Introducing General Vector Spaces: Engaging Examples and Real-Life Applications

In summary, the conversation discusses the struggle of introducing general vector spaces to students and the lack of tangible real-life applications for them. The speaker suggests that additional structures and information, such as linear maps and subspaces, make the topic more interesting. The best real-life applications mentioned include solving differential equations and using Fourier analysis in quantum mechanics.
  • #1
matqkks
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5
Students familiar with Euclidean space find the introduction of general vectors spaces pretty boring and abstract particularly when describing vector spaces such as set of polynomials or set of continuous functions. Is there a tangible way to introduce this? Are there examples which will have a real impact? I would like to introduce this in an engaging manner to introductory students. Are there any real life applications of general vector spaces?
 
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  • #2
Well, the definition of general vector spaces is an abstraction from the usual properties of $\mathbb{R}^n$. I'm not sure what kind of "real life" applications you could show. Almost everything usually is described using linear algebra, but you need additional information and structures for it to become interesting, like linear maps, decomposition into subspaces, etc. I don't think there'll be a vector space that's all useful just by existence.
 
  • #3
The best real-life applications of vector spaces of which I am aware are differential equations, especially Sturm-Liouville type. Introductory Quantum Mechanics spends a lot of time solving the Schrödinger equation, which in its non-relativistic time-independent form, is a self-adjoint eigenvalue problem. You set up basis vectors for the eigenspace, and then you write down your solution with Fourier analysis - as a sum of eigenvectors.
 

FAQ: Introducing General Vector Spaces: Engaging Examples and Real-Life Applications

What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and a set of operations that can be performed on those vectors. These operations include vector addition and scalar multiplication. A vector space must also satisfy certain properties, such as closure under these operations and the existence of a zero vector and additive inverses.

What are the basic properties of vector spaces?

There are five basic properties of vector spaces: closure under addition, closure under scalar multiplication, associativity of scalar multiplication, distributivity of scalar multiplication over vector addition, and the existence of a zero vector and additive inverses. These properties ensure that the operations performed on vectors in a vector space are well-defined and follow certain rules.

How are vector spaces used in real-world applications?

Vector spaces are used in a wide range of fields, including physics, engineering, and computer science. In physics, vector spaces are used to represent physical quantities such as velocity, force, and electric fields. In engineering, they are used to solve problems related to forces, motion, and optimization. In computer science, vector spaces are used in machine learning and data analysis to represent data and make predictions.

How is the dimension of a vector space determined?

The dimension of a vector space is determined by the number of linearly independent vectors in the space. In other words, it is the number of vectors that cannot be expressed as a linear combination of the other vectors in the space. For example, in a 3-dimensional vector space, the dimension is 3 because any three non-collinear vectors can form a basis for the space.

Can vector spaces have infinite dimensions?

Yes, vector spaces can have infinite dimensions. This means that there is an infinite number of linearly independent vectors in the space. An example of an infinite-dimensional vector space is the space of all polynomials, where the basis is given by the set of all monomials of different degrees. Infinite-dimensional vector spaces have many important applications in mathematics and physics, such as in Fourier analysis and quantum mechanics.

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