Abstract
In this work, we construct a well-posed first-order system least squares (FOSLS) simultaneously space-time formulation of parabolic PDEs. Using an adaptive wavelet solver, this problem is solved with the best possible rate in linear complexity. Thanks to the use of a basis that consists of tensor products of wavelets in space and time, this rate is equal to that when solving the corresponding stationary problem. Our findings are illustrated by numerical results.
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Communicated by: Ivan Oseledets
The first author has been supported by the Netherlands Organization for Scientific Research (NWO) under contract. no. 613.001.216
Appendix: Decay estimates
Appendix: Decay estimates
In this Appendix we prove the technical results Theorem A.3, Corollaries A.7, A.9, and A.11 that were used in the proof of Theorem 4.18.
The following lemma is an application of Schur’s lemma that is often used to bound the spectral norm of a matrix whose row and column indices run over index sets of multi-level bases.
Lemma A.1
For index sets\(J,J^{\prime }\),let\(|\cdot | \colon J \cup J^{\prime } \rightarrow \mathbb {N}_{0}\),and let\(M:=[m_{\lambda ^{\prime },\lambda }]_{(\lambda ^{\prime },\lambda ) \in J^{\prime } \times J}\)besuch that for someξ ≥ 0,ρ > 0,
and
Then
where \((M|_{\{(\lambda ^{\prime },\lambda )\colon |\lambda ^{\prime }|>|\lambda |+k\}})_{\lambda ^{\prime },\lambda }:=\left \{\begin {array}{cl} m_{\lambda ^{\prime },\lambda } & \text {when} |\lambda ^{\prime }|>|\lambda |+k\\ 0 & \text {otherwise} \end {array} \right .\), and ∥⋅∥ denotes the matrix spectral norm, i.e., here the norm on \(\mathcal {L}(\ell _{2}(J),\ell _{2}(J^{\prime }))\). The absolute value refers to taking entry-wise absolute value. (Similar notations will be used at other occasions.)
Proof
With \(I_{\ell ^{\prime },\ell }:=[|m_{\lambda ^{\prime },\lambda }|]_{\{(\lambda ^{\prime },\lambda )\colon |\lambda ^{\prime }|=\ell ^{\prime }, |\lambda |=\ell \}}\), we have
where \(\ell , \ell ^{\prime }\) run over \(\mathbb {N}_{0}\).
The number of non-zero entries in each column or row of \(I_{\ell ^{\prime },\ell }\) is \(\lesssim 2^{\xi (\ell ^{\prime }-\ell )}\) or \(\lesssim 1\), respectively. Using \(\|\cdot \|^{2} \leq \|\cdot \|_{1}\|\cdot \|_{\infty }\), we infer that \(\|I_{\ell ^{\prime },\ell }\|^{2} \lesssim 2^{\xi (\ell ^{\prime }-\ell )} \cdot 2^{(\frac {\xi }{2}+\rho )(\ell -\ell ^{\prime })} \cdot 1 \cdot 2^{(\frac {\xi }{2}+\rho )(\ell -\ell ^{\prime })} = 4^{\rho (\ell -\ell ^{\prime })}\). □
The next lemma concerns near-sparsity of a generalized mass matrix corresponding to two temporal wavelet bases.
Lemma A.2
For \(k \in \mathbb {N}_{0}\) , \({\Theta }^{*}, {\Theta }^{\circ } \in \{{\Theta }^{\mathscr{V}_{1}}, {\Theta }^{\mathscr{P}}, {\Theta }^{a}, {\Theta }^{\mathscr{U}}/\|{\Theta }^{\mathscr{U}}\|_{L_{2}(\mathrm {I})}\}\) we have
Proof
Using that Θ∗ satisfies (t1)–(t4), being the counterparts of (s1)–(s4) for the spatial wavelets, we split the matrix into Br + Bs, where Br contains all its entries \(\langle \theta ^{*}_{\lambda ^{\prime }}, \theta ^{\circ }_{\lambda }\rangle _{L_{2}(\mathrm {I})}\) for which \(\text {supp} \theta ^{*}_{\lambda ^{\prime }}\) is contained in ω for some \(\omega \in \mathcal {O}_{\mathrm {I}}\) with |ω| = |λ| (the ‘regular’ entries), and where Bs contains the remaining (‘singular’) entries.
