Functional analysis, projection operators

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Homework Statement



I want to understand the proof of proposition 7.1 in Conway. The theorem says that if [itex]\{P_i|i\in I\}[/itex] is a family of projection operators, and [itex]P_i[/itex] is orthogonal to [itex]P_j[/itex] when [itex]i\neq j[/itex], then for any x in a Hilbert space H,

[tex]\sum_{i\in I}P_ix=Px[/tex]

where P is the projection operator for the closed subspace that's spanned by the members of the Pi(H).

Homework Equations



Suppose that [itex]P_M[/itex] and [itex]P_N[/itex] are projection operators for closed subspaces M and N respectively. Then

(a) [itex]P_M+P_N[/itex] is a projection operator for [itex]M\oplus N[/itex] if and only if M and N are orthogonal.
(b) [itex]P_MP_N[/itex] is a projection operator for [itex]M\cap N[/itex] if and only if [itex][P_M,P_N]=0[/itex].
(c) [itex]P_M-P_N[/itex] is a projection operator for [itex]M\cap N^\perp[/itex] if and only if [itex]N\subset M[/itex].

The Attempt at a Solution



For each finite [itex]F\subset I[/itex], define

[tex]S_F=\sum_{i\in F}P_ix[/tex]

The map [itex]F\mapsto S_F[/itex] is a net, and I need to show that it converges to Px. So given [itex]\varepsilon>0[/itex], I want to find a finite [itex]F_0\subset I[/itex] such that

[tex]F\geq F_0\Rightarrow \|S_F-Px\|<\varepsilon[/tex]

I think I've shown that the norm on the right is equal to

[tex]\|P_{M_{I-F}}x\|[/tex]

where [itex]M_{I-F}[/itex] is the closed subspace spanned by the vectors in the [itex]P_i(H)[/itex] with [itex]i\in I-F[/itex]. But then what? I still don't see how to pick an F that makes the above as small as I want.
 
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Allright, I think I finally found it. First, let us fix some notation: let [tex]M_i=P_i(H)[/tex] and [tex]M=P(H)[/tex]. Let's focus on this question first:

For [tex]\epsilon>0[/tex], find a finite set [tex]F_0\subseteq I[/tex] such that

[tex]\|Px-\sum_{i\in F_0}{P_ix}\|<\epsilon[/tex]

We know that [tex]Px\in M[/tex], and we know that [tex]M=\overline{span\{M_i~\vert~i\in F_0\}}[/tex]. Thus there exist a finite set F0 and vectors [tex]m_i\in M_i[/tex] for [tex]i\in F_0[/tex], such that [tex]\|Px-\sum_{i\in F_0}{m_i}\|<\epsilon[/tex].

Now, do the following:

[tex]\|Px-\sum_{i\in F_0}{P_ix}\|\leq \|Px-\sum_{i\in F_0}{m_i}\|+\|\sum_{i\in F_0}{m_i}-\sum_{i\in F_0}{P_ix}\|[/tex]

I'll let you continue from here... It is of course obvious that you should try to find an upper bound to [tex]\|m_i-P_ix\|[/tex]...
 
micromass said:
We know that [tex]Px\in M[/tex], and we know that [tex]M=\overline{span\{M_i~\vert~i\in F_0\}}[/tex]. Thus there exist a finite set F0 and vectors [tex]m_i\in M_i[/tex] for [tex]i\in F_0[/tex], such that [tex]\|Px-\sum_{i\in F_0}{m_i}\|<\epsilon[/tex].
Aha. (I assume the first [itex]\in F_0[/itex] should be [itex]\in I[/itex]). Px is in the closure of the set of linear combinations of members of the Mi, and that means that every open ball around Px contains one of those linear combinations. Very clever. (Nothing like this occurred to me when I was thinking about this problem).

micromass said:
[tex]\|Px-\sum_{i\in F_0}{P_ix}\|\leq \|Px-\sum_{i\in F_0}{m_i}\|+\|\sum_{i\in F_0}{m_i}-\sum_{i\in F_0}{P_ix}\|[/tex]

I'll let you continue from here... It is of course obvious that you should try to find an upper bound to [tex]\|m_i-P_ix\|[/tex]...
I actually have to go to bed, but I'll continue tomorrow. Thank you very much.
 
I'm so dumb I wasn't able to see how to use the other thing you hinted, but I found a way to solve the problem (using your first hint). Perhaps not the most elegant solution. I decided to express Px as a sum of vectors that belong to the Mi plus a "remainder" that's orthogonal to the other terms.

[tex]\varepsilon^2>\Big\|\sum_{i\in F_0}m_i-Px\Big\|^2=\Big\|\sum_{i\in F_0}(m_i-P_i x)+(Px)^\perp_{F_0}\Big\|^2=\sum_{i\in F_0}\|m_i-P_ix\|^2+\|(Px)^\perp_{F_0}\|^2\geq \|(Px)^\perp_{F_0}\|^2[/tex]

Then I realized that what I called [itex](Px)^\perp_{F_0}[/itex] here is what I called [itex]P_{M_{I-F}}x[/itex] in my first post. So

[tex]\Big\|\sum_{i\in F_0}P_ix-Px\big\|=\|P_{M_{I-F}}x\|=\|(Px)^\perp_{F_0}\|<\varepsilon[/tex]

And then the rest wasn't too hard. If [itex]F\geq F_0[/itex], then [itex]I-F\subset I-F_0[/itex] and

[tex]\Big\|\sum_{i\in F}P_ix-Px\big\|=\|P_{M_{I-F}}x\|=\|P_{M_{I-F}}P_{M_{I-F_0}}x\|\leq\|P_{M_{I-F_0}}x\|<\varepsilon[/tex]

Thanks again for the help. If your solution differs significantly from mine, I would be interested in seeing it.