Eigenvalue Problem and the Calculus of Variations

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Discussion Overview

The discussion revolves around the eigenvalue problem represented by the equation ##B u = \lambda A u##, where ##A## and ##B## are linear operators and ##u## is a function. Participants explore the relationship between this eigenvalue problem and the calculus of variations, particularly in finding stationary values of the expression ##(Bu,u)## under the constraint ##(Au,u)=1##.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant seeks clarification on how the eigenvalue problem relates to finding stationary values of ##(Bu,u)## subject to the constraint ##(Au,u)=1##.
  • Another participant suggests applying the method of Lagrange multipliers to the stationary value problem, indicating a potential approach to solve it.
  • A further contribution mentions a trial function approach, where ##u = \sum_i \alpha_i w_i##, and discusses minimizing ##(Bu,u)## under the constraint ##\sum \alpha_i^2=1##, leading to the eigenvalue problem.
  • One participant notes that the terms "linear operator" and "matrix" coincide only in finite-dimensional spaces, suggesting limitations in the context of infinite-dimensional spaces.
  • Several participants reference a previous discussion about finding eigenvalues of ##B^{-1}A## or ##A^{-1}B##, indicating ongoing interest in this topic.

Areas of Agreement / Disagreement

Participants express varying approaches to the eigenvalue problem and its connection to the calculus of variations. There is no consensus on a single method or interpretation, and multiple viewpoints are presented.

Contextual Notes

There are limitations regarding the representation of linear operators and matrices in infinite-dimensional spaces, which some participants highlight. Additionally, the discussion references prior threads, indicating a broader context of ongoing exploration in the topic.

member 428835
Hi PF!

Given ##B u = \lambda A u## where ##A,B## are linear operators (matrices) and ##u## a function (vector) to be operated on with eigenvalue ##\lambda##, I read that the solution to this eigenvalue problem is equivalent to finding stationary values of ##(Bu,u)## subject to ##(Au,u)=1##, where ##(g,f) = \int fg##.

Can someone explain this to me, or point me in the right direction? I don't see how the two relate.
 
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Did you try applying the method of Lagrange multipliers to the stationary value problem?
 
Orodruin said:
Did you try applying the method of Lagrange multipliers to the stationary value problem?
Could you elaborate? I've seen something like this done before I think, where ##u = \sum_i \alpha_i w_i## where ##\alpha## is a constant and ##w## is a known trial function. By construction ##(w_i,w_j)=\delta_{ij}##. Then evidently we choose ##\alpha## to minimize ##(Bu,u)## (why?) under the constraint ##\sum \alpha_i^2=1##, and hence we arrive at the eigenvalue problem ##Bu=\lambda u##.
 
joshmccraney said:
Hi PF!

Given ##B u = \lambda A u## where ##A,B## are linear operators (matrices) .

Note that Linear Operator and Matrix coincide only in finite-dimensional spaces. There is no such representation in infinite -dim spaces.
 
StoneTemplePython said:
If you're still trying to tackle the problem of finding eigenvalues of ##B^{-1}A## or ##A^{-1}B## --you posted about this a few months back as I recall, then you may want to check out this thread:

https://www.physicsforums.com/threads/eigenvalues-of-the-product-of-two-matrices.588101
Yes, I definitely did ask about this a while ago. I ended up taking someone's advice on here (don't recall who it was) and used a build in function that worked great (errors were mine but shockingly also the paper's).
 

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