Projections on Banach and Hilbert spaces

In summary, there are two different definitions for a projection in both Banach and Hilbert spaces. In Hilbert space, it is common to require that the projections be "orthogonal" and self-adjoint. The projections are also required to be bounded, and in Hilbert space, the norm of a projection is always equal to 1. However, this is not necessarily true in a general inner product space.
  • #1
jostpuur
2,116
19
I've now encountered two different definitions for a projection.

Let X be a Banach space. An operator P on it is a projection if P^2=P.

Let H be a Hilbert space. An operator P on it is a projection if P^2=P and if P is self-adjoint.

But the Hilbert space is also a Banach space, and there's two different definitions for projections then. Are these common definitions anyway?
 
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  • #2
in hilbert space angles make sense whilst in banach space they do not. so in hilbert space it is reasonable to require that the projections be "orthogonal" in the sense that the kernel be orthogonal to the image.

this is guaranted by making the projections self adjoint. say are your projections also required to be bounded? or is that automatic?
 
  • #3
mathwonk said:
say are your projections also required to be bounded? or is that automatic?

The text mentions "bounded linear operator" once in the beginning, and from then on talks only about "linear operators". I think that linear operator here always means a bounded linear operator, so... I would interpret this so that the projections are required to be bounded, because in the definition "linear operators" are defined to be projections with P^2=P and P^*=P conditions.

Is it common to call also non-bounded linear mappings "linear operators"? hmh... oh that is common, because derivative operators are linear operators :confused:

I somehow got an impression that ||P||=1 always, but now when we started talking about this, I'm not sure how to prove this. Is this true only in inner product spaces, or in Hilbert spaces?
 
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  • #4
Argh! I think I've just lived with a misunderstanding for weeks I thought that [itex]\mathcal{L}(X,Y)[/itex] is the set of bounded linear mappings, but in reality that is the set of all linear mappings?
 
  • #5
L(X,Y) usually refers to the set of all linear maps from X to Y; B(X,Y) (or C(X,Y)) are the bounded ones.

The zero map is a projection (both in the Banach space and Hilbert space sense), and ||0|| = 0. On the other hand if P is a nonzero projection, then
(1) ||P|| = ||P^2|| <= ||P||^2, which implies that ||P|| >= 1; and
(2) ||x||^2 = ||Px + (1-P)x||^2 = ||Px||^2 + ||(1-P)x||^2, which implies that ||Px|| <= ||x||, and thus ||P|| <= 1.

(1) & (2) combined give us that ||P||=1. This proof is only valid on a Hilbert space, but not necessarily on a general inner product space. Do you see why?
 
  • #6
morphism said:
L(X,Y) usually refers to the set of all linear maps from X to Y; B(X,Y) (or C(X,Y)) are the bounded ones.

The zero map is a projection (both in the Banach space and Hilbert space sense), and ||0|| = 0. On the other hand if P is a nonzero projection, then
(1) ||P|| = ||P^2|| <= ||P||^2, which implies that ||P|| >= 1; and
(2) ||x||^2 = ||Px + (1-P)x||^2 = ||Px||^2 + ||(1-P)x||^2, which implies that ||Px|| <= ||x||, and thus ||P|| <= 1.

(1) & (2) combined give us that ||P||=1. This proof is only valid on a Hilbert space, but not necessarily on a general inner product space. Do you see why?

I suppose my first explanation was wrong. Since nobody has been quoting it yet, I'm changing it.

The proof assumes (Px|x-Px)=0. If P is self-adjoint, then (Px|Px)=(P^2x|x)=(Px|x) -> (Px|x-Px)=0. I suppose that doesn't come any other way.
 
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Related to Projections on Banach and Hilbert spaces

1. What is the definition of a projection on a Banach space?

A projection on a Banach space is a linear operator that maps the space onto a closed subspace, and the image of the operator is equal to the subspace itself. In simpler terms, a projection on a Banach space is a way of "projecting" a vector onto a smaller subspace within the space.

2. How is a projection on a Banach space different from a projection on a Hilbert space?

The main difference between a projection on a Banach space and a projection on a Hilbert space is the type of space they operate on. A Banach space is a complete normed vector space, while a Hilbert space is a complete inner product space. This means that projections on Banach spaces can have more general properties compared to projections on Hilbert spaces.

3. Can a projection on a Banach space be non-linear?

No, a projection on a Banach space is always a linear operator. This is because a projection must preserve the vector space operations of addition and scalar multiplication, and a non-linear operator would not satisfy this property.

4. What is the significance of projections on Banach and Hilbert spaces in functional analysis?

Projections on Banach and Hilbert spaces play a crucial role in understanding the structure and properties of these spaces. They also have applications in various areas of mathematics, such as approximation theory, optimization, and differential equations.

5. How are projections on Banach and Hilbert spaces used in practical applications?

Projections on Banach and Hilbert spaces have practical applications in many fields, including physics, engineering, and economics. For example, they are used in signal processing to extract important information from noisy signals, and in statistics to find the best fit for a set of data points. They are also used in machine learning algorithms for dimensionality reduction and data clustering.

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