Linear Algebra: Geometric Interpretation of Self-Adjoint Operators

In summary, the conversation discusses the geometric meaning of a statement involving self-adjoint operators and eigenvalues. It is stated that if there exists a unit vector v, such that the operator T applied to it is close to being a multiple of itself (lambda*v), then the value lambda is also close to being an eigenvalue of T. This is similar to the concept of a function converging to a certain value.
  • #1
smithg86
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Homework Statement


I'm not interested in the proof of this statement, just its geometric meaning (if it has one):

Suppose [tex] T \in L(V) [/tex] is self-adjoint, [tex]\lambda \in[/tex] F, and [tex]\epsilon > 0[/tex]. If there exists [tex]v \in V[/tex] such that [tex]||v|| = 1 [/tex] and [tex] || Tv - \lambda v || < \epsilon[/tex], then [tex]T[/tex] has an eigenvalue [tex]\lambda '[/tex] such that [tex]| \lambda - \lambda ' | < \epsilon[/tex].

Homework Equations


n/a

The Attempt at a Solution


I thought this was similar to the statement that a function f(x) converges to a certain value(?)
 
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  • #2
Eigenvectors are vectors v, such that T(v) is a multiple of v and the the eigenvalues are those constant multiples. This says that if you can find a unit vector v, such that T(v) is 'almost' a multiple of itself (lambda*v), then lambda is 'almost' an eigenvalue.
 
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1. What is the geometric interpretation of self-adjoint operators in linear algebra?

The geometric interpretation of self-adjoint operators in linear algebra is that they represent linear transformations that preserve the angle between vectors. This means that the transformation does not change the shape or orientation of the vectors, but only their length.

2. How are self-adjoint operators related to symmetric matrices?

Self-adjoint operators are closely related to symmetric matrices. In fact, every self-adjoint operator has a corresponding symmetric matrix, and vice versa. This means that the properties and operations of self-adjoint operators can also be applied to symmetric matrices.

3. What are some real-life applications of self-adjoint operators?

Self-adjoint operators have many applications in various fields, such as quantum mechanics, signal processing, and computer graphics. In quantum mechanics, self-adjoint operators are used to represent observables like energy and momentum. In signal processing, they are used to analyze and filter signals. In computer graphics, they are used for transformations and animations of 3D objects.

4. How do you determine if an operator is self-adjoint?

To determine if an operator is self-adjoint, you can use the self-adjointness test. This involves checking if the operator's matrix representation is equal to its transpose. If the two are equal, then the operator is self-adjoint. Another way is to check if the operator's eigenvalues are all real numbers. If they are, then the operator is self-adjoint.

5. Can a self-adjoint operator have complex eigenvalues?

No, a self-adjoint operator cannot have complex eigenvalues. This is because complex eigenvalues would result in non-real eigenvectors, which would violate the property of self-adjoint operators to preserve the angle between vectors. Therefore, all eigenvalues of a self-adjoint operator must be real numbers.

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