Hilbert space & orthogonal projection

In summary, the conversation discusses the existence of a continuous linear operator A in a real Hilbert space H such that a bilinear form a can be represented as (Ax,y) for all x,y in H. It also explores the existence of a constant r for which the operator T defined by T(x)=P_C(rz-rAx+x) is a contraction. The conversation mentions attempts to use inequalities to prove this, but it is unclear if the r used in the definition of T is the same as the one used in the definition of a contraction mapping.
  • #1
quasar987
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[SOLVED] Hilbert space & orthogonal projection

Homework Statement



Let H be a real Hilbert space, C a closed convex non void subset of H, and a: H x H-->R be a continuous coercive bilinear form (i.e.
(i) a is linear in both arguments
(ii) There exists M [itex]\geq[/itex] 0 such that |a(x,y)| [itex]\leq[/itex] M||x|| ||y||
(iii) There exists B>0 such that a(x,x) [itex]\geq[/itex] B||x||^2

(a) Show that the exists a continuous linear operator A:H-->H such that a(x,y)=(Ax,y) for all x,y in H.

(b) Let z be in H. Show that the exists a constant r>0 such that the operator T:C-->C defined by [itex]T(x)=P_C(rz-rAx+x)[/itex] is a contraction, i.e., ||T(x) - T(y)|| [itex]\leq[/itex] k||x - y|| for some constant k < 1. P_C is the operator "orthogonal projection on C", i.e. is the (unique) element of C such that [itex]d(z,P_C(z)) = d(z,C)[/itex].

The Attempt at a Solution



(a) is done. The operator in question is obtained via Riesz's representation theorem: we choose Ax to be the unique element of H such that a(x,y) = (Ax,y) for all y. Linearity and continuity of A follow from the corresponding properties of a.

(b) I already know that the orthogonal projection map is non expensive: for all x,y in H, [tex]||P_C(x) - P_C(y)|| \leq ||x - y||[/tex].

So [tex]||T(x) - T(y)|| \leq ||x-y-rAx+rAy||[/tex]

And here I've tried using every inequality I know but with no luck. Clearly, we need something stronger than the triangle inequality, because

[tex]||x-y-rAx+rAy||\leq ||x-y|| + r||A(x-y)||\leq (1+r||A||)||x-y||[/tex],

which is stricly greater than ||x - y||...
 
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  • #2
Do you know whether the r used in the definition of T is the same r used in the defintion of a contraction mapping? E.g., I could have written ||T(x) - T(y)|| < k||x - y|| for some constant k < 1.
 
  • #3
They are not necessarily the same constant! I apologize for the confusion and I have edited the OP. Thanks for pointing it out.
 

What is Hilbert space?

Hilbert space is a mathematical concept that describes a complete vector space with an inner product. It is a generalization of Euclidean space and is used in functional analysis and other areas of mathematics.

What is orthogonal projection?

Orthogonal projection is a mathematical operation that projects a vector onto a subspace in such a way that the projected vector is perpendicular (orthogonal) to the subspace. It is commonly used in linear algebra and geometry.

What is the relationship between Hilbert space and orthogonal projection?

Hilbert space is often used as the underlying space for orthogonal projection. This is because Hilbert spaces have a complete inner product structure, making them well-suited for studying projections and other linear transformations.

What is the role of orthogonal projection in quantum mechanics?

In quantum mechanics, orthogonal projection plays a crucial role in the measurement process. The projection of a quantum state onto a particular basis represents the probability of measuring that state in that basis. Additionally, orthogonal projection is used in the definition of quantum operators and their properties.

How is orthogonal projection used in signal processing?

In signal processing, orthogonal projection is used for data compression and noise reduction. By projecting a signal onto a subspace, the signal can be represented in a lower-dimensional space without significant loss of information. This is particularly useful for reducing the size of large datasets or removing unwanted noise from a signal.

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