Invariance - Normal Linear Transformation

In summary: No, I don't think the invariant subspace problem is equivalent to the orthogonal complement problem. The orthogonal complement problem asks whether every subspace is its own orthogonal complement, while the invariant subspace problem asks whether every subspace is reducible to a smaller subspace.
  • #1
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Homework Statement
Let W be a complex finite dimensional vector space with a hermitian scalar product and let T: W -> W be linear and normal. Prove that U is a T-invariant subspace of W if and only if V is a T*-invariant subspace, where V is the orthogonal complement of U.

The attempt at a solution
Let u in U and v in V. If U is T-invariant, then (Tu, v) = 0. But (Tu, v) = (u, T*v) so T*v is in V and V is T*-invariant. Similarly, if V is T*-invariant, then U is T-invariant. Note that I haven't used the fact that T is normal. It seems to me that T doesn't have to be normal. Am I wrong?
 
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  • #2
No, you are absolutely correct. However I think there is a typo in the problem: it should have said "V is a T-invariant subspace" instead of "V is a T*-invariant subspace" (which is trivial, as you noted). This modified exercise is a bit more challenging, and does require the fact that T is normal. :)
 
  • #3
How did you spot that? I'll try to solve the modified exercise.
 
  • #4
I spotted it because I'm an invariant subspace aficionado. ;) In fact the converse is also true: if an operator T on a finite dimensional complex inner product space has the property that [itex]U^\perp[/itex] is T-invariant whenever U is, then T must be normal.

A subspace U is said to be reducing if both it and its ortho complement are invariant. The reason for this terminology follows from the observation that if U is reducing for T, then the matrix for T with respect to the decomposition [itex]U \oplus U^\perp[/itex] is block diagonal, so that in some sense U "reduces" T to something simpler. We can thus restate your exercise and its converse succinctly as: T is normal iff every T-invariant subspace is reducing.

...at least this is the case in finite dimensions. In infinite dimensions, i.e. in the setting of Hilbert spaces, it is actually not known if this is true; in fact, this problem is equivalent to one of the most notorious open problems in analysis: the invariant subspace problem.
 

1. What is invariance in normal linear transformation?

Invariance in normal linear transformation refers to the property where the shape and orientation of a pattern or object remains the same after undergoing a linear transformation, such as rotation or translation.

2. Why is invariance important in scientific research?

Invariance is important in scientific research because it allows researchers to study patterns and objects without being affected by changes in their shape or orientation. This makes it easier to identify and analyze underlying patterns and relationships in data.

3. How is invariance achieved in normal linear transformation?

Invariance is achieved in normal linear transformation by using orthogonal matrices, which preserve the length and angle of vectors in a transformation. This ensures that the transformed pattern or object remains the same as the original, except for its position and orientation.

4. What are some real-world applications of invariance in normal linear transformation?

Invariance in normal linear transformation has many practical applications, such as image recognition in computer vision, signal processing in telecommunications, and data analysis in statistics. It is also used in fields like physics, engineering, and economics to study patterns and relationships in data.

5. Can invariance ever be broken in normal linear transformation?

Invariance can be broken in normal linear transformation if the transformation is not done properly. For example, if a transformation involves scaling or shearing, the shape and orientation of the pattern or object will change, and invariance will be broken. It is important to use orthogonal matrices and follow proper transformation techniques to maintain invariance in normal linear transformation.

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