Spectral coarse spaces based on indefinite operators: the \(H_k\)-GenEO method


1 Introduction↩︎

This paper is concerned with the theory and implementation of two-level domain decomposition preconditioned iterative methods for solving finite element approximations of the indefinite boundary value problem: \[\tag{1} \begin{alignat}{2} -\nabla \cdot (A \nabla u) - k^2 n u &= f \qquad && \text{in } \Omega, \tag{2} \\ u &= 0 \qquad && \text{on } \partial\Omega, \tag{3} \end{alignat}\] where \(k>0\), \(A\) is an almost everywhere positive definite matrix with \(L^\infty\) entries and \(n\) is an almost everywhere positive \(L^\infty\) function. The domain \(\Omega \subseteq \mathbb{R}^{d}\) (\(d=2,3\)) is assumed to be a Lipschitz polygon (\(d=2\)) or polyhedron (\(d=3\)). We emphasise the highly indefinite case—when \(k\) is large—since this presents particular challenges for iterative solvers. While 2 is instantly recognisable as a (heterogeneous) Helmholtz equation, the appearance of the Dirichlet boundary condition 3 means that 1 does not directly model scattering problems (where 2 would normally be posed on an infinite domain, accompanied by a radiation condition instead of 3 ). However, 1 does appear in other physical models of wave phenomena (e.g.a vibrating membrane constrained at its boundary) and its solution typically oscillates with decreasing period as \(k \rightarrow \infty\). Thus 1 provides us with a relevant highly indefinite model problem with variable coefficients, and we use it here to assess the performance of a new class of iterative solvers for such problems. We will assume throughout that the ‘wavenumber’ \(k\) is such that the problem 2 , 3 is well posed, in the sense that it has a unique weak solution for all \(f \in L^2(\Omega)\).

The problem 1 is discretised using conforming finite elements to obtain a linear system (see 12 ). When this system is very large (especially in 3D), it should be solved iteratively using a good preconditioner (i.e.a cheap approximate inverse). Here we propose a novel domain decomposition preconditioner and analyse its performance when used with GMRES as the iterative solver. In classical one-level overlapping domain decomposition methods, the global domain \(\Omega\) is covered by a set of overlapping ‘local’ subdomains \(\Omega_j^\ell\), \(j=1,\ldots,Q\), and the one-level Schwarz preconditioner is built from partial solutions of the global problem on each subdomain. Since this is, in general, not scalable as the number of subdomains grows, an additional global coarse solve is usually added to enhance scalability, as well as robustness with respect to coefficient heterogeneity or increasing \(k\).

Spectral coarse spaces. Since classical coarse spaces (piecewise polynomials on a coarse grid), are generally not robust for highly heterogeneous problems, various operator-dependent coarse spaces have been introduced to better treat the heterogeneity, e.g.’spectral’ coarse spaces, built from well-chosen modes of suitable generalised PDE eigenvalue problems defined on subdomains. Such spectral information was used for both robust solution approximation (e.g. [1], [2]) and for preconditioning in [3], [4]. However, all this work was for positive definite (albeit highly heterogeneous) PDEs.

In this paper we propose and analyse a novel variant of the GenEO spectral coarse space. GenEO coarse spaces were first introduced in [5], where it was proved that, for self-adjoint positive definite problems, when combined with a one-level method, and using a preconditioned conjugate gradient solver, the resulting algorithm is not only scalable with respect to the number of subdomains but also robust with respect to coefficient variation. Related works for positive definite problems include, for example [6][9]; see also [5] and [10]. Here we construct coarse spaces for 1 from eigenproblems defined on each subdomain of an overlapping ‘coarse’ cover: \(\Omega_i^c\), \(i=1,\ldots,N\). This need not be related to the ‘local’ cover \(\Omega_j^\ell\) introduced above; indeed the \(\Omega_i^c\) can be relatively large. To explain ideas simply, now let \(\xi_i^c\), \(i=1,\ldots,N\) be a partition of unity subordinate to the coarse cover. The original GenEO method was aimed at problem 1 with \(k = 0\), and built a coarse space from selected modes of the generalised eigenvalue problem (GEVP): \[\label{new1} -\nabla \cdot (A \nabla p) = \lambda \bigl[-\xi_i \nabla \cdot (A \nabla (\xi_i p))\bigr] \qquad \text{on} \;\; \Omega_i^c,\tag{4}\] (computed using standard variational finite element approximation). The eigenmodes of 4 provide a powerful coarse space for 1 when \(A\) is highly heterogeneous and \(k = 0\) [5]. However, they are less suitable when \(k\) is very large, as demonstrated numerically in [11], [12] where the coarse space based on 4 (and called \(\Delta\)-GenEO) was compared to a coarse space (called \(H\)-GenEO), based on eigenmodes of the GEVP: \[\label{new2} -\nabla \cdot (A \nabla p) - k^2 n p = \lambda \bigl[-\xi_i \nabla \cdot (A \nabla (\xi_i p))\bigr] \qquad \text{on} \;\; \Omega_i^c.\tag{5}\] The indefinite operator from 2 now appears on the left-hand side of 5 , ensuring that some eigenmodes of this problem will be oscillatory (provided the \(\Omega_i^c\) are large enough). In [11], [12] \(H\)-GenEO was found to have better robustness properties than \(\Delta\)-GenEO at high frequency but no theory was provided. In the current paper we propose and analyse the ‘\(H_k\)-GenEO’ coarse space based on the following variant of 5 : \[\begin{align} \label{new3} -\nabla \cdot (A \nabla p) - k^2 n p = \lambda \bigl[-\xi_i \nabla \cdot (A \nabla (\xi_i p)) + k^2 \xi_i n (\xi_i p) \bigr] \qquad \text{on} \;\; \Omega_i^c. \end{align}\tag{6}\] The same indefinite operator appears on the left-hand side, while the right-hand side has the extra positive term making it close to the norm in which we do the analysis of this paper. Our actual analysis is for the finite element approximation of this—where the multiplication \(\xi_i p\) is replaced by an algebraically defined operator \(\Xi_i(p)\); see ?? .

Main results. Our main results are given in Theorem 10 and Corollary 2 and we give a simplified summary of these here. Let \(H_\ell\) (respectively \(H_c\)) denote the maximum diameter of the ‘local’ subdomains \(\Omega_j^\ell\) (respectively ‘coarse’ subdomains \(\Omega_i^c\)). On each \(\Omega_i^c\) let \(\{\lambda_m^i: m = 1,2,\ldots\}\) denote the eigenvalues of the numerical approximation of 6 (real and ordered in non-decreasing order). Then, if only the first \(m_i\) eigenfunctions on each \(\Omega_i^c\) are used in the coarse space, and \(\lambda_{m_i +1 }^i > 0\), we define \[\tau \mathrel{\mathop{:}}=\min_{i=1}^N \lambda^i_{m_i+1} \label{def:tau}\tag{7}\] to be the eigenvalue tolerance. Let \(C_{\mathrm{\textsf{stab}}}> 0\) denote the stability constant arising from the assumption that 1 has a unique weak solution in \(H^1_0(\Omega)\) for each \(f \in L^2(\Omega)\) (see Assumption 2). Without loss of generality, we assume that, on \(\Omega\), the eigenvalues of the coefficient matrix \(A\) are all \(\ge 1\), and all values of \(n\) are \(\leq 1\) (Assumption 1). Then, when the 2-level additive Schwarz preconditioned system is solved by GMRES, our theory proves its robust convergence under certain conditions. For this illustration, suppose the finite element mesh is quasiuniform with mesh diameter satisfying \(h \sim k^{-1}\). Then, \(k\)-robustness holds provided \[\label{Hk95results} H_\ell \lesssim k^{-1} \qquad \text{and} \qquad \tau \gtrsim (1+C_{\mathrm{\textsf{stab}}})^{2}\, k^{2} ,\tag{8}\] where the hidden constants depend on the number of times any subdomain is overlapped by others. The rate of GMRES convergence depends modestly on \(a_{\mathrm{\textsf{max}}}\) (the maximum eigenvalue of \(A\)) and \(n_{\mathrm{\textsf{min}}}\) (the minimum value of \(n\)), although this dependence is minimal in practice. Our results extend to more general meshes, with stricter conditions for robustness.

The first condition in 8 arises because \(H_\ell\) is required to be small enough to guarantee the solvability of the local problems. Importantly, our theory imposes no explicit upper constraint on the coarse diameter \(H_c\), and we see in the numerical results that it is beneficial to have the coarse subdomains large enough, making the GEVP 6 indefinite, and providing oscillatory modes. The separation of local and coarse subdomain sizes which we employ here contrasts with some earlier work, which assumed a single subdomain cover for both local and coarse decomposition.

Our analysis of 6 given here is a substantial generalisation of the theory in [13], which studied the application of \(\Delta\)-GenEO coarse spaces (using 4 , but applied to systems arising from general non-self-adjoint and indefinite PDEs of convection-diffusion-reaction type). In that context only the much more stringent estimate \(\tau \gtrsim (1+C_{\mathrm{\textsf{stab}}})^2\,k^{8}\) on the eigenvalue tolerance could be proved, although the \(k^8\) factor was later reduced in [14]. Nevertheless, the eigenvalue tolerance estimate in 8 still turns out to be rather pessimistic in practice. Our numerical results (in 2D) seem to indicate that choosing a large enough positive (but constant) value for \(\tau\) yields \(k\)-robust convergence of GMRES with very modest coarse space dimensions as \(k\) increases. Remarks on this are given in §5.

To emphasise the difference between this paper and what went before, we remark that a substantial task here concerns the study of the indefinite GEVP 6 and the corresponding projection onto the eigenspace spanned by all negative (and some positive) eigenvalues. In this respect our analysis is substantially more difficult and different from earlier studies using the positive definite problem 4 . Since this spectral theory may be interesting in its own right, it is written in an abstract form in Section 2.2. Our proof of 8 uses the ‘Elman theory’ for GMRES ([15] or [16]), and proceeds by proving an upper bound for the norm of the preconditioned matrix and a lower bound on the distance of its field of values from the origin. We estimate these quantities, explicitly analysing their dependence on, and independence of, the key parameters of the problem.

Other relevant work. The first publications on spectral coarse spaces for Helmholtz problems with truncated far-field Sommerfeld radiation condition include methods based on Dirichlet-to-Neumann maps [17], [18], while a numerical survey is given in [11] and then enriched in [19]. In [20] approximation spaces for Helmholtz problems are constructed by solving local positive semi-definite GEVPs (similar to 4 ) in subspaces of local Helmholtz harmonic functions. The resulting multiscale approximation space is shown to be quasi-optimal (i.e.the Galerkin error is bounded by the best approximation in this space times a constant independent of the parameters—in this case the coefficients of the PDE and \(k\)). Subsequently [21] showed that the corresponding classical additive Schwarz preconditioner yields a Richardson iteration which is a parameter-independent contraction. However, the subdomains in [21] are constrained to be of diameter \(\mathcal{O}(k^{-1})\) and therefore the local Helmholtz problems are (close to) coercive. A similar coarse space was proposed in [22], where Helmholtz problems with constant coefficients are analysed. Most of the theory in [22] is concerned with the absorptive case where \(k\) is replaced by \(k + \mathrm{i} \varepsilon\) with \(\varepsilon>0\). In the theory for \(\varepsilon\) small the subdomain diameter is constrained to be \(o(k^{-1})\) as \(k \rightarrow \infty\) meaning the local problems are again coercive.

Related results on the extension of multiscale approximation methods (originally devised for elliptic problems with oscillatory coefficients), to the Helmholtz case are in [23]. The corresponding LOD (localised orthogonal decomposition) is used to construct a coarse space in [24], where corresponding theory (for Helmholtz problems with constant coefficients) is presented. Related methods are discussed in [25]. However, these multiscale approaches are quite different from the spectral methods studied in the present paper.

Additional recent work [26] has shown that classical additive Schwarz preconditioners using coarse spaces based on piecewise polynomials are robust for Helmholtz problems as \(k \rightarrow \infty\), provided the approximations on the fine and coarse levels are quasi-optimal (independent of \(k\)). Such a prescription can allow the coarse space dimension to be much smaller than the fine space dimension for high wavenumber problems, although both dimensions can be quite high. Methods based on fixed degree polynomials are considered in [27] while \(hp\) versions where \(p\) increases logarithmically with \(k\) are included in [26]. However, spectral information is not used to construct these coarse spaces and dependence on coefficient variation is not analysed.

Plan of the paper. In §2 the problem is described and a theory for finite element approximation of 1 with elements of any order is given. Then the spectral theory for indefinite problems is presented in abstract followed by the domain decomposition set-up and then the \(H_k\)-GenEO coarse space is defined. In §3 the main result is stated and the properties of the \(H_k\)-GenEO coarse space are analysed (applying the abstract theory from the previous section, and employing a number of delicate estimates). The main theorem is the proved in §4, and numerical results given in the final section.

2 Background and abstract theoretical results↩︎

Here we introduce the variational formulation of problem 2 , 3 , and its discretisation. Then, we present an abstract finite-dimensional theory for variational generalised eigenvalue problems with indefinite forms. Subsequently, we define our additive Schwarz domain decomposition preconditioner, with the novel feature being the \(H_k\)-GenEO coarse space.

2.1 Problem formulation and finite element discretisation↩︎

The weak formulation of 1 is to find \(u \in H^1_0(\Omega)\) such that \[\label{weak95form} b(u,v) = (f,v) \qquad \text{for all} \;v \in H^1_0(\Omega),\tag{9}\] where \(f \in L^2(\Omega)\), \((\cdot, \cdot)\) is the \(L^2(\Omega)\) inner product and \(b : H^1_0(\Omega) \times H^1_0(\Omega) \rightarrow \mathbb{R}\) is defined as \[b(u,v) = \int_\Omega (A\nabla u \cdot \nabla v - k^2{n} uv)\,\mathrm{d}x.\] The following weak regularity assumptions are used throughout this work.