The number of non-zero entries with \(|\lambda ^{\prime }|=\ell ^{\prime }\) and |λ| = ℓ in each column or row of Br is \(\lesssim 2^{\ell ^{\prime }-\ell }\) or \(\lesssim 1\), respectively. Thanks to (t4), for each of these entries we have \(|\langle \theta ^{*}_{\lambda ^{\prime }}, \theta ^{\circ }_{\lambda }\rangle _{L_{2}(\mathrm {I})}| \leq \|\theta ^{*}_{\lambda ^{\prime }}\|_{L_{1}(\mathrm {I})} 2^{-\ell ^{\prime }} |\theta ^{\circ }_{\lambda }|_{W^{1}_{\infty }(\text {supp} \theta ^{*}_{\lambda ^{\prime }})} \lesssim 2^{3(\ell -\ell ^{\prime })/2}\). An application of Lemma A.1 with ξ = ρ = 1 shows that \(\||B^{r}|\| \lesssim 2^{-k}\).
The number of non-zero entries with \(|\lambda ^{\prime }|=\ell ^{\prime }\) and |λ| = ℓ in each column or row of Bs is \(\lesssim 1\). For each of these entries, we have \(|\langle \theta ^{*}_{\lambda ^{\prime }}, \theta ^{\circ }_{\lambda }\rangle _{L_{2}(\mathrm {I})}| \leq \|\theta ^{*}_{\lambda ^{\prime }}\|_{L_{1}(\mathrm {I})} \|\theta ^{\circ }_{\lambda }\|_{L_{\infty }(\mathrm {I})} \lesssim 2^{(\ell -\ell ^{\prime })/2}\). An application of Lemma A.1 with ξ = 0, \(\rho =\frac {1}{2}\) shows that \(\||B^{s}|\| \lesssim 2^{-k/2}\). □
The following theorem provides the main ingredient for bounding \(\|\textbf {r}_{\frac {1}{2}}-{\tilde {\textbf {r}}}_{\frac {1}{2}}\|\).
Theorem A.3
Let Λa ⊂∨abea multi-tree, and\(r \in \text {span} {\Psi }^{a}\|_{{\Lambda }^{a}}\)For\(k \in \mathbb {N}_{0}\),it holds that
Proof
We write \(r={\sum }_{(\lambda ,\mu ) \in {\Lambda }^{a}} r_{\lambda \mu } \theta ^{a}_{\lambda } \otimes \sigma ^{a}_{\mu }\), and let
Writing \({\Lambda }^{\mathscr{V}_{1}}:=\vee _{\mathscr{V}_{1}}({\Lambda }^{a},k)\), from \(\frac {1}{2}(a+b)^{2} \leq (a^{2}+b^{2})\), we have that
Here, we could insert the factor \(\delta _{\lambda }(\mu ^{\prime })\) in the second sum because of the following reason: Let \((\lambda ^{\prime },\mu ^{\prime }) \in \vee _{\mathscr{V}_{1}} \setminus {\Lambda }^{\mathscr{V}_{1}}\) and \(\lambda \in {{\Lambda }^{a}_{1}}\) with \(|\lambda |\geq |\lambda ^{\prime }|-k\). If \(\text {meas}(\mathcal {S}(\theta _{\lambda ^{\prime }}^{\mathscr{V}_{1}}) \cap \text {supp} \theta ^{a}_{\lambda })= 0\), then the value of \(\delta _{\lambda }(\mu ^{\prime })\) is irrelevant. If \(\text {meas}(\mathcal {S}(\theta _{\lambda ^{\prime }}^{\mathscr{V}_{1}}) \cap \text {supp} \theta ^{a}_{\lambda })>0\), then the definition of \({\Lambda }^{\mathscr{V}_{1}}=\vee _{\mathscr{V}_{1}}({\Lambda }^{a},k)\) shows that \(|\mu ^{\prime }|>|\mu |+k\) for all \(\mu \in {{\Lambda }^{a}_{2}}(\lambda )\) with \(\text {meas}(\mathcal {S}(\sigma _{\mu ^{\prime }}^{\mathscr{V}_{1}}) \cap \text {supp} \sigma ^{a}_{\mu })>0\), meaning that \(\delta _{\lambda }(\mu ^{\prime })= 1\).
