Let X, Y be (quasi-)Banach spaces with X ,→ Y ∩ C([0, 1]d). Then we define the quantity
%SGn (X, Y ) := inf
M ∈N:|GMsparse|≤n ϕ:Cn→Y
sup
kf |X|k≤1
kf − ϕ(f (GMsparse)|Y k, (5.2.1) which we call sparse grid sampling width. It denotes the best worst-case error for the approximation of functions belonging to the unit ball of X by algorithms that can be described as a composition of a (possibly non-linear) reconstruction map ϕ : Cn → Y and an information map, which are in our case simply the functions values of f on a sparse grid GMsparse with |GMsparse| ≤ n. This quantity is a special restriction of the IBC worst case error for standard information [84, 85, 86]. They were introduced in [35], where the focus is on X = Sp,θr B([0, 1]d) and Y = Lq([0, 1]d). We use this results for the case X = SprW ([0, 1]d) and Y = Lq([0, 1]d). The following Lemma describes a method to bound this quantity from below.
Lemma 5.11. For 1 < p, q < ∞ (q = ∞) and r > 1p a lower bound is provided by
%SGn (SprW ([0, 1]d)), Lq([0, 1]d)) & inf
M ∈N:|GMsparse|≤n sup
kf |SrpW ([0,1]d)k≤1 f (x)=0,∀x∈GsparseM
kf |Lq([0, 1]d)k.
Proof. Let ϕ : Cn→ Lq([0, 1]d) be an arbitrary reconstruction map and kf |SprW ([0, 1]d)k ≤ 1 with f (x) = 0, ∀x ∈ GMsparse with |GMsparse| ≤ n. Then
kf |Lq([0, 1]d)k = 1
2(f − ϕ(0)) − 1
2(−f − ϕ(0))
Lq([0, 1]d
≤ 1
2kf − ϕ(0)|Lq([0, 1]dk +1
2k − f − ϕ(0)|Lq([0, 1]dk.
Finally either kf − ϕ(0)|Lq([0, 1]dk ≥ kf |Lq([0, 1]dk or k − f − ϕ(0)|Lq([0, 1]dk ≥ kf |Lq([0, 1]dk. That proves the claim.
Remark 5.12. The sparse grid structure plays no essential role in the proof provided in Lemma 5.11. Later the same arguments will be applied to obtain lower bounds for the worst case error for standard information.
Remark 5.13. It is easy to check that nestedness properties of the points xj,k (for different levels j) allow us to write the sparse grid of order M as
GMsparse = {(2−j1k1, . . . , 2−jdkd) : k ∈
×
d i=1{0, . . . , 2ji}, |j|1 = M }. (5.2.2) Theorem 5.14. Let 1 < q ≤ p < ∞ and max{1p,12} < r < 2. Then we can estimate as follows
%SGn (SprW ([0, 1]d), Lq[(0, 1)]d) sup
kf |SprW ([0,1]d)k
kf −IMf |Lq([0, 1]dk (n−1logd−1n)rlogd−12 with rank IM n Md−12M.
Proof. Inserting the relation n := |GMsparse| Md−12M into Theorem 5.4 gives the upper bound. Next we prove the lower bound. For that purpose we consider the bump function
b(x) = e−x(1−x)1 e14 (5.2.3) which is a L∞-normalized C0∞-function. We denote by
bj,k =
d
Y
i=1
b(2jixi− ki) (5.2.4)
its j-th dilation and k-th tensorized translation. Obviously supp bj,k = and that due to disjoint supports
we can estimate using Theorem 4.34 kϕ1|SprW ([0, 1]d)k . M−d−12 the definition of GMsparse in (5.0.1)). This allows us to estimate
%SGn (SprW ([0, 1]d), Lq([0, 1]d)) ≥ kϕ1|Lq([0, 1]d)k ≥ kϕ1|L1([0, 1]d)k.
The relation in (5.2.7) yields
%SGn (SprW ([0, 1]d), Lq([0, 1]d)) & 2−M rM−d−12 That finishes the proof.
Theorem 5.15. Let 1 < p < q < ∞ and 1p < r < 2 + 1p − 1q then
%SGn (SprW ([0, 1]d), Lq([0, 1]d)) sup
kf |SprW ([0,1]d)k
kf −IMf |Lq([0, 1]d)k (n−1logd−1n)r−(1p−1q)
with rank IM n Md−12M.