Assumption 1. The coefficients \(A\) and \(n\) and the domain \(\Omega\) in problem 1 satisfy the following:

()

\(A: \Omega \rightarrow \mathbb{R}^{d \times d}\) and \(n: \Omega \rightarrow \mathbb{R}\) are measurable functions with \(A\) a symmetric tensor field, such that, for some \(0 < a_{\mathrm{\textsf{min}}}\le a_{\mathrm{\textsf{max}}}\) and \(0 < n_{\mathrm{\textsf{min}}}\le n_{\mathrm{\textsf{max}}}\), \[\begin{align} {2} a_{\mathrm{\textsf{min}}}|\boldsymbol{\xi}|^2 &\le A(x)\boldsymbol{\xi} \cdot {\boldsymbol{\xi}} \le a_{\mathrm{\textsf{max}}}|\boldsymbol{\xi}|^2 \qquad && \text{for a.e.} \;x \in \Omega \text{ and all } \boldsymbol{\xi} \in \mathbb{R}^d, \\ \text{and} \qquad n_{\mathrm{\textsf{min}}}&\le n(x) \le n_{\mathrm{\textsf{max}}}\qquad && \text{for a.e.} \;x \in \Omega. \end{align}\]

Since 2 can be equivalently rewritten as \[- \nabla \cdot \biggl(\frac{A}{a_{\mathrm{\textsf{min}}}}\biggr) \nabla u - \biggl(\frac{k^2 n_{\mathrm{\textsf{max}}}}{a_{\mathrm{\textsf{min}}}}\biggr) \biggl(\frac{n}{n_{\mathrm{\textsf{max}}}}\biggr) u = \frac{f}{a_{\mathrm{\textsf{min}}}},\] we shall assume, without loss of generality, that \(a_{\mathrm{\textsf{min}}}= 1 = n_{\mathrm{\textsf{max}}}\) and that the diameter, \(D_\Omega\), of the domain \(\Omega\) satisfies \(D_\Omega \le 1\).

We assume that \(k\ge k_0\) for some given \(k_0 > 0\).

Notation 1. For any subdomain \(\Omega' \subseteq \Omega\) we use \((\cdot,\cdot)_{\Omega'}\) to denote the \(L^2(\Omega')\) inner product, \((\cdot,\cdot)_{n,\Omega'}\mathrel{\mathop{:}}=(n\cdot,\cdot)_{\Omega'}\) to denote the weighted \(L^2(\Omega')\) inner product with weight \(n\), and \(\|\cdot\|_{\Omega'}\) and \(\|\cdot\|_{n,\Omega'}\) to denote the corresponding norms. We also introduce the bilinear forms: \[a_{\Omega'}(u,v) \mathrel{\mathop{:}}=\int_{\Omega'} A\nabla u \cdot \nabla v \,\mathrm{d}x, \qquad b_{\Omega'}(u,v) \mathrel{\mathop{:}}=a_{\Omega'}(u,v) - k^2(u,v)_{{n},\Omega'} \qquad\text{for} \;u,v\in H^1(\Omega'), \label{eq:24618}\qquad{(1)}\] and define the semi-norm induced by \(a\), \(|u|_{a,\Omega'} \mathrel{\mathop{:}}=\sqrt{a_{\Omega'}(u,u)}\). We also use the \(k\)-weighted and \(A\)- and \(n\)-dependent inner product: \[(u,v)_{1, k,\Omega'} \mathrel{\mathop{:}}=a_{\Omega'}(u,v) + k^2 (u,v)_{{n},\Omega'},\] and we denote the induced \(k\)-norm by \(\|u\|_{1, k, \Omega'}\). When \(\Omega'= \Omega\), we abandon the subscript \(\Omega\) in all the notation.

The bilinear forms \(a_{\Omega'}(\cdot,\cdot )\) and \((\cdot,\cdot)_{1,k, \Omega'}\) are symmetric positive definite on \(H_0^1(\Omega')\) and \(H^1(\Omega')\) respectively; \(b(\cdot,\cdot)\) is symmetric, but in general indefinite. However, using the Cauchy–Schwarz inequality twice one can easily show that: \[\begin{align} |b_{\Omega'}(u,v)| \le \|u\|_{1,k,\Omega'}\|v\|_{1,k,\Omega'}. \label{lem:est95b} \end{align}\tag{10}\] We make the following assumption on solvability of 9 .

Assumption 2. For any \(f\in L^2(\Omega)\), problem 9 has a unique solution \(u \in H_0^1(\Omega)\) and there exists a constant \(C_{\mathrm{\textsf{stab}}}>0\) such that\[\label{eq:3229510} \|u\|_{1,k} \le C_{\mathrm{\textsf{stab}}}\|f\| \qquad \text{for all} \;f\in L^2(\Omega).\qquad{(2)}\]

Note that \(C_{\mathrm{\textsf{stab}}}\) exists if \(k^2\) is not a Dirichlet eigenvalue of the operator \(-\frac{1}{n}\mathop{\mathrm{div}}(A\nabla\cdot)\).

Assumption 3. Throughout, \(\mathcal{T}_h\) will denote a family of simplicial meshes on \(\Omega\), parameterised by maximum diameter \(h\). We assume this family is shape regular, i.e.there exists a constant \(C_{\mathrm{\textsf{SR}}}\) independent of all parameters such that \(\rho_T \ge C_{\mathrm{\textsf{SR}}}h_T\) for each \(T \in \mathcal{T}_h\), where \(h_T\) denotes the diameter of \(T\) and \(\rho_T\) denotes the diameter of the largest inscribed sphere in \(T\). In the analysis (see Lemma 5) we will require that \(h \leq \sqrt{C_1} k^{-1}\) for some constant \(C_1\) independent of \(k\). We also assume inverse estimates: \[\begin{align} \label{inv95est} \|\nabla v \|_{\tau} \leq C_{\mathrm{\textsf{inv}}}k \|v\|_{\tau} \qquad \text{for all} \;\; \tau \in \mathcal{T}_h, \;\; v \in V^h, \end{align}\qquad{(3)}\] for some constant \(C_{\mathrm{\textsf{inv}}}\ge1\).

Remark 2. A sufficient condition to ensure ?? with \(C_{\mathrm{\textsf{inv}}}\) independent of \(k\) is obtained by requiring \(h_\tau k\) to be bounded below with respect to \(k\) for all \(\tau\), so that \(h_\tau \sim 1/k\) (e.g.a quasiuniform mesh with all elements refined of order \(1/k\)). Our strongest results are when \(C_{\mathrm{\textsf{inv}}}\) is independent of \(k\). However, \(C_{\mathrm{\textsf{inv}}}\) could still be quite large, allowing local mesh refinement treating corners or edges, for example. In our estimates below, we will treat \(C_{\mathrm{\textsf{inv}}}\) explicitly, while \(C_{\mathrm{\textsf{SR}}}\) will remain implicit.

Let \(V^h \subseteq H^1_0(\Omega)\) be the \(H^1\)-conforming Lagrange finite element space of polynomials of degree \(r\ge 1\) with respect to this mesh, with nodal basis functions denoted by \(\{\phi_j: j = 1, \ldots, \dim(V^h)\}\). The indices \(j\) are also known as degrees of freedom. The Galerkin approximation of 9 is: \[\label{eq:discrete95problem} \text{find } u_h \in V^h \text{ such that }\qquad b(u_h,v_h) = (f,v_h) \qquad \text{for all} \;v_h \in V^h.\tag{11}\] This is equivalent to the linear system \[\label{eq:3229512} \mathbf{B}\mathbf{u} = \mathbf{f},\tag{12}\] where \((\mathbf{B})_{ij} \mathrel{\mathop{:}}=b(\phi_j, \phi_i)\) and \((\mathbf{f})_i \mathrel{\mathop{:}}=(f, \phi_i)\). Later we also need the matrices \(\mathbf{A}\) and \(\mathbf{S}\) that correspond to \(a\) and \(({n} \cdot,\cdot)\), namely, \((\mathbf{A})_{ij} \mathrel{\mathop{:}}=a(\phi_j, \phi_i)\) and \((\mathbf{S})_{ij} \mathrel{\mathop{:}}=({n} \phi_j, \phi_i)\) respectively.

The solvability of 11 for sufficiently fine meshes and/or high enough polynomial degree is assured by the following lemma, proved for \(r=1\) and \(h \rightarrow 0\) in [28]. Since we could not find this result for general \(r\ge 1\) in the literature, we give a proof here.

Lemma 1 (Solvability for higher polynomial degrees). Let Assumptions 1 and 2 hold, and set \[\label{eq:definition95gamma} \beta \mathrel{\mathop{:}}= k\max_{\substack{{\psi} \in L^2(\Omega) \\[0.2ex] {\|\psi\|_n = 1}}}\; \min_{v_h \in V^h} \|{z_\psi}-v_h\|_{1,k},\qquad{(4)}\] where \(z_\psi\) is the unique element of \(H^1_0(\Omega)\) such that \(b(w,{z_\psi}) = (w,\psi)_{{n}}\) for all \(w \in H^1_0(\Omega)\). Then, there exists a continuous function \(\theta: (0,\infty)\to(0,\infty)\), solely depending on \(\Omega\), \(A\), \(n\) and \(k\), with \(\lim_{\tau \to 0} \theta(\tau) = 0\) and such that \[\label{eq:estimate95gamma} \beta \le C \, \theta\Bigl(\frac{h}{r}\Bigr),\qquad{(5)}\] where \(C>0\) is a constant which can depend on the shape-regularity constant \(C_{\mathrm{\textsf{SR}}}\). In addition, if \(\beta < 1/\sqrt{2}\), then the linear system in 11 uniquely determines \(u_h\), and we have, for all \(f \in L^2(\Omega)\), \[\label{eq:quasi95optimality} \|u-u_h\|_{1,k} \le \frac{1}{{1-2\beta^2}} \min_{v_h \in V^h} \|u-v_h\|_{1,k} \le \biggl(\frac{\beta}{{1-2\beta^2}}\biggr)\frac{1}{k} \|f\|_{{n^{-1}}}.\qquad{(6)}\]

The uniqueness of \(u_h\) and the estimates in ?? for \(\beta < \sqrt{2}/2\) constitute a standard result typically referred to as the ‘Schatz argument’ [28], [29]. For completeness, we review the proof of ?? here before proving ?? .

Proof.   Let us first note that, since \(b\) is symmetric, the definition of \(\beta\) actually makes sense due to Assumption 2. Note also that a priori we only have a supremum instead of a maximum in ?? ; we will later justify that it is a maximum. It follows from the definition of \(\beta\) that, for each \(\psi\in L^2(\Omega){\backslash \{0\}}\), \[k \min_{v_h \in V^h} \|{z_\psi}-v_h\|_{1,k} = k \min_{v_h \in V^h} \|(z_\psi/\|\psi\|_n) - v_h\|_{1,k} \|\psi\|_n = k \min_{v_h \in V^h} \|(z_{(\psi/\|\psi\|_n)} - v_h\|_{1,k} \|\psi\|_n \le \beta\|\psi\|_n.\] Hence, there exists \(v_h\in V^h\) such that \[\label{def95beta95reformulated} k\|{z_\psi} - v_h\|_{1,k} \le \beta\|\psi\|_n.\tag{13}\] Now let \(f\in L^2(\Omega)\) and let \(u\in H_0^1(\Omega)\) be the unique solution of 9 . Let us first assume that \(u_h \in V^h\) is any solution of 11 . By Assumption 2, there exists a unique \(\xi {= z_{k(u - u_h)}}\in H^1_0(\Omega)\) such that \[\label{existence95xi} b(w,\xi) = k(w,u-u_h)_{{n}} \qquad \text{for all} \;\; w \in H^1_0(\Omega).\tag{14}\] On one hand, (by 13 with \(\psi=k(u-u_h)\)), there exists \(\xi_h \in V^h\) such that \[\|\xi-\xi_h\|_{1,k} \le \beta \|u-u_h\|_{{n}}.\] On the other hand, taking the test function \(w = u-u_h\) in 14 we see that \[k\|u-u_h\|_{{n}}^2 = b(u-u_h,\xi) = b(u-u_h,\xi-\xi_h) \le \|u-u_h\|_{1,k}\|\xi-\xi_h\|_{1,k},\] where we used 10 and the fact that \(b(u-u_h,w_h)=0\) for \(w_h\in V^h\) since \(u\) solves 9 and \(u_h\) solves 11 . Combining these two estimates we obtain \[k\|u-u_h\|_{{n}} \le \beta\|u-u_h\|_{1,k},\] and hence \[\begin{align} (1-2\beta^2)\|u-u_h\|_{1,k}^2 &\le \|u-u_h\|_{1,k}^2-2k^2\|u-u_h\|_{{n}}^2 = b(u-u_h,u-u_h) \\[1ex] &= b(u-u_h,u-v_h) \le \|u-u_h\|_{1,k}\|u-v_h\|_{1,k} \end{align}\] for all \(v_h \in V^h\). Assuming that \(2\beta^2 < 1\) we obtain the first estimate in ?? . The second estimate follows from the definition of \(\beta\); see again 13 .

At that point, we have established ?? for any solution, but we do not know yet if such a solution exists. However, \(u_h\) is defined through a finite-dimensional linear system. In this setting, stability implies uniqueness, which in turns implies existence. This concludes the proof of ?? .

