Question about inner product spaces

In summary, the conversation discusses the possibility of writing an arbitrary vector \Psi in a given inner product space V as the sum of a vector \Psi^\parallel that is parallel to a fixed vector \Phi in V and a vector \Psi^\perp that is perpendicular to \Phi. It is shown that if the inner product is positive definite, then this can be done by setting \Psi^\parallel equal to \frac{(\Phi,\Psi)}{(\Phi,\Phi)}\,\Phi and \Psi^\perp equal to \Psi-\Psi^\parallel. The more general theorem states that in a Hilbert space H, for an arbitrary vector x and a closed linear subspace K, there exists a unique vector y in K
  • #1
AxiomOfChoice
533
1
Suppose you have an inner product space [itex]V[/itex] (not necessarily finite dimensional; so it could be an infinite dimensional Hilbert space or something). Fix a vector [itex]\Phi[/itex] in this space. Given an arbitrary vector [itex]\Psi \in V[/itex], can I write it as
[tex]
\Psi = \Psi^{\parallel} + \Psi^{\perp},
[/tex]
where [itex]\Psi^{\parallel}[/itex] is parallel to the given [itex]\Phi[/itex] and [itex]\Psi^{\perp}[/itex] is perpendicular to the given [itex]\Phi[/itex]?
 
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  • #2
If the inner product is positive definite, then you can. Just write:

[tex]\Psi^\parallel = \frac{(\Phi,\Psi)}{(\Phi,\Phi)}\,\Phi[/tex]

[tex]\Psi^\perp=\Psi-\Psi^\parallel[/tex]
 
  • #3
This is just the fact that

[tex]V=<\Phi>\oplus <\Phi>^\perp[/tex]

where <> denotes the span.
 
  • #4
The more general theorem says that if x is a member of a Hilbert space H, and K is a closed linear subspace of H, there's a unique y in K such that [itex]x-y\perp K[/itex]. If we define [itex]x_\parallel=y[/itex] and [itex]x_\perp=x-y[/itex], we can write [itex]x=x_\parallel+x_\perp[/itex]. The theorem also says that y is at the minimum distance from x: d(x,y)=d(x,K).

(I'm saying linear subspace to emphasize that it's a subspace of the vector space, not the Hilbert space. A closed linear subspace is a linear subspace that's also a closed set. Some authors use the term "linear subspace" only when the set is closed, and the term "linear manifold" when it may not be closed).
 
  • #5
@Fredrik: V is not assumed to be a Hilbert space (i.e. need not be complete) in this topic.
 

FAQ: Question about inner product spaces

What is an inner product space?

An inner product space is a mathematical concept that is used to define and analyze vector spaces. It is a vector space equipped with an additional operation called an inner product, which is used to measure the angle between two vectors and their lengths.

How is an inner product defined?

An inner product is a function that takes in two vectors and returns a scalar value. It satisfies certain properties such as linearity, symmetry, and positive definiteness. The most common example of an inner product is the dot product in 3-dimensional space.

What are the applications of inner product spaces?

Inner product spaces have various applications in mathematics, physics, and engineering. They are used to study vector spaces and their properties, as well as to solve problems in optimization, geometry, and quantum mechanics.

How is the concept of orthogonality related to inner product spaces?

Orthogonality is a fundamental concept in inner product spaces. Two vectors are considered orthogonal if their inner product is equal to zero, which means they are perpendicular to each other. This concept is used in various applications such as finding the shortest distance between two points and determining the basis of a vector space.

Can inner product spaces be generalized to infinite dimensions?

Yes, inner product spaces can be defined in infinite dimensions. In this case, the inner product becomes an integral instead of a sum, and the concept of orthogonality is extended to include functions and other infinite-dimensional objects. This has important applications in fields such as functional analysis and quantum field theory.

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