Proof. Inserting the relation n |GMsparse| 2MMd−1 into Theorem 5.5 proves the upper bound. We prove the lower bound now. Let bj,k as in (5.2.4). We define
ϕ2 := 2−(r−1p)Mb(M +1,0,...,0),(0,...,0) (5.2.8) Theorem 4.34 together with (5.2.6) yields
kϕ2|SprW ([0, 1]d)k . 1.
Again, by construction ϕ2(x) = 0 for all x ∈ SG(M ). This allows us to estimate
%SGn (SprW ([0, 1]d), Lq([0, 1]d)) ≥ kϕ2kq
2−(r−1p)Mkb(M +1,0,...,0),(0,...,0)|Lq([0, 1]d)k.
Finally inserting the estimate in 5.2.6 gives
%SGn (SprW ([0, 1]d), Lq([0, 1]d)) & 2−(r−p1+1q)M (n−1logd−1n)r−(p1−1q). That proves the claim.
Theorem 5.16. Let 1 < p < ∞ and 1p < r < 2 + 1p. Then
%SGn (SprW ([0, 1]d), L∞([0, 1]d)) sup
kf |SprW ([0,1]d)k
kf − IMf |Lq([0, 1]d)k
(n−1logd−1n)r−1p(logd−1n)1−1p with rank IM n Md−12M.
Proof. Inserting the relation n |GMsparse| 2MMd−1 into Theorem 5.6 proves the upper bound. We prove the lower bound now. Let bj,k as in (5.2.4). We define
ϕ3 := M−d−1p 2−M (r−1p) X
|j|1=M
bj,(0,...,0) (5.2.9)
and distinguish the cases 1 < p ≤ 2 and 2 < p < ∞. In case 1 < p ≤ 2 Lemma 3.4 and Theorem 4.34 yield
kϕ3|SprW ([0, 1]d)k . kϕ3|Sp,pr B([0, 1]d)k . M−d−1p X
|j|1=M
1
| {z }
.Md−1
1p . 1.
In case 2 < p < ∞ the non-compact embedding in Lemma 3.5 and Theorem 4.34 yield kϕ3|SprW ([0, 1]d)k . kϕ3|Sr+
1 2−1
p
2,p B([0, 1]d)k . M−d−1p X
|j|1=M
1
| {z }
.Md−1
1p . 1.
Again, by construction ϕ3(x) = 0 for all x ∈ SG(M ). This allows us to estimate
%SGn (SprW ([0, 1]d), L∞([0, 1]d)) ≥ kϕ3k∞
M(d−1)(1−1p)2−M (r−1p)kbj,(0,...,0)|L∞([0, 1]d)k
| {z }
=1
.
Finally inserting the relation n Md−12M gives
%SGn (SprW ([0, 1]d), L∞([0, 1]d)) & 2−(r−p1)MM(d−1)(1−1p)
(n−1logd−1n)r−1p(logd−1n)1−1p. That proves the claim.
Remark 5.17. In the limiting case with r = 2 and p ≥ q (or r = 2 + 1p − 1q in case p < q) we are not able to prove sharp bounds for
sup
kf |SprW ([0,1]d)k
kf − IMf |Lq([0, 1]d)k.
We obtain logarithmic gaps between the upper bounds and the lower bounds for sparse grid sampling widths obtained in Theorems 5.14 and 5.15 (which are valid also for r ≥ 2).
5.3 Sampling recovery in the energy-norm
For the rest of this chapter we are interested in measuring sampling errors in the energy norm H1([0, 1]d) := W21([0, 1]d). The interest in this setting is motivated by the convergence analysis of Galerkin methods. Energy sparse grids depend on the ratio of the smoothness in the model and the target space. This point sets can be defined as
G∆energy
α,β(M ) := {(2−j1k1, . . . , 2−jdkd) : k ∈ Dj, j ∈ Nd−1, α|j|1− β|j|∞ ≤ M }.
where α and β are the mentioned degrees of freedom. The first reference where we could find this approach is the PhD thesis of Knapek [67]. Sampling in combination with measuring the error in the energy norm was also considered in [8], [9], [10], [30]
and [48]. We continue considering the sampling operator I∆α,β(M )f (x) := X
j∈∆α,β(M )
X
k∈Dj
dj,k(f )vj,k (5.3.1)
with defi-nition one can easily verify that
|G∆energy
α,β(M )| X
j∈∆α,β(M )
2|j|1
which gives under the conditions of Lemma C.22
|G∆energy
α,β(M )| 2α−βM .
For this operator we can prove the following convergence theorem.