We now turn our attention to ?? . We first observe that, thanks to Fredholm alternative (applied in \(H^{-1}(\Omega)\)), Assumption 2 guarantees that the mapping \(S: H^{-1}(\Omega) \to H^1_0(\Omega)\) defined by \[b(S\psi,v) = \langle \psi,v \rangle\] for all \(\psi \in H^{-1}(\Omega)\) and \(v \in H^1_0(\Omega)\) is a well-defined continuous linear operator where \(\langle\cdot,\cdot\rangle\) denotes the \(H^{-1}(\Omega)\), \(H_0^1(\Omega)\)-pairing.

Let \(B_{L^2} = \{\phi \in L^2(\Omega) \; | \; \|{n^{-1}}\phi\|_{n} \le 1\}\) denote the closed unit ball in \(L^2(\Omega)\). Since the injection \(L^2(\Omega) \hookrightarrow H^{-1}(\Omega)\) is compact, we conclude that \(K \mathrel{\mathop{:}}=S(B_{L^2})\) is a compact set in \(H^1_0(\Omega)\). Note that for \(\phi \in L^2(\Omega)\) we have \(S\phi = z_{\{n^{-1} \phi\}}\) (with the notation introduced above). Hence, \[\label{beta95K} \max_{v \in K} \min_{v_h \in V^h} \|v-v_h\|_{1,k} = \max_{\substack{\phi \in L^2(\Omega) \\[0.2ex] {\|n^{-1}\phi\|_n \le 1}}}\; \min_{v_h \in V^h} \| z_{(n^{-1}\phi)}-v_h\|_{1,k} = \max_{\substack{{\psi} \in L^2(\Omega) \\[0.2ex] \|\psi\|_n \le 1}}\; \min_{v_h \in V^h} \| z_\psi-v_h\|_{1,k} = \frac{\beta}{k}\,.\tag{15}\] Let us fix \(\varepsilon > 0\). Since \(K\) is compact, there exist \(N\) functions \(\{v^j\}_{j=1}^N \subseteq K\) such that, for each \(v \in K\), there exists \(j \in \{1,\dots,N\}\) with \[k\|v-v^j\|_{1,k} \le \frac{\varepsilon}{3}.\] We then invoke the density of \(C_{\rm c}^\infty(\Omega)\) in \(H^1_0(\Omega)\), which guarantees that, for each \(j\in\{1,\ldots,N\}\), there exists \({\widetilde{v}^j} \in C_{\rm c}^\infty(\Omega)\) with \[k\|v^j-\widetilde{v}^j\|_{1,k} \le \frac{\varepsilon}{3}.\] Now we invoke a degree-explicit approximation result (e.g.Melenk and Sauter [30]) which implies that there exists a constant \(C\) (depending on \(C_{\mathrm{\textsf{SR}}}\)) such that \[\min_{v_h \in V^h} \| \widetilde{v}^j - v_h\|_{H^i(\Omega)} \le C \Bigl(\frac{h}{r}\Bigr)^{2-i}|\widetilde{v}^j|_{H^2(\Omega)} \qquad \text{for each} \;\; i = 0,1, \quad j = 1,\ldots , N.\] This estimate follows from [30] (given only on a unit element), by applying a standard scaling argument on each finite element and using the fact that the resulting global approximation operator maps continuous functions into \(V^h\); see, e.g.the steps in [30]. Hence there exist \(v^1_h,\ldots,v^N_h \in V^h\) such that \[\begin{align} k \|\widetilde{v}^j-v^j_h\|_{1,k} &\le k\Bigl(a_{\mathrm{\textsf{max}}}\|\widetilde{v}^j-v_h^j\|_{H^1(\Omega)}+k^2\|\widetilde{v}^j-v_h^j\|_{L^2(\Omega)}^2\Bigr)^{1/2} \nonumber\\[1ex] &\le C\frac{kh}{r}\biggl(a_{\mathrm{\textsf{max}}}+\Bigl(\frac{kh}{r}\Bigr)^2\biggr)^{1/2}|\widetilde{v}^j|_{H^2(\Omega)} \le C\frac{kh}{r}\biggl(a_{\mathrm{\textsf{max}}}+\Bigl(\frac{kh}{r}\Bigr)^2\biggr)^{1/2}M(\varepsilon) \label{est95interm95ml01} \end{align}\tag{16}\] for \(h/r\) small enough, where \(M(\varepsilon) \mathrel{\mathop{:}}=\max_{1 \le i \le N} |\widetilde{v}^i|_{H^2(\Omega)}.\) Putting the pieces together and using the triangle inequality we obtain \[k \min_{v_h \in V^h} \|v-v_h\|_{1,k} \le \frac{2\varepsilon}{3} + C\frac{kh}{r}\biggl(a_{\mathrm{\textsf{max}}}+\Bigl(\frac{kh}{r}\Bigr)^2\biggr)^{1/2}M(\varepsilon)\] for all \(v \in K\). Let us write \(\beta(h,r)\mathrel{\mathop{:}}=\beta\) to indicate the dependence on \(h\) and \(r\). We can assume, without loss of generality, that \(C \, \ge1\). It follows from 15 and 16 that \[\frac{\beta(h,r)}{C\,} \le \frac{2\varepsilon}{3} + C\frac{kh}{r}\biggl(a_{\mathrm{\textsf{max}}}+\Bigl(\frac{kh}{r}\Bigr)^2\biggr)^{1/2}M(\varepsilon).\] Clearly, there exists \(\delta>0\) such that \(\frac{\beta(h,r)}{C\,}\le\varepsilon\) if \(h/r\in(0,\delta)\). Now choose a decreasing sequence \(\varepsilon_\ell>0\) such that \(\lim_{\ell\to\infty}\varepsilon_\ell=0\). Then there exist \(\delta_\ell>0\) such that \((\delta_\ell)_{\ell=1}^\infty\) is strictly decreasing, \(\lim_{\ell\to\infty}\delta_\ell=0\) and \[\frac{\beta(h,r)}{C\,} \le \varepsilon_\ell \qquad\text{for} \;\; h,r>0 \quad \text{with}\;\;\; \frac{h}{r}<\delta_\ell.\] Defining \(\theta(\delta_\ell)\mathrel{\mathop{:}}=\varepsilon_\ell\) and interpolating linearly we obtain \(\theta\) satisfying ?? . ◻

Before proceeding, we recall Friedrichs’ inequality (e.g.[31]).

Lemma 2 (Friedrichs’ inequality and a consequence). Let \(\Omega' \subseteq \mathbb{R}^d\) be an open set that lies between two parallel hyperplanes, separated by a distance \(L\). Then, for all \(u \in H^1_0(\Omega')\), \[\label{eq:friedrichs} \|u \|_{\Omega'} \le \frac{L}{\sqrt{2}\,} \|\nabla u\|_{\Omega'}.\qquad{(7)}\] Combining ?? with Assumption 1 (ii) we obtain that, for any subdomain \(\Omega' \subseteq \Omega\) with diameter \(H\), the following estimate holds: \(\|u\|_{\Omega'} \le \frac{H}{\sqrt{2}\,} \|\nabla u\|_{\Omega'} \le \frac{H}{\sqrt{2}\,} \|u\|_{1,k,\Omega'}\) for all \(u \in H^1_0(\Omega')\).

2.2 Abstract spectral theory for indefinite generalised eigenvalue problems↩︎

We shall build our coarse space by solving local generalised eigenvalue problems, which may be indefinite (see ?? ). So in this section we derive, in an abstract setting, the required properties of such problems. First we collect some properties of a positive semi-definite bilinear form (such as appears on the right-hand side of ?? ).

Lemma 3. Suppose \(\widetilde{V}\) is a real vector space with \(\dim \widetilde{V}= \tilde{n}\). Let \(\mathrm{\texttt{c}}\) be a symmetric positive semi-definite bilinear form on \(\widetilde{V}\), let \(\ker \mathrm{\texttt{c}}\mathrel{\mathop{:}}=\{ v \in \widetilde{V}: \mathrm{\texttt{c}}(v,w) = 0 \;\text{for all} \;w \in \widetilde{V}\}\) and suppose that \(\dim \ker \mathrm{\texttt{c}}= s < \tilde{n}\). Further, let \(\widehat{V}\subseteq \widetilde{V}\) be an \((\tilde{n}-s)\)-dimensional subspace such that \(\widehat{V}\cap\ker\mathrm{\texttt{c}}=\{0\}\). Then \(\widetilde{V}=\widehat{V}\oplus\ker\mathrm{\texttt{c}}\), where \(\oplus\) denotes a direct sum, and \(\mathrm{\texttt{c}}\) is positive definite on \(\widehat{V}\).

Proof. The relation \(\widetilde{V}=\widehat{V}\oplus\ker\mathrm{\texttt{c}}\) is clear since \(\dim\widehat{V}+\dim\ker\mathrm{\texttt{c}}=\tilde{n}\) and \(\widehat{V}\cap\ker\mathrm{\texttt{c}}=\{0\}\). The form \(\mathrm{\texttt{c}}|_{\widehat{V}\times\widehat{V}}\) is symmetric and satisfies \(\mathrm{\texttt{c}}(v,v)>0\) for \(v\in\widehat{V}\setminus\{0\}\) by the assumption on \(\widehat{V}\). Hence \(\mathrm{\texttt{c}}|_{\widehat{V}\times\widehat{V}}\) is positive definite. ◻

Theorem 3 (Abstract theorem for indefinite eigenvalue problems). Let \(\widetilde{V}\), \(\mathrm{\texttt{c}}\) and \(s\) be as in Lemma 3 and suppose also that \(\mathrm{\texttt{b}}\) is a symmetric (possibly indefinite) bilinear form on \(\widetilde{V}\). Consider the generalised eigenvalue problem: find \(\lambda \in \mathbb{R}\) and \(p \in \widetilde{V}\backslash \{0\}\) such that \[\label{Ivan1} \mathrm{\texttt{b}}(p,v) = \lambda \mathrm{\texttt{c}}(p,v) \qquad \text{for all} \quad v \in \widetilde{V}.\qquad{(8)}\] If \(s \ge 1\), suppose also that \(\mathrm{\texttt{b}}\) is positive definite on \(\ker\mathrm{\texttt{c}}\). Then \(\widetilde{V}\) has a basis \(\{p_j: j = 1, \ldots,\tilde{n}\}\), in which \(\{p_j: j = 1, \ldots, \tilde{n}-s\}\) are eigenvectors of ?? which can be chosen to be orthonormal with respect to \(\mathrm{\texttt{c}}\) and the corresponding finite eigenvalues can be ordered \[\label{Ivan11} -\infty < \lambda_1 \leq \lambda_2 \leq \ldots \leq \lambda_{\tilde{n}-s} < \infty.\qquad{(9)}\] The remaining basis vectors \(\{p_j: j = \tilde{n}-s+1, \ldots, \tilde{n}\}\) form a basis of \(\ker\mathrm{\texttt{c}}\). In this sense we say that they correspond to infinite eigenvalues of ?? . Moreover, \[\begin{align} {2} \mathrm{\texttt{b}}(p_j,p_{j'}) &= \lambda_j \delta_{j,j'}, \quad && \;j \in \{1, \ldots, \tilde{n}-s\}, \;j'\in\{1,\ldots,\tilde{n}\}; \label{orth95b} \\ \mathrm{\texttt{c}}(p_j,p_{j'}) &= \delta_{j,j'}, \quad && \;j\in\{1,\ldots,\tilde{n}-s\},\;j'\in\{1,\ldots,\tilde{n}\}. \label{orth95c} \end{align}\] {#eq: sublabel=eq:orth95b,eq:orth95c} If \(s=0\) (trivial case) the same conclusion holds but the positive definiteness condition on \(\mathrm{\texttt{b}}\) is not required, and all eigenvalues are finite.

Proof. Suppose \(s \ge 1\) and \(\mathrm{\texttt{b}}\) is positive definite on \(\ker \mathrm{\texttt{c}}\). Let us choose \(\widehat{V}\subseteq\widetilde{V}\) such that \(\widetilde{V}= \widehat{V}\oplus \ker \mathrm{\texttt{c}}\) (as in Lemma 3); then every \(p \in \widetilde{V}\) has a unique decomposition \(p = \widehat{p}+ p_{\mathrm{\texttt{c}}}\) with \(\widehat{p}\in \widehat{V}\) and \(p_{\mathrm{\texttt{c}}}\in \ker \mathrm{\texttt{c}}\). Thus the GEVP ?? is equivalent to seeking \(\lambda \in \mathbb{R}\), \(\widehat{p}\in \widehat{V}\) and \(p_{\mathrm{\texttt{c}}} \in \ker \mathrm{\texttt{c}}\) such that \(\widehat{p}+ p_{\mathrm{\texttt{c}}} \ne 0\) and \[\begin{align} \label{Ivan1a} \mathrm{\texttt{b}}(\widehat{p}+ p_{\mathrm{\texttt{c}}}, v ) = \lambda \mathrm{\texttt{c}}(\widehat{p}, v) \qquad \text{for all} \;\; v \in \widetilde{V}. \end{align}\tag{17}\] This, in turn, is equivalent to solving the coupled system \[\begin{align} {2} \mathrm{\texttt{b}}(\widehat{p}, v_\mathrm{\texttt{c}}) + \mathrm{\texttt{b}}(p_{\mathrm{\texttt{c}}}, v_{\mathrm{\texttt{c}}}) &= 0 \qquad && \text{for all} \;\; v_{\mathrm{\texttt{c}}} \in \ker \mathrm{\texttt{c}}, \tag{18}\\ \mathrm{\texttt{b}}(\widehat{p},\widehat{v}) + \mathrm{\texttt{b}}(p_{\mathrm{\texttt{c}}},\widehat{v}) &= \lambda \mathrm{\texttt{c}}(\widehat{p},\widehat{v}) \qquad\quad && \text{for all} \;\; \widehat{v}\in \widehat{V}, \tag{19} \end{align}\] for \(\lambda \in \mathbb{R}\), \(\widehat{p}\in \widehat{V}\) and \(p_{\mathrm{\texttt{c}}} \in \ker \mathrm{\texttt{c}}\) such that \(\widehat{p}+ p_{\mathrm{\texttt{c}}} \ne 0\).