Using Lemma A.2 for \(({\Theta }^{*},{\Theta }^{\circ })=({\Theta }^{\mathscr{V}_{1}},{\Theta }^{a})\), the first sum can be bounded on a multiple of
where we used that \({\Sigma }^{\mathscr{V}_{1}}\) is a Riesz basis for \({H^{1}_{0}}({\Omega })\), and that Θa is a Riesz basis for L2(I).
To bound the second sum, recall that for μ ∈◇a, it holds that \(\text {supp} \sigma ^{a}_{\mu }=\omega _{\mu }\) for some \(\omega _{\mu } \in \mathcal {O}_{\Omega }\) with \(|\omega _{\mu }|=\max (|\mu |-1,0)\). Define the tiling \(\mathcal {T}(\lambda ) \in \mathcal {O}_{\Omega }\) as the union, over the leaves μ of the tree \({{\Lambda }^{a}_{2}}(\lambda )\), of the children of ωμ when |μ| > 0, or of ωμ itself when |μ| = 0. Then \(\text {span}\{\sigma ^{a}_{\mu } \colon \mu \in {{\Lambda }^{a}_{2}}(\lambda )\}=\mathcal {P}_{m}(\mathcal {T}(\lambda ))\), and \(\{\mu ^{\prime } \in \Diamond _{\mathscr{V}_{1}}\colon \delta _{\lambda }(\mu ^{\prime })= 1\}=\Diamond _{\mathscr{V}_{1}} \setminus \Diamond _{\mathscr{V}_{1}}(\mathcal {T}(\lambda ),k)\), cf. Definition 4.7.
Since Θa and \({\Theta }^{\mathscr{V}_{1}}\) are Riesz bases for L2(I), and so \(\langle {\Theta }^{\mathscr{V}_{1}},{\Theta }^{a} \rangle _{L_{2}(\mathrm {I})} \in \mathcal {L}(\ell _{2}(\lhd _{a}),\ell _{2}(\lhd _{\mathscr{V}_{1}}))\), invoking [34, Prop. A.1] using that \({\Sigma }^{\mathscr{V}_{1}}\) satisfies (s1)–(s4), the second sum can be bounded on a multiple of
where we used that Θa is a Riesz basis for L2(I). □
If Θa was a Riesz basis for H− 1(Ω), then in the proof of Theorem A.3 it would have been natural to write \(\langle \sigma ^{\mathscr{V}_{1}}_{\mu ^{\prime }},{\sum }_{\mu \in {{\Lambda }^{a}_{2}}(\lambda )} r_{\lambda \mu } \sigma ^{a}_{\mu }\rangle _{L_{2}({\Omega })}\) as \(\langle {\Sigma }^{\mathscr{V}_{1}},{\Sigma }^{a}\rangle _{L_{2}({\Omega })} \)\([ r_{\lambda \mu }]_{\mu \in {{\Lambda }^{a}_{2}}(\lambda )}\). In this case the approach of the insertion of the factor \(\delta _{\lambda }(\mu ^{\prime })\) would have given the bound
Although in the current setting where \(\langle {\Sigma }^{\mathscr{V}_{1}}, {\Sigma }^{a} \rangle _{L_{2}({\Omega })} \not \in \mathcal {L}(\ell _{2}(\Diamond _{a}),\ell _{2}(\Diamond _{\mathscr{V}_{1}}))\), this estimate makes not much sense, for other collections this result, formulated in the next proposition, is going to be useful.
