thought of as a function {1, 2, …, n} → ℝ, where k ∊ {1, …, n} gets sent to vk. So, an n-dimensional (real) vector space is a collection of functions {1, 2, …, n} -> ℝ, where you can add two functions together and multiply functions by a real number.
Under this interpretation, the idea of “infinite-dimensional” vector spaces becomes much more reasonable (in my opinion anyway), since it’s not too hard to imagine that there are situations where you want to look at functions with an infinite domain. For example, you can think of an infinite sequence of numbers as a function with infinite domain. (i.e., an infinite sequence (v1, v2, …) is a function ℕ → ℝ, where k ∊ ℕ gets sent to vk.)
and this idea works for both “countable” and “uncountable” “vectors”. i.e., you can use this framework to study a vector space where each “vector” is a function f: ℝ → ℝ. why would you want do this? because in this setting, integration and differentiation are linear maps. (e.g., if f, g: ℝ → ℝ are “vectors”, then D(f + g) = Df + Dg, and ∫*(f+g) = ∫f + ∫g, where D denotes taking the derivative.)
From an engineering perspective, functional analysis is the main mathematical framework behind (1) and (2) in my previous comment. Although they didn’t teach functional analysis for real in any of my coursework, I kinda picked up that it was going to be an important topic for what I want to do when I kept seeing textbooks for it cited in PDE and “signals and systems” books. I’ve been learning it on my own since I finished Calc III like four years ago.
Such an incredibly interesting and deep topic IMO.