Now, by assumption on \(\mathrm{\texttt{b}}\), for each \(\widehat{w}\in \widehat{V}\), there exists a unique \(w_\mathrm{\texttt{c}}\in \ker \mathrm{\texttt{c}}\) such that \[\begin{align} \label{Ivan1c1} \mathrm{\texttt{b}}(w_\mathrm{\texttt{c}}, v_\mathrm{\texttt{c}}) = - \mathrm{\texttt{b}}(\widehat{w}, v_\mathrm{\texttt{c}}) \qquad \text{for all} \;\; v_\mathrm{\texttt{c}}\in \ker \mathrm{\texttt{c}}. \end{align}\tag{20}\] Denoting the solution to 20 as \(w_\mathrm{\texttt{c}}= \mathcal{S}\widehat{w}\), this defines a linear map \(\mathcal{S}: \widehat{V}\rightarrow \ker \mathrm{\texttt{c}}\) with the property \[\begin{align} \label{Ivan1b1} \mathrm{\texttt{b}}(\mathcal{S}\widehat{w}, v_\mathrm{\texttt{c}}) = - \mathrm{\texttt{b}}(\widehat{w}, v_\mathrm{\texttt{c}}) \qquad \text{for all} \;\; \widehat{w}\in \widehat{V}\;\; \text{and} \;\; v_\mathrm{\texttt{c}}\in \ker \mathrm{\texttt{c}}, \end{align}\tag{21}\] or, equivalently, \[\begin{align} \label{Ivan1d} \mathrm{\texttt{b}}\bigl((I + \mathcal{S}) \widehat{w}, v_\mathrm{\texttt{c}}\bigr) = 0 \qquad \text{for all} \;\; \widehat{w}\in \widehat{V}\;\; \text{and} \;\; v_\mathrm{\texttt{c}}\in \ker \mathrm{\texttt{c}}. \end{align}\tag{22}\]

We now consider the GEVP: find \(\lambda \in \mathbb{C}\) and \(\widehat{p}\in \widehat{V}\backslash \{0\}\) such that \[\begin{align} \label{Ivan1e} \widetilde{\mathrm{\texttt{b}}} (\widehat{p}, \widehat{v}) \mathrel{\mathop{:}}=\mathrm{\texttt{b}}\bigl((I + \mathcal{S}) \widehat{p}, \widehat{v}\bigr) = \lambda \mathrm{\texttt{c}}(\widehat{p}, \widehat{v}) \qquad \text{for all} \;\; \widehat{v}\in \widehat{V}. \end{align}\tag{23}\] (This is the Schur complement of 18 , 19 .)

Note that \(\mathcal{S}\) is self-adjoint with respect to \(\mathrm{\texttt{b}}\). This is because, using the symmetry of \(\mathrm{\texttt{b}}\) (at the first and third steps) and the definition of \(\mathcal{S}\) (at the second and fourth steps), \[\mathrm{\texttt{b}}(\mathcal{S}\widehat{p}, \widehat{v}) = \mathrm{\texttt{b}}(\widehat{v}, \mathcal{S}\widehat{p}) = -\mathrm{\texttt{b}}(\mathcal{S}\widehat{v}, \mathcal{S}\widehat{p}) = -\mathrm{\texttt{b}}( \mathcal{S}\widehat{p},\mathcal{S}\widehat{v}) = \mathrm{\texttt{b}}(\widehat{p}, \mathcal{S}\widehat{v}).\] Thus the bilinear form \(\widetilde{\mathrm{\texttt{b}}}\) on the left-hand side of 23 is symmetric on \(\widehat{V}\). Now \(\mathrm{\texttt{c}}\) is symmetric and (by Lemma 3) positive definite on \(\widehat{V}\); so the GEVP 23 has \(\tilde{n}-s\) eigenpairs, which we denote \((\lambda_j, \widehat{p}_{j})\), \(j = 1, \ldots , \tilde{n}-s\), with real finite eigenvalues \(\lambda_j\), which can be ordered as in ?? . The \(\widehat{p}_{j} \in \widehat{V}\) can be chosen orthonormal with respect to \(\mathrm{\texttt{c}}\) and form a basis of \(\widehat{V}\). Now we define \[\begin{align} \label{eigdefj} p_j = (I + \mathcal{S})\widehat{p}_{j}, \qquad j = 1, \ldots, \tilde{n}-s. \end{align}\tag{24}\] By 24 , 23 and since \(\mathcal{S}\widehat{p}_{j} \in \ker \mathrm{\texttt{c}}\), we have \[\mathrm{\texttt{b}}(p_j, \widehat{v}) = {\widetilde{\mathrm{\texttt{b}}}(\widehat{p}_{j},\widehat{v})} = \lambda_j \mathrm{\texttt{c}}(\widehat{p}_{j}, \widehat{v}) = \lambda_j \mathrm{\texttt{c}}(p_{j}, \widehat{v}) \qquad \text{for all} \;\; \widehat{v}\in \widehat{V}.\] Also, by 24 and 22 we have \(\mathrm{\texttt{b}}(p_j, v_{\mathrm{\texttt{c}}}) = 0\) for all \(v_{\mathrm{\texttt{c}}} \in \ker \mathrm{\texttt{c}}\), and so (see 18 and 19 ), \((\lambda_j, p_j),\, j = 1, \ldots ,\tilde{n}-s\) are eigenpairs of the original GEVP ?? . Now, choosing an arbitrary basis, \(\{p_{\tilde{n}-s+1},\ldots,p_{\tilde{n}}\}\), of \(\ker\mathrm{\texttt{c}}\) we obtain a basis of \(\widetilde{V}\). For \(j \in \{1, \ldots , \tilde{n}-s\}\), relation ?? follows for \(j' \in \{ 1, \ldots, \tilde{n}-s\}\) because \(\mathcal{S}\widehat{p}_{j}, \mathcal{S}\widehat{p}_{j'} \in \ker \mathrm{\texttt{c}}\), and so \(\mathrm{\texttt{c}}(p_j,p_{j'}) = \mathrm{\texttt{c}}(\widehat{p}_{j},\widehat{p}_{j'}) = \delta_{j,j'}\) since the \(\{\widehat{p}_{j}: j = 1, \ldots, \tilde{n}-s\}\) were chosen orthonormal with respect to \(\mathrm{\texttt{c}}\). For \(j' \in \{\tilde{n}-s+1, \ldots, \tilde{n}\}\), ?? is trivial because then \(p_{j'} \in \ker \mathrm{\texttt{c}}\).

Relation ?? then follows directly from ?? because, for \(j \in \{1, \ldots, \tilde{n}-s\}\) and any \(j' {\in \{1,\dots,\tilde{n}\}}\), \(\mathrm{\texttt{b}}(p_j, p_{j'}) = \lambda_j\mathrm{\texttt{c}}(p_j, p_{j'})\). This completes the proof when \(s\ge1\), while the case \(s = 0\) is trivial since ?? can then be reduced to a standard eigenvalue problem. ◻

Remark 4. Note that some statements in the previous proof can be made more general:

  1. The requirement that \(\mathrm{\texttt{b}}\) is positive definite on \(\ker \mathrm{\texttt{c}}\) in part (i) of Theorem 3 could be weakened to simply requiring that problem 20 has a unique solution for all \(\widehat{w}\in \widehat{V}\), where \(\widehat{V}\) is as in Lemma 3. However, since we shall need the positive definiteness in Proposition 5 anyway, we avoid this extra generality.

  2. In general, the eigenvectors \(p_j,\, j = 1, \ldots \tilde{n}-s\) defined via 24 are not elements of \(\widehat{V}\) because \(\mathcal{S}\) maps into \(\ker \mathrm{\texttt{c}}\).

The following corollary then follows directly from Theorem 3.

Corollary 1. Under the conditions of Theorem 3, every \(v \in V\) can be expressed as \[\label{Ivan5} v = v_0 + \sum_{j=1}^{\tilde{n}-s} \mathrm{\texttt{c}}(v,p_j)p_j \quad \text{with} \quad v_0 \in \ker \mathrm{\texttt{c}},\qquad{(10)}\] If \(\ker\mathrm{\texttt{c}}= \{0\}\) then \(s=0\) and \(v_0 = 0\) .

We now define a projection operator mapping \({\widetilde{V}}\) onto the span of the first \(m\) eigenvectors of ?? , where \(m\) is chosen so that all eigenvectors corresponding to negative eigenvalues are included (recall the ordering ?? ). This operator is crucial in the analysis of the proposed preconditioner.

Proposition 5 (Projection onto the \(m\)-dimensional eigenspace). Under the conditions of Theorem 3, suppose that there exists \(m \in \{ 1, \ldots, \tilde{n}-s-1\}\) such that \(\lambda_{m+1}>0\), and define the projector \[\label{Ivan6} \Pi v = \sum_{j=1}^m \mathrm{\texttt{c}}(v,p_j) p_j \qquad \text{for all} \;\; v \in \widetilde{V}.\qquad{(11)}\] Then \[\label{Ivan7} \mathrm{\texttt{c}}(v-\Pi v,v-\Pi v) \le \frac{1}{\lambda_{m+1}} \mathrm{\texttt{b}}(v-\Pi v,v-\Pi v).\qquad{(12)}\]

When applying this proposition we will show that such an \(m\) exists, under a modest assumption.

Proof.   Let \(v \in {\widetilde{V}}\) be expressed as in ?? and set \(\alpha_j\mathrel{\mathop{:}}=\mathrm{\texttt{c}}(v,p_j)\), \(j\in\{1,\ldots,\tilde{n}-s\}\). We use the properties from Corollary 1 and Theorem 3 to obtain, \[\begin{align} \mathrm{\texttt{b}}(v-\Pi v,v-\Pi v) &= \mathrm{\texttt{b}}\Biggl(v_0 + \sum_{j=m+1}^{\tilde{n}-s} \alpha_j p_j\, ,\, v_0 + \sum_{j=m+1}^{\tilde{n}-s} \alpha_j p_j \Biggr) \nonumber\\[1ex] &= \mathrm{\texttt{b}}(v_0,v_0) + 2 \sum_{j = m+1}^{\tilde{n}-s} \alpha_j \mathrm{\texttt{b}}( p_j, v_0) + \sum_{j=m+1}^{\tilde{n}-s} \lambda_j \alpha_j^2 \nonumber \\ &= \mathrm{\texttt{b}}(v_0,v_0) + 2 \sum_{j = m+1}^{\tilde{n}-s} \alpha_j \lambda_j \mathrm{\texttt{c}}(p_j, v_0) + \sum_{j=m+1}^{\tilde{n}-s} \lambda_j \alpha_j^2. \end{align}\] Then we use the the fact that \(v_0 \in \ker \mathrm{\texttt{c}}\) and the positive definiteness of \(\mathrm{\texttt{b}}\) on \(\ker \mathrm{\texttt{c}}\) to obtain \[\label{Ivan281} \mathrm{\texttt{b}}(v-\Pi v,v-\Pi v) = \mathrm{\texttt{b}}(v_0,v_0) + \sum_{j=m+1}^{\tilde{n}-s} \lambda_j \alpha_j^2 \ge \sum_{j=m+1}^{\tilde{n}-s} \lambda_j \alpha_j^2 \ge \lambda_{m+1} \sum_{j=m+1}^{\tilde{n}-s} \alpha_j^2.\tag{25}\] Using an almost identical argument and the results from Theorem 3 we also obtain \[\mathrm{\texttt{c}}(v-\Pi v,v-\Pi v) = \sum_{j=m+1}^{\tilde{n}-s} \alpha_j^2. \label{Ivan282}\tag{26}\] Now ?? follows from 25 and 26 . ◻

This lemma shows that, although the bilinear form \(\mathrm{\texttt{b}}(\cdot,\cdot)\) may be indefinite on the full space, it becomes positive definite on the complement of the subspace spanned by the first \(m\) eigenvectors, provided all discarded eigenvalues \(\lambda_{j}\) (for \(j > m\)) are strictly positive. In the context of coarse space construction, this implies that if we retain all eigenfunctions associated with non-positive or small eigenvalues, then the remainder is well-controlled by \(\mathrm{\texttt{b}}(\cdot,\cdot)\), leading to stability estimates essential for robust preconditioning.