Proposition A.4
For\(*,\circ \in \{\mathscr{U},\mathscr{V}_{1},\mathscr{P},a\}\),let\(M_{\lhd }:=[m^{\lhd }_{\lambda ^{\prime }, \lambda }]_{(\lambda ^{\prime },\lambda )} \in \mathcal {L}(\ell _{2}(\lhd _{*}),\ell _{2}(\lhd _{\circ }))\),\(M_{\Diamond }:=[m^{\Diamond }_{\mu ^{\prime }, \mu }]_{(\mu ^{\prime },\mu )} \in \mathcal {L}(\ell _{2}(\Diamond _{*}),\ell _{2}(\Diamond _{\circ }))\),where\(m^{\lhd }_{\lambda ^{\prime }, \lambda }= 0\)when\(\text {meas}(\mathcal {S}(\theta ^{\circ }_{\lambda ^{\prime }}) \cap \text {supp} \theta ^{*}_{\lambda }) = 0\),and\(m^{\Diamond }_{\mu ^{\prime }, \mu }= 0\)when\(\text {meas}(\mathcal {S}(\sigma ^{\circ }_{\mu ^{\prime }}) \cap \text {supp} \sigma ^{*}_{\mu }) = 0\).Then for a multi-tree Λ∗⊂∨∗,and\( k \in \mathbb {N}_{0}\),it holds that
The remaining of this Appendix will consist of various applications of Proposition A.4 for which in several lemmas we estimate norms of type \(\|M_{\lhd }|_{\{(\lambda ^{\prime },\lambda )\colon |\lambda ^{\prime }|>|\lambda |+k\}}\|\) or \(\|M_{\lhd }|_{\{(\lambda ^{\prime },\lambda )\colon |\lambda ^{\prime }|>|\lambda |+k\}}\|\). The next lemma deals with the first task.
Lemma A.5
For \(k \in \mathbb {N}_{0}\) , it holds that
Proof
For proving the first inequality, we split the matrix into Br + Bs, where Br contains all its entries \(\left \langle \frac {\sigma _{\mu ^{\prime }}^{\mathscr{U}}}{\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{-1}({\Omega })}},\sigma _{\mu }^{\mathscr{V}_{1}}\right \rangle _{L_{2}({\Omega })}\) for which \(\text {supp} \sigma ^{\mathscr{U}}_{\mu ^{\prime }}\) is contained in ω for some \(\omega \in \mathcal {O}_{\Omega }\) with |ω| = |μ| (the ‘regular’ entries), and where Bs contains the remaining (‘singular’) entries.
Thanks to (\({s}_{4}^{\mathscr{U}}\)), for the regular entries we can estimate
where we used \(\|\sigma _{\mu }^{\mathscr{V}_{1}}\|_{H^{1}({\Omega })} \eqsim 1\), and \(\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{L_{2}({\Omega })}^{2} \leq \|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{1}({\Omega })} \|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{-1}({\Omega })}\lesssim 2^{|\mu ^{\prime }|} \|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{L_{2}({\Omega })}\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{-1}({\Omega })}\). An application of Lemma A.1 with ξ = n and ρ = 1 shows that \(\||B^{r}|\|\lesssim 2^{-k}\).
Since the wavelets \(\sigma _{\mu }^{\mathscr{V}_{1}}\) are piecewise polynomial functions in H1(Ω), they are contained in \(W_{\infty }^{1}({\Omega })\). Using (s4), for the remaining singular entries we estimate
again by \(\|\sigma _{\mu }^{\mathscr{V}_{1}}\|_{H^{1}({\Omega })} \eqsim 1\), and \(\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{L_{2}({\Omega })} \lesssim 2^{|\mu ^{\prime }|}\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{-1}({\Omega })}\) (cf. (4.9)). An application of Lemma A.1 with ξ = n − 1 and ρ = 1/2 shows that \(\||B^{s}|\|\lesssim 2^{-k/2}\).
