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

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SUMMARY

The discussion focuses on the engagement of introductory students with general vector spaces, particularly those beyond Euclidean space, such as polynomial and continuous function spaces. It highlights that while these concepts may seem abstract, they are foundational in applications like differential equations and quantum mechanics. Specifically, the Sturm-Liouville problem and the Schrödinger equation exemplify the practical utility of vector spaces through eigenvalue problems and Fourier analysis. The conversation emphasizes the need for additional structures, such as linear maps and subspace decomposition, to make these topics more relatable and impactful.

PREREQUISITES
  • Understanding of linear algebra concepts, including vector spaces and linear maps.
  • Familiarity with differential equations, particularly Sturm-Liouville theory.
  • Basic knowledge of quantum mechanics and eigenvalue problems.
  • Experience with Fourier analysis and its applications in solving equations.
NEXT STEPS
  • Research the applications of Sturm-Liouville problems in physics and engineering.
  • Explore the role of eigenvalues and eigenvectors in quantum mechanics.
  • Study the principles of Fourier analysis and its use in solving differential equations.
  • Investigate how linear maps and subspace decomposition enhance the understanding of vector spaces.
USEFUL FOR

This discussion is beneficial for mathematics educators, physics students, and anyone interested in the practical applications of vector spaces in advanced topics like quantum mechanics and differential equations.

matqkks
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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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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.
 
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.
 

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