2.3 Domain decomposition↩︎

Notation 6. Let \(\Omega'\) be any subdomain of \(\Omega\), composed of a union of elements of the mesh \(\mathcal{T}_h\). We introduce the finite element spaces: \[\widetilde{V}_{\Omega'} \mathrel{\mathop{:}}=\bigl\{v|_{\Omega'} : v \in V^h\bigr\} \subseteq H^1(\Omega') \qquad \text{and} \qquad V_{\Omega'} \mathrel{\mathop{:}}=\bigl\{v \in \widetilde{V}_{\Omega'} : v|_{\partial \Omega'} = 0\bigr\} \subseteq H^1_0(\Omega').\] For any \(v_{\Omega'} \in V_{\Omega'}\), we let \(E_{\Omega'} v_{\Omega'} \in V^h\) denote its zero extension to the whole domain \(\Omega\), and we define the operator \(R_{\Omega'}: V^h \rightarrow V_{\Omega'}\) by \[(R_{\Omega'} v , w_{\Omega'})_{\Omega'} \mathrel{\mathop{:}}=(v,E_{\Omega'}w_{\Omega'}) \quad \text{for all} \quad v \in V^h \quad \text{and} \quad w_{\Omega'} \in V_{\Omega'}.\]

In order to construct the two-level Schwarz preconditioner, we choose two overlapping covers of \(\Omega\). The first consists of ‘local subdomains’ \(\{\Omega_j^\ell\}_{j=1}^Q\) on which restrictions of [eq: 2_12] will be solved. The second consists of ‘coarse-space subdomains’ \(\{\Omega_i^c\}_{i=1}^N\) on which generalised eigenvalue problems will be solved to build the coarse space. All subdomains are assumed to be polygonal (2D) or polyhedral (3D) and have boundaries which are resolved by the fine mesh \(\mathcal{T}_h\), and satisfy \[\Omega = \bigcup_{i=1}^{N} \Omega_i^c, \qquad \Omega = \bigcup_{j=1}^{Q} \Omega_j^\ell. \label{covers}\tag{27}\] There is no need for these covers to coincide and in practical algorithms it may be convenient for them to be different. The ‘coarse’ subdomains should capture the indefiniteness and are not required to be small in theory. For each, \({\Omega_i^c}\) and \({\Omega_j^\ell}\), we denote their diameters by \(H_{c,i}\), \(H_{\ell,j}\) respectively, and we set \(H_c \mathrel{\mathop{:}}=\max_{i=1}^N H_{c,i}\) and \(H_\ell \mathrel{\mathop{:}}=\max_{j=1}^Q H_{\ell,j}\). We define the corresponding finite element spaces \(V_{\Omega^c_i}, \widetilde{V}_{\Omega^c_i}\) and \(V_{\Omega^\ell_j}, \widetilde{V}_{\Omega^\ell_j}\) as in Notation 6. We also use abbreviations \(E_i^c = E_{\Omega_i^c}\) and \(E_j^\ell = E_{\Omega_j^\ell}\).

Assumption 4. In the theory we assume that the overlap \(\delta_\ell\) of the local cover \(\{\Omega_j^\ell\}\) (as defined in, e.g.[16]) satisfies \(\delta_\ell \geq k^{-1}\).

Standard properties for such overlapping covers are (see, e.g.[32]): \[\sum_{i=1}^N \Vert v \vert_{\Omega_i^c} \Vert_{1,k,\Omega_i^c}^2 \leq \Lambda_c \Vert v \Vert_{1,k}^2 , \qquad \sum_{j=1}^Q \Vert v\vert_{\Omega_j^\ell} \Vert_{1,k,\Omega_j^\ell}^2 \leq \Lambda_\ell \Vert v \Vert_{1,k}^2 \qquad \text{for all} \;\; v \in V^h, \label{overlap}\tag{28}\] where \[\label{Lambda95l95c} \Lambda_\circ \mathrel{\mathop{:}}=\max_{T\in\mathcal{T}_h}\bigl(\#\bigl\{\Omega^\circ_i \mid 1 \le i \le N,\, T \subseteq \Omega^\circ_i\bigr\} \bigr) \qquad \text{for} \;\; \circ \in \{c,\ell\}.\tag{29}\] In addition ([32]), for any \(v_j \in H^1_0(\Omega)\) with \(\mathop{\mathrm{supp}}v_j \subseteq \overline{\Omega_j^\ell}\) for each \(j = 1, \ldots, Q\), we have the estimate \[\label{overlap1} \bigg\| \sum_{j=1}^Q v_j \bigg\|_{1,k}^2 \le \Lambda_\ell \sum_{j=1}^Q \|v_j|_{\Omega_j^\ell}\|_{1,k,\Omega_j^\ell}^2,\tag{30}\] and, similarly, for \(v_i \in H_0^1(\Omega)\) with \(\mathop{\mathrm{supp}}v_i \subseteq \overline{\Omega_i^c}\), for each \(i=1,\ldots,N\). \[\label{overlap2} \bigg\| \sum_{i=1}^N v_i \bigg\|_{1,k}^2 \le \Lambda_c \sum_{i=1}^N \|v_i|_{\Omega_i^c}\|_{1,k,\Omega_i^c}^2 .\tag{31}\] The one-level additive Schwarz preconditioner can now be given in matrix form as \[\label{one95level} \mathbf{M}^{-1}_{AS,1} = \sum_{j=1}^{Q} \mathbf{E}^{\ell}_j (\mathbf{B}^{\ell}_j)^{-1} \mathbf{R}^{\ell}_j, \qquad \text{where } \mathbf{B}^{\ell}_j = \mathbf{R}^{\ell}_j \mathbf{B} \mathbf{E}^{\ell}_j;\tag{32}\] here, \(\mathbf{E}^{\ell}_j\) and \(\mathbf{R}^{\ell}_j\) denote the matrix representations of \(E_j^\ell\) and \(R_j^\ell\), respectively, with respect to the basis functions \(\lbrace \phi_i \rbrace_{i=1}^{\dim(V^h)}\) of \(V^h\).

In order to improve the preconditioner, a global coarse space is added. Let \(V_0 \subseteq V^h\) be such a coarse space, let \(E_0: V_0 \rightarrow V^h\) be the natural embedding, and let \(R_0\) be the \(L^2\) adjoint of \(E_0\), \[(R_0 w,v_0) = (w,E_0 v_0) \qquad \text{for all} \;w \in V^h, v_0 \in V_0.\] The two-level additive Schwarz preconditioner is then \[\label{eq:3229523} \mathbf{M}^{-1}_{AS,2} = \mathbf{E}_0 \mathbf{B}^{-1}_0 \mathbf{R}_0 + \mathbf{M}^{-1}_{AS,1}, \qquad \text{where } \mathbf{B}_0 \mathrel{\mathop{:}}=\mathbf{R}_0 \mathbf{B} \mathbf{E}_0\tag{33}\] (with \(\mathbf{E}_0\) and \(\mathbf{R}_0\) denoting matrix representations of \(E_0\) and \(R_0\)). The preconditioned version of 12 is \[\label{eq:3229524} \mathbf{M}^{-1}_{AS,2} \mathbf{B} \mathbf{u} = \mathbf{M}_{AS,2}^{-1} \mathbf{f}.\tag{34}\]

For the analysis we need certain projection operators defined as follows. For each \(j = 1, \ldots, Q\), we define \(T^\ell_j: V^h \rightarrow V_{\Omega^\ell_j}\) and \(T_0: V^h \rightarrow V_0\) by \[\begin{align} b_{\Omega^\ell_j}(T^\ell_j u, v) = b(u, E^\ell_j v) \qquad &\text{for all} \;{u\in V^h,}\, v \in V_{\Omega^\ell_j}, \tag{35} \\[0.5ex] b(T_0 u, v) = b(u, E_0 v) = b(u,v) \qquad &\text{for all} \;\; u\in V^h \;\; \text{and} \;\; \;v \in V_0. \tag{36} \end{align}\] Sufficient conditions for the existence of \(T^\ell_j\) and \(T_0\) (and hence invertibility of \(\mathbf{B}^\ell_j\), \(\mathbf{B}_0\)) are given later (Lemmas 8 and 10). Given the operators \(T^\ell_j\) and \(T_0\), we define \(T: V^h \rightarrow V^h\) by \[T = E_0T_0 + \sum_{j=1}^Q E^\ell_j T^\ell_j. \label{eq:3229526}\tag{37}\] Then the preconditioned system [eq: 2_24] is related to \(T\) via the following proposition (cf, e.g.[33]).

Proposition 7. For any \(u,v \in V^h\), with corresponding nodal vectors \(\mathbf{u},\mathbf{v} \in \mathbb{R}^{\dim(V^h)}\), \[\label{eq:3229527} \langle \mathbf{M}_{AS,2}^{-1} \mathbf{B} \mathbf{u}, \mathbf{v} \rangle_{\mathbf{D}_k} = (T u, v)_{1, k},\qquad{(13)}\] where \(\langle \cdot , \cdot \rangle_{\mathbf{D}_k}\) is the inner product on \(\mathbb{R}^{\dim(V^h)}\) and the matrix \(\mathbf{D}_k\) given by \[\label{def:Dk} \mathbf{D}_k\mathrel{\mathop{:}}=\mathbf{A} + k^2 \mathbf{S}.\qquad{(14)}\]

2.4 The \(H_k\)-GenEO coarse space↩︎

Definition 1 ([5]). Given any \(\Omega' \subseteq \Omega\), formed from a union of elements \(T \in \mathcal{T}_h\), let \[\begin{align} \overline{\mathop{\mathrm{dof}}}(\Omega') &\mathrel{\mathop{:}}=\bigl\{j \mid 1 \le {j} \le \dim(V^h) \text{ and } \mathop{\mathrm{supp}}(\phi_{j}) \cap \Omega' \ne \emptyset\bigr\}, \\[0.5ex] \mathop{\mathrm{dof}}(\Omega') &\mathrel{\mathop{:}}=\bigl\{j \mid 1 \le j \le \dim(V^h) \text{ and } \mathop{\mathrm{supp}}(\phi_{{j}}) \subseteq \overline{\Omega'}\bigr\} \end{align}\] denote, respectively, the degrees of freedom in the closed domain \(\overline{\Omega'}\) and its interior.

Notation 8. In what follows we will write \(A\lesssim B\), to mean \(A \leq C B\) with a constant \(C\) independent of the key parameters \(k,h,C_{\mathrm{\textsf{inv}}}, \delta_\ell, H_\ell, H_c, \Lambda_c, \Lambda_\ell\), \(a_{\mathrm{\textsf{max}}}\), \(n_{\mathrm{\textsf{min}}}\) and \(C_{\mathrm{\textsf{stab}}}\). Equivalently, we write this as \(B \gtrsim A\). We write \(A \sim B\) when \(A \lesssim B\) and \(B\lesssim A\). The hidden constants in these expressions may depend on other constants such as \(k_0\) and \(C_{\mathrm{\textsf{SR}}}\), but we do not keep explicit track of these.

Definition 2 (Operator POU on the coarse subdomains). For any \(l\in\mathop{\mathrm{dof}}\Omega\), let \(\mu_l\) denote the number of subdomains for which \(l\) is an internal degree of freedom, i.e. \[\mu_l \mathrel{\mathop{:}}=\#\bigl\{i \mid 1 \le i \le N,\, l \in \mathop{\mathrm{dof}}({\Omega}_i^c)\bigr\}{.}\] Then, for each \(i = 1, \ldots, N\), we define the coarse space partition of unity operator, \(\Xi_i^c: \widetilde{V}_{\Omega^c_i} \rightarrow V_{\Omega^c_i}\) using a weighted combination of degrees of freedom, namely, for \(v=\sum_{l\in\overline{\mathop{\mathrm{dof}}}(\Omega^c_i)} v_l\phi_l^i\in \widetilde{V}_{\Omega^c_i}\) we define \[\Xi_i^c(v) \mathrel{\mathop{:}}=\sum_{l\in\mathop{\mathrm{dof}}(\Omega^c_i)}\frac{1}{\mu_l }v_l \phi_l^i, \qquad \text{where} \quad \phi_l^i \mathrel{\mathop{:}}=\phi_l\vert_{\Omega_i^c}.\]

It is easy to verify that this is a partition of unity, in the sense that \[\label{partition95of95unity} \sum_{i = 1}^N E_i^{c} \Xi_i^{c}({v|_{\Omega_i^c}}) = v \qquad \text{for all} \;\; v \in V^h.\tag{38}\]

Definition 3 (Local generalised eigenvalue problem (GEVP)). For each \(i\in\{1,\ldots,N\}\), define \(b_{\Omega_i^c}\) as in ?? and set \[\label{Ivan14} c_{\Omega_i^c}(w,v) \mathrel{\mathop{:}}=\bigl(\Xi_i^c(w),\Xi_i^c(v)\bigr)_{1,k,\Omega_i^c} \qquad \text{for all} \;w,v \in \widetilde{V}_{\Omega_i^c}.\qquad{(15)}\] The generalised eigenvalue problem is then to find \(p^i\in \widetilde{V}_{\Omega_i^c} \setminus\{0\}\) and \(\lambda^i\in\mathbb{R}\), such that \[\label{eq:3259512} b_{\Omega_i^c}(p^i,v) = \lambda^i \, c_{\Omega_i^c} (p^i, v) \qquad \text{for all} \;v\in\widetilde{V}_{\Omega_i^c}.\qquad{(16)}\]

In order to define the \(H_k\)-GenEO coarse space we need an assumption on the spectra of ?? .

Assumption 5. Let \((p^i_m,\lambda^i_m)\) be eigenpairs for ?? with \(\lambda_1^i, \lambda_2^i, \ldots\) chosen in non-decreasing order. For each \(i= 1,\ldots,N\), we assume that there is \(m_i\geq 1\) such that \(\lambda_{m_i+1}^i>0\) . (A mild sufficient condition for the existence of such \(m_i\) is given in Theorem 13.)

Definition 4 (\(H_k\)-GenEO Coarse space). Under Assumption 5, the coarse space, \(V_0\), is given by \[V_0 \mathrel{\mathop{:}}=\mathop{\mathrm{span}}\bigl\{E_i^c\Xi_i^c(p^i_m): \, m = 1, \ldots, m_i\;\; \text{and} \;\; i = 1, \ldots, N\bigr\}.\]

Remark 9. In contrast to previous works on the GenEO construction (e.g.[5], [13] and [8]) (where the forms are always at least semi-definite), the left-hand side of ?? can here be indefinite and also the right-hand side in [eq: 5_12] is based on a \(k\)-weighted scalar product. However, as \(k \rightarrow 0\) the GEVP ?? approaches the GEVP solved in the classical GenEO method, e.g.[5].

In the proof of our main result we also need the additional estimates for \(\Vert \Xi_i^{{c}}(v)\Vert_{\Omega_i^c}\).