Moving to the second inequality, we split the matrix into Br + Bs, where Br contains all its entries \(\left \langle \frac {\frac {\partial }{\partial x_{i}}\sigma _{\mu ^{\prime }}^{\mathscr{U}}}{\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{1}({\Omega })}},\sigma _{\mu }^{a}\right \rangle _{L_{2}({\Omega })}\) for which \(\text {supp}\sigma ^{\mathscr{U}}_{\mu ^{\prime }}\) is contained in ω ∩Ω for some \(\omega \in \mathcal {O}_{\Omega }\) with |ω| = |μ| (the ‘regular’ entries), and where Bs contains the remaining (‘singular’) entries.
Thanks to (s4), for the regular entries we can estimate
where we used that \(\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{L_{2}({\Omega })} \lesssim 2^{-|\mu ^{\prime }|} \|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{1}({\Omega })}\) (4.9). An application of Lemma A.1 with ξ = n and ρ = 2 shows that \(\||B^{r}|\|\lesssim 4^{-k}\).
For the remaining singular entries we estimate
An application of Lemma A.1 with ξ = n − 1 and ρ = 1/2 shows that \(\||B^{s}|\|\lesssim 2^{-k/2}\). □
Lemma A.6
For \(k \in \mathbb {N}_{0}\) , it holds that
Proof
We split the matrix into Br + Bs, where Br contains all (‘regular’) entries \(\left \langle \frac {(\theta _{\lambda ^{\prime }}^{\mathscr{U}})^{\prime }}{\|(\theta _{\lambda ^{\prime }}^{\mathscr{U}})^{\prime }\|_{L_{2}(\mathrm {I})}},\theta _{\lambda }^{\mathscr{V}_{1}}\right \rangle _{L_{2}(\mathrm {I})}\) for which \(\text {supp} \theta ^{\mathscr{U}}_{\lambda ^{\prime }}\) is contained in ω ∩ I for some \(\omega \in \mathcal {O}_{\mathrm {I}}\) with |ω| = |λ| (so that in particular \(\theta ^{\mathscr{U}}_{\lambda ^{\prime }}\) vanishes on ∂I), and where Bs contains the remaining (‘singular’) entries.
For the regular entries, we can estimate
where we used (t4), Poincaré’s inequality, an inverse inequality, and \(\|\theta _{\lambda }^{\mathscr{V}_{1}}\|_{L_{2}(\mathrm {I})} \eqsim 1\). An application of Lemma A.1 with ξ = 1 and ρ = 2 shows that \(\||B^{r}|\|\lesssim 4^{-k}\).
For the remaining singular entries, we estimate
An application of Lemma A.1 with ξ = 0 and \(\rho =\frac {1}{2}\) shows that \(\||B^{s}|\|\lesssim 2^{-k/2}\). □
The following Corollary will be used to bound \(\|(\textbf {r}_{1}-{\tilde {\textbf {r}}}_{1})|_{\vee _{\mathscr{U}}}\|\).
Corollary A.7
Let \({\Lambda }^{\mathscr{V}_{1}} \subset \vee _{\mathscr{V}_{1}}\) be a multi-tree. Then for \(k \in \mathbb {N}_{0}\) ,
Proof
(a). From \(\|\theta ^{\mathscr{U}}_{\lambda } \otimes \sigma ^{\mathscr{U}}_{\mu }\|_{\mathscr{U}} \geq \|\theta ^{\mathscr{U}}_{\lambda }\|_{H^{1}(\mathrm {I})} \|\sigma ^{\mathscr{U}}_{\mu }\|_{H^{-1}({\Omega })}\), for the first inequality it is sufficient to prove that
From \({\Theta }^{\mathscr{U}} / \|{\Theta }^{\mathscr{U}}\|_{H^{1}(\mathrm {I})}\), \({\Theta }^{\mathscr{V}_{1}}\), \({\Sigma }^{\mathscr{U}} / \|{\Sigma }^{\mathscr{U}}\|_{H^{-1}({\Omega })}\), and \({\Sigma }^{\mathscr{V}_{1}}\) being Riesz bases for H1(I), L2(I), H− 1(Ω), and \({H^{1}_{0}}({\Omega })\), we have
The proof of the first inequality is completed by applications of Proposition A.4 and Lemmata A.5(first statement)–A.6.
