Are orthonormal eigenbases unique?

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Discussion Overview

The discussion centers on the uniqueness of orthonormal eigenbases for operators on finite-dimensional inner product spaces, particularly focusing on the implications of orthogonal diagonalization and the properties of eigenspaces. The scope includes theoretical considerations and mathematical reasoning related to linear algebra and eigenvalue problems.

Discussion Character

  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant notes that while an orthonormal basis is not unique due to the ability to scale vectors and extend bases, they question the uniqueness specifically of orthonormal eigenbases.
  • Another participant suggests considering the identity map as an example where distinct orthonormal bases exist, implying that uniqueness does not hold.
  • A different participant explains that the decomposition of the vector space into eigenspaces allows for multiple choices of bases, especially when the dimension of the eigenspace is greater than one.
  • It is noted that even in one-dimensional eigenspaces, there are two unit vectors to choose from, leading to multiple orthonormal eigenbases.
  • One participant highlights that in complex spaces, there are infinitely many choices for vectors due to the ability to multiply by complex exponentials, further complicating the notion of uniqueness.
  • Another participant mentions practical considerations in engineering applications where normalization choices can lead to different but valid eigenbases.

Areas of Agreement / Disagreement

Participants generally agree that orthonormal eigenbases are not unique, with multiple competing views on the extent and nature of this non-uniqueness. The discussion remains unresolved regarding the implications of these choices on the concept of uniqueness.

Contextual Notes

Participants express uncertainty about the definitions and implications of uniqueness in the context of different fields (real vs. complex) and the dimensionality of eigenspaces. There are unresolved aspects regarding the normalization choices in practical applications.

Bipolarity
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Suppose you have an operator ## T: V → V ## on a finite-dimensional inner product space, and suppose it is orthogonally diagonalizable. Then there exists an orthonormal eigenbasis for V. Is this eigenbasis unique?

Obviously, in the case of simple diagonalization, the basis is not unique since scaling (by nonzero) any vector in an eigenbasis yields a valid eigenbasis.

Likewise, an orthonormal basis for a space of at least dimension 2 is not unique, since we can take any two nonparallel vectors in the space and extend each to its own orthonormal basis through Gram-Schmidt. The two bases must be distinct.

But what about an orthonormal eigenbasis? Is this set unique? My guess is that it is, but I need to know for sure so I can think about which direction I want to steer my proof.

Thanks!

BiP
 
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Psst...consider the identity map and two distinct orthonormal bases for V.

Edit: There are plenty of other examples too. This one is just the easiest.
 
Last edited:
You can decompose your vector space V uniquely into
[tex]V = \bigoplus_{\lambda} V_{\lambda}[/tex]
where [itex]V_{\lambda}[/itex] is the eigenspace of eigenvalue [itex]\lambda[/itex]. Inside of each eigenspace if the dimension is not equal to 1 you have a lot of freedom as to what basis you pick (as jgens example gives). Even if each eigenspace is 1 dimensional, you still have two unit vectors from each eigenspace that you can pick, so there are still 2n orthornomal eigenbases.
 
Office_Shredder said:
Even if each eigenspace is 1 dimensional, you still have two unit vectors from each eigenspace that you can pick, so there are still 2n orthornomal eigenbases.

Unless I'm missing the point completely, I suppose you mean vectors V and -V?

If you count those as "different" vectors, then if V is complex there are infinite number of vectors to pick from. You can multiply the vector by ##e^{i\theta}## for any ##\theta##.

FWIW in practical engineering eigenvalue problems, you often make a further arbitrary choice about normalization (e.g. make the element of the vector with the maximum modulus real and positive) to nail down those arbitrary but annoying differences, when comparing results from slightly different eigensolutions.
 
AlephZero said:
Unless I'm missing the point completely, I suppose you mean vectors V and -V?

If you count those as "different" vectors, then if V is complex there are infinite number of vectors to pick from. You can multiply the vector by ##e^{i\theta}## for any ##\theta##.

Oh yeah I forgot sometimes the field isn't [itex]\mathbb{R}[/itex] :redface:
 
Ahh I forgot that scaling a vector by a number on the unit disk in the complex plane (or -1 if the field is real)) preserves normalization of the vector.

Thank you guys!

BiP
 

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