Lemma 4. The following estimates hold: \[\begin{align} {2} \Vert \Xi_i^{{c}} (v)\Vert_{\Omega_i^c} &\lesssim \Vert v \Vert_{\Omega_i^c} \qquad && \text{for all} \;\; v \in \widetilde{V}_{{\Omega_i^c}}, \label{Xiest1} \\[1ex] \Vert \Xi_i^{{c}} (v)\Vert_{\Omega_i^c} &\gtrsim \Lambda_c^{-1} \Vert v \Vert_{\Omega_i^c} \qquad && \text{for all} \;\; v \in V_{{\Omega_i^c}}. \label{Xiest2} \end{align}\] {#eq: sublabel=eq:Xiest1,eq:Xiest2} Here the hidden constants depend on \(C_{\mathrm{\textsf{SR}}}\) but not on any of the key parameters (see Notation 8).

Proof.   Due to the assumption that we are working with Lagrange elements, for any \(T \in \mathcal{T}_h\), there is an affine map \(F_T\) mapping the unit simplex \(\widehat{T}\) to \(T\). Then (see, e.g.[34]), for any \(w \in V^h\), setting \(\widehat{w} = w \circ F_T\), we have \[\begin{align} \label{eqnorm} \Vert w \Vert_T^2 \;\sim\; h_T^{d} \Vert \widehat{w}\Vert_{\widehat{T}}^2 \;\sim \;h_T^{d} \sum_{j \in \overline{\mathop{\mathrm{dof}}}(T)} w_j^2, \end{align}\tag{39}\] where \(w_j\) is the value of \(w\) at \(j \in \mathop{\mathrm{dof}}(T)\) and the hidden constants are independent of the mesh and \(w\). (The second relation in 39 uses (\(r\)-dependent) equivalence of norms on the space of polynomials of degree \(r\).) Hence, by Definition 2 of \(\Xi_i^{{c}}(v)\) and since freedoms of \(\Xi_i^c(v)\) are interior to \(\Omega_i^c\), we have for \(v \in V_{\Omega_i^c}\), \[\begin{align} \|\Xi_i^{{c}}(v)\|_{\Omega_i^c}^2 &= \sum_{T \subseteq \overline{\Omega_i^c}}\|\Xi_i^{c}(v)\|_{T}^2 \sim \sum_{T \subseteq \overline{\Omega_i^c}} h_T^d \sum_{j \in \overline{\mathop{\mathrm{dof}}}(T) \cap \mathop{\mathrm{dof}}(\Omega_i^c)} (\Xi_i^{{c}}(v))_j^2 = \sum_{T \subseteq \overline{\Omega_i^c}} h_T^d \sum_{j \in \overline{\mathop{\mathrm{dof}}}(T) \cap \mathop{\mathrm{dof}}(\Omega_i^c)} \mu_j^{-2} v_j^2 \nonumber\\[0.5ex] &\ge \Lambda_c^{-2} \sum_{T \subseteq \overline{\Omega_i^c}} h_T^d \sum_ {j \in \overline{\mathop{\mathrm{dof}}}(T) \cap \mathop{\mathrm{dof}}(\Omega_i^c)} v_j^2 \sim \Lambda_c^{-2}\sum_{T \subseteq \overline{\Omega_i^c}}\|v\|_{T}^2 = \Lambda_c^{-2} \|v\|_{\Omega_i^c}^2, \end{align}\] proving ?? . The proof of ?? is analogous but simpler. ◻

3 Statement of the main result and technical tools↩︎

We introduce the notation \[\begin{align} \Theta \mathrel{\mathop{:}}=\frac{1}{\min_{1 \le i \le N} \lambda^i_{m_i+1}} = {\frac{1}{\tau}}, \quad \text{where} \;\tau \;\text{is defined in \eqref{def:tau}}. \label{notation:theta} \end{align}\tag{40}\]

Theorem 10 (GMRES convergence of the two-level preconditioned system). Let Assumptions 1, 2, 3, 4 and 5 be satisfied and let \(C_{\mathrm{\textsf{inv}}}\), \(C_{\mathrm{\textsf{eig}}}\), \(\Lambda_l\), \(\Lambda_c\) be as in Assumption 3, Theorem 15 and 29 , respectively, and set \[\begin{align} \label{Cstar} C_{\ast}\mathrel{\mathop{:}}=C_{\mathrm{\textsf{eig}}}C^2_{\mathrm{\textsf{inv}}}. \end{align}\qquad{(17)}\] Further, let \(C_1\), \(C_2\), \(C_3\) be the constants (independent of the key parameters) derived in Lemma 5, Theorem 16 and Lemma 7 respectively. Assume that \(k\ge k_0\). Then there exists \(h_{\star}>0\) such that the following statements hold for all \(h\in(0,h_{\star})\) with \((kh)^2 \le C_1\). Suppose that \[\label{eq:324953} s \mathrel{\mathop{:}}=2{C_3}\bigl(1+\Lambda_\ell \Lambda_c^2 {a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1}} C_{\ast}\Theta \bigr)\Bigl({2 C_2\Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1}C_{\ast})^{1/2}k\Theta^{1/2}}(1+C_{\mathrm{\textsf{stab}}})+3k\Lambda_\ell H_\ell\Bigr) < 1.\qquad{(18)}\] Then, when GMRES is applied with the \(\langle\cdot,\cdot\rangle_{\mathbf{D}_k}\)-inner product with \(\mathbf{D}_k\) as in ?? to solve the preconditioned system given by 34 , then after \(m\) iterations, the norm of the residual, \(\mathbf{r}^{(m)}\), is bounded as follows: \[\label{eq:3249522} \|\mathbf{r}^{(m)}\|_{\mathbf{D}_{k}}^2 \le \bigl(1-\gamma^2\bigr)^m \|\mathbf{r}^{(0)}\|_{\mathbf{D}_{k}}^2,\qquad{(19)}\] where \(\gamma\) is given by \[\label{def95gamma} \gamma \mathrel{\mathop{:}}=\frac{1-s}{C_3\bigl(1+\Lambda_\ell\Lambda_c^2a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1}C_{\ast}\Theta\bigr)(18 + 8 {\Lambda_{\ell}}^2)}.\qquad{(20)}\]

Corollary 2. Under the same conditions as Theorem 10, if ?? holds, then there exists \(C>0\), which is independent of key parameters such that \[\label{eq:main} k H_\ell \le C \qquad\text{and}\qquad (1+C_{\mathrm{\textsf{stab}}})^2 k^2 \Theta \le C.\qquad{(21)}\] Conversely, if ?? holds for \(C>0\) small enough so that \[\label{C95small} 2C_3\bigl(1+\Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1}C_{\ast}C {k_0^{-2}} \bigr) \bigl({2 C_2\Lambda_c(a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}C)^{1/2}} +3 \Lambda_\ell C\bigr) < 1,\qquad{(22)}\] then ?? is satisfied and \(\gamma\) from ?? is bounded below by a positive number independent of \(k\).

Proof. Assume first that ?? is satisfied. Since \(\Lambda_\ell\ge1\), \(\Lambda_c\ge1\), \(a_{\mathrm{\textsf{max}}}\ge1\), \(n_{\mathrm{\textsf{min}}}\le 1\) and \(C_{\ast}\ge 1\), we have \[2C_2 k \Theta^{\frac{1}{2}} (1+C_{\mathrm{\textsf{stab}}}) + 3k H_\ell < \frac{1}{2C_3},\] and hence \[k H_\ell < \frac{1}{6C_3} \qquad\text{and}\qquad (1+C_{\mathrm{\textsf{stab}}})^2 k^{2}\Theta < \frac{1}{16C_2^2C_3^2},\] which yields ?? .

Conversely, assume that ?? is satisfied with \(C>0\) such that ?? holds. Since, by assumption, \({k\ge k_0}\), we have \[\Theta \le \frac{C}{(1+C_{\mathrm{\textsf{stab}}})^2 k^{2}} \le C{k_0^{-2}}.\] Hence, using this with ?? and ?? , we have \[\begin{align} s \le 2C_3 \bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}C k_0^{-2}\bigr) \bigl(2 C_2\Lambda_c(a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}C)^{1/2} +3\Lambda_\ell C\bigr) < 1, \label{ests} \end{align}\tag{41}\] i.e.@eq:eq:324953 is satisfied.

Moreover, combining ?? with 41 , we obtain a \(k\)-independent lower bound for \(\gamma\). ◻

In practice, conditions ?? will introduce constraints in the size of the local subdomains \(\Omega_j^\ell\) and on the number of modes to be added in the coarse space, both depending on \(k\).

For the rest of the paper we will assume that Assumptions 1, 2, 3, 4 and 5 are satisfied, although some of the intermediate results below require only a subset of these assumptions.

3.1 Properties of the \(H_k\)-GenEO coarse space↩︎

In this subsection we apply the abstract spectral theory from §2.2 to the generalised eigenvalue problem [eq: 5_12]. To ease the exposition, we restrict here to a single coarse subdomain \(\Omega_i^c\) with \(i\) fixed and (in this subsection only) we use the following simplified notation.

Notation 11 (Notation used in §3.1). For a given \(i\) we set \[\begin{align} & \widetilde{V}_i \mathrel{\mathop{:}}=\widetilde{V}_{\Omega_i^c}, \quad V_i \mathrel{\mathop{:}}=V_{\Omega_i^c}, \quad b_i \mathrel{\mathop{:}}={b}_{\Omega_i^c}, \quad c_i \mathrel{\mathop{:}}=c_{\Omega_i^c}, \\ & n_i \mathrel{\mathop{:}}=\dim \widetilde{V}_i \quad \text{and} \quad s_i \mathrel{\mathop{:}}=\dim \ker c_i. \end{align}\]

The next result identifies \(\ker c_i\) and shows that \(b_i\) is positive definite on it, as required in Theorem 3.

Lemma 5 (Positive definiteness of \(b_i\) on the kernel of \(c_i\)). We have \[\label{ker95scC} \ker c_i = \mathrm{span} \{ \phi_l^i: l \in \overline{\mathop{\mathrm{dof}}}(\Omega_i^c) \backslash \mathop{\mathrm{dof}}(\Omega_i^c)\}, \quad \text{where} \quad \phi_l^i = \phi_l\vert_{\Omega_i^c},\qquad{(23)}\] and \(\phi_l\in \widetilde{V}_i\) is the nodal basis function \((\phi_l)_{l'} = \delta_{l,l'}\). Moreover, there exists a constant \(C_1>0\) (which may depend on \(C_{\mathrm{\textsf{SR}}}\) from Assumption 3) such that, for all \(k,h\) satisfying \((hk)^2 \le C_1\), we have \[\begin{align} \label{pdb1} b_i(v,v) \ge {C_1} h^{-2} \|v\|_{\Omega_i^c}^2 \qquad \text{for all} \;\; v \in \ker c_i. \end{align}\qquad{(24)}\]

Proof.   The hidden constants in this proof may depend on \(C_{\mathrm{\textsf{SR}}}\). To obtain ?? , note that, from Definition 2, if \(v \in \widetilde{V}_i\) with \(v_l = 0\) for all \(l \in \mathop{\mathrm{dof}}\Omega_i^c\), then \(\Xi_i^c(v) \equiv 0\) and so \(v \in \ker c_i\). Conversely, if \(v \in \ker c_i\), then, by Assumption 1, \[0 = c_i(v,v) = \int_{\Omega_i^c} \left({\nabla} {\Xi_i^c}(v)\cdot A \nabla {\Xi_i^c}(v) + k^2 {n} \,{(\Xi_i^c(v))^2} \right) \ge {\Vert \nabla \Xi_i^c(v)\Vert_{a,\Omega_i^c}^2 + k^2 n_{\mathrm{\textsf{min}}}\Vert \Xi_i^c(v) \Vert_{\Omega_i^c}^2},\] which implies \(\Xi_i^c(v)\equiv 0\), and thus \(v_l = 0\) for all \(l \in \mathop{\mathrm{dof}}\Omega_i^c\), hence proving ?? .

To obtain ?? , note first that, for any \(v \in \widetilde{V}_i\) (since \(a_{\mathrm{\textsf{min}}}=1 = n_{\mathrm{\textsf{max}}}\)), \[b_{i}(v,v) = \int_{\Omega_i^c} \bigl(\nabla v \cdot (A\nabla v) - k^2 {n} v^2\bigr) \ge \|\nabla v\|_{\Omega_i^c}^2 - k^2 \|v\|_{\Omega_i^c}^2. \label{iggeq1}\tag{42}\] Now let \(v \in \ker c_i\) and note that \(v\) is supported only on elements which touch \(\partial \Omega_i\). For any degree of freedom \(l \in \partial \Omega_i^c\) let \(\mathcal{T}_\ell^0\) denote the set of elements containing the freedom \(l\) and let \(\mathcal{T}_l\) denote the union of \(\mathcal{T}_\ell^0\) with all elements which touch the elements in \(\mathcal{T}_\ell^0\). Then let \(h_\ell = \mathrm{diam}(\mathcal{T}_\ell)\), and let \(B(l,h_l)\) denote the open ball of radius \(h_l\) centred at \(l\). Then it is easy to see that \(\mathop{\mathrm{supp}}v \subseteq \bigcup_{l\in \partial \Omega_i^c} B(l,h_l)\). Moreover, for each \(l\), \(v\) vanishes on a portion of the boundary of \(B(l,h_l)\) having \(d-1\) dimensional measure \(\gtrsim h_l\). So, by the generalised Friedrichs’ inequality (see, e.g.[16]) and since \(h_l \lesssim h\), we have \[\label{Fried} \Vert \nabla v \Vert_{B(l,2h_l)}^2 \gtrsim {h_{l}}^{-2} \Vert v \Vert_{B(l,2h_l)}^2 \gtrsim {h}^{-2} \Vert v \Vert_{B(l,2h_l)}^2.\tag{43}\] Also, by shape regularity, each \(T \in \mathop{\mathrm{supp}}v\) can only be overlapped by a bounded number of balls \(B(l,h_l)\) as \(h \rightarrow 0\); so we have \[\begin{align} \Vert \nabla v \Vert_{\Omega_i^c}^2 &= \sum_{T\in \mathcal{T}_h(v)} \Vert \nabla v \Vert_{T}^2 \sim \sum_{T\in \mathcal{T}_h(v)} \sum_{l \in \partial \Omega_i^c}\Vert \nabla v \Vert_{T\cap B(l,h_l)}^2 = \sum_{l \in \partial \Omega_i^c} \sum_{T\in \mathcal{T}_h(v)} \Vert \nabla v \Vert_{T\cap B(l,h_l)}^2 = \sum_{l \in \partial \Omega_i^c} \Vert \nabla v \Vert_{B(l,h_l)}^2. \end{align}\] Combining this with 43 we obtain \(\Vert \nabla v \Vert_{\Omega_i^c}^2 \gtrsim h^{-2} \Vert v \Vert_{\Omega_i^c}^2\).