(b). From \(\text {span} \frac {\partial }{\partial x_{i}} b_{i} {\Psi }^{\mathscr{V}_{1}}|_{{\Lambda }^{\mathscr{V}_{1}}} \subset \text {span} {\Psi }^{a}|_{\vee _{a}({\Lambda }^{\mathscr{V}_{1}},0)}\) (similar to Lemma 4.16), for \(\textbf {c} \in \ell _{2}({\Lambda }^{\mathscr{V}_{1}})\) there exists a \(\textbf {d} \in \ell _{2}(\vee _{a}({\Lambda }^{\mathscr{V}_{1}},0))\) such that
where
From \(\|\theta ^{\mathscr{U}}_{\lambda } \otimes \sigma ^{\mathscr{U}}_{\mu }\|_{\mathscr{U}} \geq \|\theta ^{\mathscr{U}}_{\lambda }\|_{L_{2}(\mathrm {I})} \|\sigma ^{\mathscr{U}}_{\mu }\|_{H^{1}({\Omega })}\), \(\vee _{\mathscr{U}}({\Lambda }^{\mathscr{V}_{1}},k)=\vee _{\mathscr{U}}(\vee _{a}\)\(({\Lambda }^{\mathscr{V}_{1}},0),k)\) it remains to be proven that
Indeed, this gives
showing the second inequality.
From \({\Theta }^{\mathscr{U}} / \|{\Theta }^{\mathscr{U}}\|_{L_{2}(\mathrm {I})}\), Θa, \({\Sigma }^{\mathscr{U}} / \|{\Sigma }^{\mathscr{U}}\|_{H^{1}({\Omega })}\), and Σa being Riesz bases for L2(I), L2(I), \({H_{0}^{1}}({\Omega })\), and L2(Ω), we have
The proof of the remaining inequality is completed by applications of Proposition A.4, Lemma A.2 for \(({\Theta }^{*},{\Theta }^{\circ })=(\frac {{\Theta }^{\mathscr{U}}}{\|{\Theta }^{\mathscr{U}}\|_{L_{2}(\mathrm {I})}},{\Theta }^{a})\), and the second statement from Lemma A.5.
(c). A subset of the arguments that showed the second inequality gives the third one. □
Lemma A.8
For\(k \in \mathbb {N}_{0}\)and 1 ≤ i ≤ n, it holdsthat
Proof
We split the matrix into Br + Bs, where Br contains all its entries \(\left \langle \sigma _{\mu ^{\prime }}^{\mathscr{P}},\right .\!\!\)\(\left .\partial _{i} \sigma _{\mu }^{\mathscr{V}_{1}}\right \rangle _{L_{2}({\Omega })^{n}}\) for which \(\text {supp} \sigma ^{\mathscr{P}}_{\mu ^{\prime }}\) is contained in ω ∩Ω for some \(\omega \in \mathcal {O}_{\Omega }\) with |ω| = |μ| (the ‘regular’ entries), and where Bs contains the remaining (‘singular’) entries.
For the regular entries using (s4) we can estimate
An application of Lemma A.1 with ξ = n and ρ = 1 shows that \(\||B^{r}|\|\lesssim 2^{-k}\).
Since the wavelets \(\sigma _{\mu }^{\mathscr{V}_{1}}\) are piecewise polynomial, and functions in H1(Ω), they are contained in \(W_{\infty }^{1}({\Omega })\). For the remaining singular entries we estimate
An application of Lemma A.1 with ξ = n − 1 and ρ = 1/2 shows that \(\||B^{s}|\|\lesssim 2^{-k/2}\). □
The following Corollary will be used to bound \(\|(\textbf {r}_{1}-{\tilde {\textbf {r}}}_{1})|_{\vee _{\vec {\mathscr{P}}}}\|\).