Theorem 5.18. Let 1 < p < ∞ and
Then there exists a constant Cε > 0 (independent of f and M ) such that
kf − I∆α,β(M )f |H1([0, 1]d)k ≤ Cε2−Mkf |SprW ([0, 1]d)k (5.3.3)
Proof. We expand f into the series (4.4.3) kf − I∆α,β(M )f |H1([0, 1]d)k .
holds (the finite overlap of directions i0 with ji0 = −1 causes no problems). According to Lemma 3.14, we have
kvj,k|H1([0, 1]d)k kvj,k|L2([0, 1]d)k +
Obviously,
kvj,k|L2([0, 1]d)k 2−|j|12 . Similar elementary calculations as above yield
∂
∂xivj,k
L2([0, 1]d)
.2ji−|j|12 . Combining both estimates gives
kvj,k|H1([0, 1]d)k . 2|j|∞−|j|12 . Inserting this and applying H¨older’s inequality yields
kf − I∆α,β(M )f |H1([0, 1]d)k
. X
j /∈∆α,β(M )
2|j|∞−|j|12 X
k∈Dj
|dj,k(f )|212
. (5.3.4)
. X
j /∈∆α,β(M )
2−2[(r−(1p−12)+)|j|1−|j|∞]12 X
j /∈∆α,β(M )
22(r−12−(1p−12)+)|j|1 X
k∈Dj
|dj,k(f )|212 . (5.3.5) Inserting the estimate from Lemma C.23 gives
kf − I∆α,β(M )f |H1([0, 1]d)k ≤ 2−M X
j /∈∆α,β(M )
22(r−12−(1p−12)+)|j|1 X
k∈Dj
|dj,k(f )|212 .
We apply Theorem 4.19 and obtain
kf − I∆α,β(M )f |H1([0, 1]d)k . 2−Mkf |Sr−(
1 p−12)+
2 W ([0, 1]d)k.
In case p = 2 we are done. In case p > 2 we finish with the trivial embedding SprW ([0, 1]d) ,→ S2rW ([0, 1]d).
In case p < 2 we apply Lemma 3.4, (vi) (diagonal embedding) that yields kf − I∆α,β(M )f |H1([0, 1]d)k . 2−Mkf |SprW ([0, 1]d)k.
Remark 5.19. The parameter ε in Theorem 5.18 can be interpreted as a degree of freedom. Its explicit choice influences the constant Cε and in the other way around the constant for the number of sampling nodes used by I∆α,β(m) according to Lemma C.22.
Finally we state a result dealing with r = 2 + (1p − 12)+. This result was originally obtained in [8, Theorem 3.8] for p = 2. Nevertheless the arguments there seem to contain a problematic step. We provide an alternative proof using Faber-Schauder sampling representations.
Theorem 5.20. There exists a constant Cε > 0 (independent of f and M ) such that kf − I∆α,β(M )f |H1([0, 1]d)k ≤ Cε2−Mkf |S2+(
1 p−1
2)+
2 W ([0, 1]d)k (5.3.6) holds with
α = 2 −1 p −1
2
+− ε and β = 1 − ε where
0 < ε < 1.
Proof. We proceed similar as in the proof of Theorem 5.18 and obtain the equivalent formulation of (5.3.4)
kf − I∆α,β(M )f |H1([0, 1]d)k . X
j /∈∆α,β(M )
2|j|∞
X
k∈Dj
dj,k(f )χj,k
L2([0, 1]d) . This can be estimated by
kf − I∆α,β(M )f |H1([0, 1]d)k
. sup
j /∈∆α,β(M )
22|j|1
X
k∈Dj
dj,k(f )χj,k
L2([0, 1]d)
X
j /∈∆α,β(M )
2−(2|j|1−|j|∞).
We apply Theorem 4.30 and obtain
kf − I∆α,β(M )f |H1([0, 1]d)k . kf |S22W ([0, 1]d)k X
j /∈∆α,β(M )
2−(2|j|1−|j|∞).
The estimate for the sum in Lemma (C.23) gives
kf − I∆α,β(M )f |H1([0, 1]d)k . 2−Mkf |S22W ([0, 1]d)k.
In case p = 2 we are done. In case p > 2 we finish with the trivial embedding Sp2W ([0, 1]d) ,→ S22W ([0, 1]d).
In case p < 2 we apply Lemma 3.4, (vi) (diagonal embedding) that yields kf − I∆α,β(M )f |H1([0, 1]d)k . 2−Mkf |S2+
1 p−12
p W ([0, 1]d)k.
That finishes the proof.