Hence, from 42 , there exists a constant \(C'>0\) (which may depend on \(C_{\mathrm{\textsf{SR}}}\)) such that \[b_i (v,v) \ge (C' h^{-2} - k^2) \|v\|_{\Omega_i^c}^2 = (C' - (hk)^2) h^{-2} \|v\|_{\Omega_i^c}^2\] for all \(h\) and \(v \in \ker c_i\). Hence, if \((hk)^2 \le C_1\mathrel{\mathop{:}}=C'/2\), we have \(b_i (v,v) \ge C_1 h^{-2} \|v\|_{\Omega_i^c}^2\). ◻

Remark 12. We can now apply the theory in §2.2 with \(\mathrm{\texttt{b}}\mathrel{\mathop{:}}=b_{i}\) and \(\mathrm{\texttt{c}}\mathrel{\mathop{:}}=c_{i}\). The corresponding eigenvalues ?? are denoted as follows \[\begin{align} \label{Ivan11a} -\infty < \lambda_1^i \leq \lambda_2^i \leq \cdots \leq \lambda_{n_i-s_i}^i < \infty, \end{align}\qquad{(25)}\] and the corresponding eigenfunctions are \(\{p_m^i: m = 1, \ldots, n_i-s_i\} \subseteq \widetilde{V}_i\). The remaining eigenfunctions \(\{p_{n_i-s_i+1}^i, \ldots , p_{n_i}^i\}\) form a basis of \(\ker c_i\). The eigenfunctions satisfy the orthogonality properties corresponding to ?? and ?? . To apply Proposition 5, we need to verify Assumption 5, i.e. show that at least one eigenvalue in ?? is positive. A sufficient condition for this is given next.

Theorem 13 (Positivity of at least one eigenvalue). Consider the GEVP of Definition 3. For each \(i\in\{1,\ldots,N\}\), assume that there exists a set \(\mathcal{D}_i\) of \(s_i+1\) degrees of freedom in \(\mathop{\mathrm{dof}}(\Omega_i^c)\) such that \(\mathop{\mathrm{supp}}\phi_l^i\cap\mathop{\mathrm{supp}}\phi_{l'}^i\) has zero \(d\)-dimensional measure for each pair \(l,l' \in \mathcal{D}_i\) with \(l\ne l'\). Then there exists a constant \(C_{\mathrm{\textsf{pos}}}>0\) such that, if \((hk)^2 \le C_{\mathrm{\textsf{pos}}}\), then, for each \(i\in\{1,\ldots,N\}\), there is at least one index \(m_i\in\{1,\ldots,n_i-s_i\}\) such that \(\lambda_{m_i}^i > 0\).

Remark 14. The assumptions of Theorem 13 impose only a mild restriction. For example, consider a square subdomain discretised by a uniform grid \(n_{dof}\times n_{dof}\), then \(s_i \sim\) the number of boundary dofs \(\sim n_{dof}\), while the number of interior dofs is \(\sim n_{dof}^2\). For \(n_{dof}\) large enough, a set \(\mathcal{D}_i\) can be chosen on a subgrid of size \(\sim (n_{dof}/2)^2\).

Proof of Theorem 13. Let \(i\in\{1,\ldots,N\}\). Note that if \(l \in \mathop{\mathrm{dof}}(\Omega_i^c)\), then \(\phi_l^{{i}} \in V_i\). Denoting the dofs in the assumption by \(l_1,\ldots,l_{s_i+1}\) we use Corollary 1 to write \[\begin{align} \label{each95side} \phi_{{l_j}}^{{i}} = v_0^{i,{j}} + \sum_{m=1}^{n_i-s_i}\alpha_m^{i,{j}} p_m^i, \qquad j\in\{1,\ldots,s_i+1\}, \end{align}\tag{44}\] with \(v_0^{i,j}\in\ker c_i\) and \(\alpha_m^{i,j}\in\mathbb{R}\). Since \(s_i=\dim\ker c_i\), there exist \(\beta_1^i,\ldots,\beta_{s_i+1}^i\in\mathbb{R}\), not all vanishing, such that \(\sum_{j=1}^{s_i+1}\beta_j^i v_0^{i,j}=0\). Set \(\psi^i\mathrel{\mathop{:}}=\sum_{{j=1}}^{s_i+1}\beta_{{j}}^i\phi_{{l_j}}^i\). Then multiplying each side of 44 by \(\beta_j^i\) and summing over \(j\) we obtain \[\label{ml02} \psi^i = \sum_{m=1}^{n_i-s_i}\widetilde{\alpha}_m^i p_m^i \qquad \text{with} \;\; \widetilde{\alpha}_m^i = \sum_{j=1}^{s_i+1} \beta_j \alpha_m^{i,j}.\tag{45}\] Further, set \(d_l^i \mathrel{\mathop{:}}=\mathop{\mathrm{diam}}\mathop{\mathrm{supp}}\phi_l^i\). For \(j\in\{1,\ldots,s_i+1\}\), it follows from Friedrichs’ inequality ?? and the assumptions \(a_{\mathrm{\textsf{min}}}= n_{\mathrm{\textsf{max}}}= 1\) that \[\begin{align} b_{i}(\phi_{l_j}^{{i}},\phi_{l_j}^{{i}}) \ge\|\phi_{l_j}^i\|_{a,\Omega_i^c}^2 - k^2\|\phi_{l_j}^i\|_{\Omega_i^c}^2 \ge \Bigl(\frac{2}{(d_{l_j}^i)^2}-k^2\Bigr)\|\phi_{l_j}^i\|_{\Omega_i^c}^2 = \frac{1}{(h_{l_j}^i)^{2}}\biggl(2\Bigl(\frac{h_{l_j}^i}{d_{l_j}^i}\Bigr)^2-(kh_{l_j}^i )^2\biggr)\|\phi_l^i\|_{\Omega_i^c}^2. \label{ml01} \end{align}\tag{46}\] where \(h_{l_j}^i \mathrel{\mathop{:}}=\max\{ h_T: T \subseteq \mathop{\mathrm{supp}}\phi_{l_j}^i\}\). The assumed shape-regularity of the mesh implies that \({h_{l_j}^i/d_{l_j}^i}\) is bounded below by a positive constant, and we have \(k h_{l_j}^i \le kh\); so, for small enough \(hk\), the right-hand side of 46 is strictly positive for all \(j\in\{1,\ldots,s_i+1\}\). Since \(\mathop{\mathrm{supp}}\phi_{l_j}^i\cap\mathop{\mathrm{supp}}\phi_{l_{j'}}^i\) has measure zero for \(j\ne j'\), we also have \(b_{i}(\phi_{l_j}^i,\phi_{l_{j'}}^i)=0\) for \(j\ne j'\) and hence \(b_{i}(\psi^i,\psi^i) = \sum_{j=1}^{s_i+1} (\beta_j^i)^2 b_i(\phi_{l_j}^i,\phi^i_{l_j}) > 0\).

Now, if \(\lambda_m^i\le0\) for all \(m=1,\ldots,n_i-s_i\), then, by 45 and ?? , \[b_{i}(\psi^i,\psi^i) = \sum_{m=1}^{n_i-s_i}\lambda_m^i (\widetilde{\alpha}_m^i)^2 \le 0,\] which is a contradiction. ◻

Our next main result is Theorem 15, which gives an estimate from below for the minimum eigenvalue \(\lambda_1^i\) of ?? , needed in the proof of Theorem 16. To prepare for this, we choose a complement of \(\ker c_i\) as follows: \[\label{choice95of95complement} \widehat{V}_i \mathrel{\mathop{:}}=\mathrm{span} \bigl\{\phi_l^i: l \in \mathop{\mathrm{dof}}(\Omega_i^c)\bigr\} = V_{i} \subseteq H^1_0(\Omega_i^c),\tag{47}\] which is \((n_i-s_i)\)-dimensional and satisfies \(\widehat{V}_i\cap\ker c_i=\{0\}\) by ?? ; hence the decomposition \[\label{decomp} \widetilde{V}_i = \widehat{V}_i \oplus \ker c_i\tag{48}\] holds, and the assumptions of Lemma 3 are fulfilled.

Theorem 15 (Estimate of \(\lambda_1^i\) from below). Let \((hk)^2 \le C_1\) with \(C_1\) as in Lemma 5. Then \[\label{eigest2} \lambda_1^i \gtrsim -C_{\mathrm{\textsf{eig}}}\qquad \text{for all} \;\; i\in\{1,\ldots,N\}\qquad{(26)}\] with \[C_{\mathrm{\textsf{eig}}}= \Lambda_c^{2}n_{\mathrm{\textsf{min}}}^{-1}(a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+2)^2.\]

Proof.   We recall from equation 23 that \(\lambda_1^i\) is the minimum eigenvalue of the problem: find \(\lambda^i \in \mathbb{R}\) and \(\widehat{p}^i \in \widehat{V}_i\backslash \{0\}\) such that \[\label{Ivan3195old} \widetilde{b}_i (\widehat{p}^i, \widehat{v}^i) \mathrel{\mathop{:}}=b_i\bigl((I + \mathcal{S}_i ) \widehat{p}^i, \widehat{v}^i\bigr) = \lambda^i c_i(\widehat{p}^i, \widehat{v}^i) \qquad \text{for all} \;\; \widehat{v}^i\in \widehat{V}_i,\tag{49}\] where \(\mathcal{S}_i: \widehat{V}_i \rightarrow \ker c_i\) is the operator defined by \[\label{Ivan31aa} b_i(\mathcal{S}_i \widehat{v}^i, {w_{c_i}}) = - b_i(\widehat{v}^i, {w_{c_i}}) \qquad \text{for all} \;\; {w_{c_i}} \in \ker c_i.\tag{50}\] Using 50 and the fact that \(\mathcal{S}_i\) maps into \(\ker c_i\), we have that 49 is equivalent to \[\label{Ivan31} b_i\bigl((I + \mathcal{S}_i ) \widehat{p}^i, (I + \mathcal{S}_i) \widehat{v}^i\bigr) = \lambda^i c_i(\widehat{p}^i, \widehat{v}^i) \qquad \text{for all} \;\; \widehat{v}^i\in \widehat{V}_i.\tag{51}\] Since, by Lemma 3, \(c_i\) is positive definite on \(\widehat{V}_i\), we obtain from Rayleigh’s principle that \[\label{Rayleigh} \lambda_1^i = \min_{0 \ne {\widehat{v}^i} \in \widehat{V}_i}\frac{{b_i\bigl((I + \mathcal{S}_i ) \widehat{v}^i, (I + \mathcal{S}_i) \widehat{v}^i\bigr)}}{c_{i} (\widehat{v}^i,\widehat{v}^i)}.\tag{52}\] To estimate 52 from below, we first derive an estimate from above for \(\Vert \mathcal{S}_i\widehat{v}^i \Vert_{\Omega_i^c}\) for any \(\widehat{v}^i \in \widehat{V}_i\). Using ?? , the definition 50 of \(\mathcal{S}_i\), the fact that \(n_{\mathrm{\textsf{max}}}= 1\) and ?? , we have, for any \(\widehat{v}^i \in \widehat{V}_i\), \[\begin{align} C_1 h^{-2} \Vert \mathcal{S}_i \widehat{v}^i \Vert_{\Omega_i^c}^2 &\le b_i(\mathcal{S}_i \widehat{v}^i ,\mathcal{S}_i \widehat{v}^i ) = -b_i(\widehat{v}^i ,\mathcal{S}_i \widehat{v}^i ) \nonumber \\[0.5ex] &\le \Vert \nabla(\mathcal{S}_i \widehat{v}^i )\Vert_{\Omega_i^c} \Vert \nabla \widehat{v}^i \Vert_{\Omega_i^c} + k^2 \Vert \mathcal{S}_i \widehat{v}^i \Vert_{\Omega_i^c} \Vert \widehat{v}^i \Vert_{\Omega_i^c} \nonumber \\[0.5ex] & \le (a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+ 1)k^2 \Vert \mathcal{S}_i \widehat{v}^i \Vert_{\Omega_i^c} \Vert \widehat{v}^i \Vert_{\Omega_i^c}, \label{Ivan31a} \end{align}\tag{53}\] and thus \[\label{Ivan32} \|\mathcal{S}_i\widehat{v}^i\|_{\Omega_i^c} \le (a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+ 1) C_1^{-1} (hk)^2 \Vert \widehat{v}^i \Vert_{\Omega_i^c} \le (a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+ 1)\Vert \widehat{v}^i \Vert_{\Omega_i^c}.\tag{54}\] As a result, for the numerator in 52 , we have \[\label{Ivan34} b_i\bigl((I+\mathcal{S}_i)\widehat{v}^i , (I+\mathcal{S}_i)\widehat{v}^i\bigr) \ge -k^2 \|(I+\mathcal{S}_i)\widehat{v}^i\|_{\Omega_i^c}^2 \ge -k^2 (a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+ 2)^2 \|\widehat{v}^i\|_{\Omega_i^c}^2.\tag{55}\] For the denominator note that \(\widehat{V}_i=V_i\) by 47 ; hence Lemma 4 implies that \[c_i(\widehat{v}^i, \widehat{v}^i) \ge k^2 n_{\mathrm{\textsf{min}}}\Vert \Xi_i^c( \widehat{v}^i )\Vert_{\Omega_i^c}^2 \gtrsim k^2 n_{\mathrm{\textsf{min}}}\Lambda_c^{-2} \Vert \widehat{v}^i \Vert_{\Omega_i^c}^2. \label{Ivan35}\tag{56}\] For the rest of this proof (only) let \(\mathbf{min}\) denote the minimum of any quantity over all \(0 \ne {\widehat{w}^i} \in \widehat{V}_i\). Recall also that \(c_i\) is positive definite on \(\widehat{V}_i\). If \(\mathbf{min}\,\widetilde{b}_i(\widehat{w}^i,\widehat{w}^i)\ge0\), then \(\lambda_1^i\ge0\) and ?? is trivially true. Now, assume that \(\mathbf{min}\,\widetilde{b}_i(\widehat{w}^i,\widehat{w}^i) < 0\). Then, for all \(0 \ne \widehat{v}^i \in \widehat{V}_i\), \[\frac{\widetilde{b}_i(\widehat{v}^i, \widehat{v}^i)}{c_i(\widehat{v}^i, \widehat{v}^i)} \ge \frac{\mathbf{min}\,\widetilde{b}_i(\widehat{w}^i, \widehat{w}^i)}{c_i(\widehat{v}^i, \widehat{v}^i)} \ge \frac{\mathbf{min}\,\widetilde{b}_i(\widehat{w}^i, \widehat{w}^i)}{\mathbf{min}\,{c_i(\widehat{w}^i, \widehat{w}^i)}} = \frac{\mathbf{min}\,{b}_i((I + \mathcal{S}_i)\widehat{w}^i, (I + \mathcal{S}_i)\widehat{w}^i)}{\mathbf{min}\,{c_i(\widehat{w}^i, \widehat{w}^i)}},\] and so, by 55 , 56 , for all \(0 \ne \widehat{v}^i \in \widehat{V}_i\), \[\frac{ \widetilde{b}_i( \widehat{v}^i, \widehat{v}^i )}{c_i(\widehat{v}^i, \widehat{v}^i)} \gtrsim - \Lambda_c^{2}n_{\mathrm{\textsf{min}}}^{-1}(a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+2)^2,\] which proves ?? also in this case. ◻