Corollary A.9
Let \({\Lambda }^{\mathscr{V}_{1}} \subset \vee _{\mathscr{V}_{1}}\) be a multi-tree. Then for \(k \in \mathbb {N}_{0}\) ,
Proof
Using Lemma A.2 for \(({\Theta }^{*},{\Theta }^{\circ })=({\Theta }^{\mathscr{P}},{\Theta }^{\mathscr{V}_{1}})\), Lemma A.8, \(\langle {\Theta }^{\mathscr{P}}, {\Theta }^{\mathscr{V}_{1}}\rangle _{L_{2}(\mathrm {I})} \in \mathcal {L}(\lhd _{\mathscr{V}_{1}},\lhd _{\mathscr{P}})\), and \(\langle {\Sigma }^{\mathscr{P}}, \partial _{i} {\Sigma }^{\mathscr{V}_{1}}\rangle _{L_{2}({\Omega })^{n}} \in \mathcal {L}(\Diamond _{\mathscr{V}_{1}},\Diamond _{\mathscr{P}})\), the proof follows from Proposition A.4. □
Lemma A.10
For\(k \in \mathbb {N}_{0}\),1 ≤ i ≤ n,it holds that
Proof
For proving the first inequality, we split the matrix into Br + Bs, where Br contains all its entries \(\left \langle \frac {\nabla \sigma _{\mu ^{\prime }}^{\mathscr{U}}}{\|\sigma _{\mu ^{\prime }}^{\mathscr{U}}\|_{H^{1}({\Omega })}}, \sigma _{\mu }^{a} \textbf {e}_{i} \right \rangle _{L_{2}({\Omega })^{n}}\) for which \(\text {supp} \sigma ^{\mathscr{U}}_{\mu ^{\prime }}\) is contained in ω ∩Ω for some \(\omega \in \mathcal {O}_{\Omega }\) with |ω| = |μ| (the ‘regular’ entries), and where Bs contains the remaining (‘singular’) entries.
For the regular entries, using (s4) and the first inequality in (4.9), we estimate
An application of Lemma A.1 with ξ = n and ρ = 2 shows that \(\|B^{r}\|\lesssim 4^{-k}\).
For the singular entries, we estimate
An application of Lemma A.1 with ξ = n − 1 and ρ = 1/2 shows that \(\||B^{s}|\|\lesssim 2^{-k/2}\).
The proof of the second inequality proceeds along the by now well-known steps. Using assumption (s4) on \({\Sigma }^{\mathscr{P}}\) one shows that \(\||B^{r}|\|\lesssim 2^{-k}\), whereas \(\||B^{s}|\|\lesssim 2^{-k/2}\). □
The following Corollary will be used to bound \(\|\textbf {r}_{3}-{\tilde {\textbf {r}}}_{3}\|\).
Corollary A.11
Let Λa ⊂∨abe a multi-tree. Thenfor\(k \in \mathbb {N}_{0}\),
Proof
From \(\|\theta ^{\mathscr{U}}_{\lambda } \otimes \sigma ^{\mathscr{U}}_{\mu }\|_{\mathscr{U}} \geq \|\theta ^{\mathscr{U}}_{\lambda }\|_{L_{2}(\mathrm {I})} \|\sigma ^{\mathscr{U}}_{\mu }\|_{H^{1}({\Omega })}\), in order to prove the first result it suffices to show that
This and the second result follow from applications of Proposition A.4, Lemma A.2, and Lemma A.10. □
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Rekatsinas, N., Stevenson, R. An optimal adaptive tensor product wavelet solver of a space-time FOSLS formulation of parabolic evolution problems. Adv Comput Math 45, 1031–1066 (2019). https://doi.org/10.1007/s10444-018-9644-2
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DOI: https://doi.org/10.1007/s10444-018-9644-2
Keywords
- Parabolic PDEs
- Space-time variational formulation
- First order system least squares
- Adaptive wavelet solver
- Optimal rates
- Linear complexity