3. Integral Operator Approximations
Let
. The semi-open intervals of equal lengths
, nh = 1,
,
together with the open
are defining for
a partition of (0,1), k=1,n,
. As in [5], to build the subspaces with their union a dense set, consider the interval indicator functions with the supports of these intervals (k=1,n):
Consider now, the family
F of finite dimensional subspaces
that are the linear spans of interval indicator functions of the h-partitions defined by (4) with disjoint supports, i.e. pairwise disjoint,
, built on a multi-level structure and denote
, their union that is a dense set in
H well known in literature.
As an exercise in [9], the author shows the density of step functions built on functions of indicators of intervals pairwise disjoint in where is a domain in . In [5] and [6] the authors used the dense set built on (4) with the open subintervals respectively with the closed subintervals partitions, to obtain the best rate of convergence to zero of the eigenvalues of integral operators with Mercer kernels ([8]). Choosing open subintervals or closed subintervals for partitioning the domain, the unions of the subspaces generated in both cases are also dense ([1]): if one from three sets is dense, then the other two sets are dense.
Note 1.
a) The family F is an including family of subspaces.
b) S is dense and, with .
c) For any the functions , not necessarily in S have the support : for ).
Proof.
a). The including property is obtained from (4) by halving the mesh h, observing that any could be embed into as per: .
b). For any , from an index its orthogonal projection , . Here, . From a) we have . So, because the best approximation of u in is given by . Therefore, is decreasing with h decreasing. We could remark that for any (equivalently with: given there exists h such that ), holds iff S is dense.
c). For , k= 1,n, (4). Then . □
3.1 Convergent operator approximations. The option for semi-open subintervals partitioning ensures the subspaces including property and, every pair of indicator subinterval functions has disjoint supports obtaining as result sparse diagonal matrix representations.
Citing [5], (pg 986), the integral operator
with the kernel function:
is a finite rank integral operator orthogonal projection having the spectrum {0, 1} with the eigenvalue 1 of the multiplicity n (nh=1) corresponding to the orthogonal eigenfunctions
. Then,
,
and,
for
. For any
,
, the constants , k=1,n being given by
.
Thus, is an orthogonal projection onto like is mentioned in [5].
Let
. Its integral operator approximation on
denoted by
is a finite rank operator approximation, with the kernel function (citing again [5]):
where the pieces
of the kernel function
in the sum have disjoint supports in
, namely
.
The operator approximations of are obtained as follows:
. So,
, being a finite rank operator approximation on obtained through schema (4)-(6).
Lemma 1.
For any integral operator , the discretization schema (4)-(6) has the properties:
a.) the sequence of operator approximations is convergent, that is: the convergence property i) holds;
b.) the matrix representations of the operator approximations over the subspaces in F are one-diagonal sparse matrices;
c.) if the diagonal entries of the matrix representations are strictly positive valued, , then the operator approximations are positive i.e. the positivity property ii) holds.
Proof. a.) From . Then,
. Because is a sequence of orthogonal projections onto a family of including subspaces whose union is dense, with holds; then the convergence of operator approximations property i) is satisfied:
for , nh=1.
b.) Now, evaluating for , we obtain
=
where .
Because has the support (Note 2.c), has the support and for , we obtain
. Thus, the matrix representation of the finite rank operator on the basis of , is:
where
and
for any pair (i,j),
showing that the matrix is 1-diagonal.
c.) Given , and from , we obtain:
where
is the positivity parameter of the operator approximation
,
that is strict positive for any
because the minimum from a finite number of strict positive quantities
,
can not be zero. □
Lemma 2. (Main Criterion)
If the operator approximations of on schema (4)-(6) are positive and its sequence of positivity parameters is bounded,
then .
Proof. The sequence converges using the approximation schema for (i.e. i) holds). Suppose that this sequence is also positive, that is , , i.e. ii) holds. Let the positivity parameters in (8) be bounded, that is exists such that , , i.e. iii) holds. Then applying Theorem 2, . □
Lemma 3. (Criterion for positive valued kernels)
If the kernel of the integral operator is strictly positive valued on (0,1)2 except for a set of measure Lebesgue 0 and the operator approximations sequence of the positivity parameters verifies for some
iii) for any , nh=1
then .
Proof. The sequence converges using the approximation schema for . Now, let for any pair (x,y). Because the set of the points (x,y) in the domain of for which the kernel is null are of measure Lebesgue zero, in (7) for any k=1,n, nh=1. Moreover, on a finite dimension subspace , and, from , ii) holds i.e. we have the positivity of the operator approximations , , nh=1. Then from iii), invoking Theorem 2, . □