The next theorem is the fundamental result describing the approximation power of the \(H_k\)-GenEO space. It constitutes a substantial generalisation of previous results in [13], [5] and [8] since here we study the indefinite GEVP [eq: 5_12].

Theorem 16. Suppose that \((kh)^2 \le C_1\) with \(C_1\) as in Lemma 5. Recalling Assumption 5, define the ‘local projector’ \(\Pi_{m_i}\) by \[\label{eq:proj} \Pi_{m_i} v \mathrel{\mathop{:}}=\sum_{m=1}^{m_i} c_i(v, p^i_m) p_m^i, \qquad v \in \widetilde{V}_i.\qquad{(27)}\] Let \(C_{\ast}\) be as in ?? . There exists a constant \(C_2>0\), which is independent of all key parameters, such that, for any \(v \in \widetilde{V}_i\) and \(w \mathrel{\mathop{:}}=v-\Pi_{m_i} v\), \[\begin{align} & 0 \le b_{i}(w,w) \le C_2^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Vert v \Vert_{1,k,\Omega_i^c}^2, \label{new951} \\[0.5ex] & c_{i}(w,w) \le (\lambda_{m_i+1}^i)^{-1} b_{i}(w,w). \label{new952} \end{align}\] {#eq: sublabel=eq:new951,eq:new952}

Proof.   The inequality in ?? follows directly from ?? of Proposition 5. Set \(\alpha_m^i \mathrel{\mathop{:}}=c_{i}(v,p_m^i)\). Then \(w = v_0 + \sum_{m = m_i+1}^{n_i-s_i} \alpha_m^i p_m^i\) with \(v_0 \in \mathop{\mathrm{span}}\{ p_{n_i-s_1+1}\ldots , p_{n_i}\} = \ker c_i\). Hence using property ?? , then Lemma 5 and the fact that \(\lambda_m^i > 0\) for all \(m \in \{m_i +1, \ldots n_i-s_i\}\), we obtain \[\begin{align} b_{i} (w,w) &= b_{i} (v_0,v_0) + 2 \sum_{m= m_i+1}^{n_i-s_i} \alpha_m^i b_{i} (p_m^i,v_0) + \sum_{m,m'= m_i+1}^{n_i-s_i} \alpha_m^i \alpha_{m'}^i b_{i} (p_m^i,p_{m'}^i) \\ &= b_{i} (v_0,v_0) + \sum_{m = m_i+1}^{n_i-s_i} (\alpha_m^i)^2 \lambda_m^i \ge 0, \end{align}\] i.e.the left-hand inequality in ?? . To obtain the right-hand inequality in ?? , note that \(b_{i}(w,\Pi_{m_i}v) = 0\), and hence \[\label{Lem341} b_{i}\!\left( w , w \right) = b_{i}(v,v) - b_{i}( \Pi_{m_i} v,\Pi_{m_i} v) = a_{\Omega_i^c}(v,v) - k^2 \|v\|_{n,\Omega_i^c}^2 - b_{i}( \Pi_{m_i} v, \Pi_{m_i} v).\tag{57}\] To estimate the last term in 57 we use ?? , ?? , ?? and then ?? to obtain \[\begin{align} -b_{i}\!\left( \Pi_{m_i} v, \Pi_{m_i} v \right) &= - \sum_{m=1}^{m_i} (\alpha_m^i)^2 \, \lambda_m^i \le -\lambda_1^i \sum_{m=1}^{m_i}(\alpha_m^i)^2 \nonumber\\ &\lesssim C_{\mathrm{\textsf{eig}}}\sum_{m=1}^{m_i} (\alpha_m^i)^2 \le C_{\mathrm{\textsf{eig}}}\sum_{m=1}^{n_i-s_i} (\alpha_m^i)^2 = C_{\mathrm{\textsf{eig}}}c_{i}(v,v). \label{Lem342} \end{align}\tag{58}\] Hence combining 57 and 58 we obtain \[\label{Lem343} b_i(w,w) \lesssim a_{\Omega_i^c}(v,v) + C_{\mathrm{\textsf{eig}}}\, c_i(v,v).\tag{59}\] To conclude, we now use ?? , the fact that \(a_{\mathrm{\textsf{max}}}\ge 1\) and \(C_{\mathrm{\textsf{inv}}}\ge 1\), and ?? to obtain \[\begin{align} c_i(v,v) &\le a_{\mathrm{\textsf{max}}}\|\nabla \Xi_i^c(v)\|_{\Omega_i^c}^2 + k^2 \|\Xi_i^c(v)\|_{\Omega_i^c}^2 \nonumber\\ &\le (a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+ 1)k^2 \Vert \Xi_i^c(v)\Vert_{\Omega_i^c}^2 \lesssim (a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}+ 1 ) k^2\Vert v \Vert_{\Omega_i^c}^2 \lesssim a_{\mathrm{\textsf{max}}}C^2_{\mathrm{\textsf{inv}}}k^2 \Vert v \Vert_{\Omega_i^c}^2, \label{hidden} \end{align}\tag{60}\] and the proof follows on combining 60 with 59 , using \(C_{\mathrm{\textsf{eig}}}\ge1\) and recalling Notation 1. ◻

From Theorem 16 it is now possible to build a global approximation property.

Lemma 6 (Global approximation property). Under the same conditions as in Theorem 16, define \(\Theta\) as in 40 and let \(C_{\ast}\) and \(C_2\) be as in ?? and Theorem 16 respectively. Further, let \(v \in V^h\) and set \[\label{def95z95095z95i} z_0 \mathrel{\mathop{:}}=\sum_{i=1}^N E_i^c \Xi^c_i \bigl(\Pi_{m_i} v|_{\Omega_i^c}\bigr).\qquad{(28)}\] Then, \(z_0 \in V_0\) and \[\label{inequ95v95z950} \inf_{z\in V_0}\|v-z\|_{1,k}^2 \le \|v-z_0\|_{1,k}^2 \le C_2^2\,\Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast} \Theta\|v\|^2_{1,k}.\qquad{(29)}\]

Proof.   Since \(\Pi_{m_i}^c v|_{\Omega_i^c}\in\mathop{\mathrm{span}}\{p_1^i,\ldots,p_{m_i}^i\}\), it is clear that \(z_0 \in V_0\), which also proves the first inequality in ?? . Using 38 , 31 , ?? and ?? we obtain \[\begin{align} \|v-z_0\|_{1,k}^2 &= \Bigg\| \sum_{i=1}^N E_i^c (\Xi_i^c (v|_{\Omega_i^c}))-\sum_{i=1}^N E^c_i \Xi^c_i \bigl(\Pi_{m_i} {v|_{\Omega_i^c}}\bigr) \Bigg\|_{1,k}^2 \\ &\le \Lambda_c \sum_{i=1}^N \left \|\Xi_i^c \left(v|_{\Omega_i^c}- \Pi_{m_i} {v|_{\Omega_i^c}} \right) \right\|_{1,k,{\Omega_i^c}}^2 \nonumber = \Lambda_c \sum_{i=1}^N c_{i}\Bigl( \bigl({v|_{\Omega_i^c}}-\Pi_{m_i}{v|_{\Omega_i^c}}\bigr), \bigl({v|_{\Omega_i^c}}-\Pi_{m_i}{v|_{\Omega_i^c}}\bigr)\Bigr) \nonumber \\ &\le \Lambda_c \sum_{i=1}^N \frac{1}{\lambda_{m_i+1}^i} b_{i}\Bigl(\bigl({v|_{\Omega_i^c}}-\Pi_{m_i}{v|_{\Omega_i^c}}\bigr),\bigl({v|_{\Omega_i^c}}-\Pi_{m_i}{v|_{\Omega_i^c}}\bigr)\Bigr) \nonumber \\ &\le C_2^2 \Lambda_c a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1}C_{\ast}\sum_{i=1}^N\frac{1}{\lambda_{m_i+1}^i}\big\|v|_{\Omega_i^c}\big\|_{1,k,\Omega_i^c}^2 \le C_2^2 \Lambda_c a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1}C_{\ast}\Theta\sum_{i=1}^N\big\|v|_{\Omega_i^c}\big\|_{1,k,\Omega_i^c}^2, \label{proof95inequ95sum95z95i} \end{align}\tag{61}\] and the result follows from 28 . ◻

The next lemma shows that the spaces \(V_0\) and \(V_{\Omega_j^\ell}\) provide a stable decomposition of \(V^h\).

Lemma 7 (Stable decomposition). Under the same conditions as in Theorem 16, let \(v \in V^h\) and define \(z_0 \in V_0\) as in ?? . Then there exists a constant \(C_3\ge1\), which is independent of all parameters, such that, for each \(j = 1,\ldots,Q\), there exist \(z_j \in V_{\Omega_j^\ell}\) so that \[v = z_0 + \sum_{j=1}^Q E_j^\ell z_j^\ell \quad \text{ and } \quad \|z_0\|_{1,k}^2 + \sum_{j=1}^Q \|z_j^\ell\|_{1,k,\Omega^\ell_j}^2 \le C_3 \bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr) \| v \|_{1,k}^2.\]

Proof.   We choose a partition of unity consisting of real-valued Lipschitz functions \(\{\Phi_j^{\ell}\}_{j=1}^Q\) on \(\Omega\) satisfying \(0 \le \Phi_j^{\ell} \le 1\), \(\mathop{\mathrm{supp}}(\Phi_j^{\ell}) \subseteq \overline{\Omega_j^\ell}\) for each \(j\), and \(\sum_{j=1}^Q \Phi_j^{\ell} \equiv 1\) on \(\Omega\); see, e.g.[16]. Then \(\|\nabla \Phi_j^{\ell}\|_{L^\infty(\Omega)} \le C_{\mathrm{\textsf{POU}}}\delta_\ell^{-1}\) for all \(j\), where \(\delta_\ell\) is the overlap parameter from Assumption 4. By [32] and then Assumption 4, for any \(v \in H^1_0(\Omega)\), \[\label{multPOU} \sum_{j=1}^Q \| \Phi_j^{\ell} v \|_{1,k,\Omega_j^\ell}^2 \lesssim \Lambda_\ell \left(1 + \frac{1}{(k{\delta_\ell})^2} \right) \|v\|_{1,k,\Omega}^2 \lesssim \Lambda_\ell \|v\|_{1,k,\Omega}^2,\tag{62}\]

Now define \(z_j^\ell \mathrel{\mathop{:}}=\Phi_j^\ell(v - z_0) \in V_j^\ell\). Then \[\sum_{j=1}^Q E_j^\ell z_j^\ell = \sum_{j=1}^Q E_j^\ell \Phi_j^\ell (v - z_0) = (v - z_0)\sum_{j=1}^Q {\Phi_j^\ell} = v - z_0.\] Moreover, from Lemma 6 we have \[\|z_0\|_{1,k}^2 \le 2\left(\|z_0 - v\|_{1,k}^2 + \|v\|_{1,k}^2\right) \lesssim (1 + \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta) \|v\|_{1,k}^2.\] Also, using 62 , we obtain \[\sum_{j=1}^Q \|z_j^\ell\|_{1,k,\Omega_j^\ell}^2 = \sum_{j=1}^Q \| \Phi_j^\ell (v - z_0)\|_{1,k,\Omega_j^\ell}^2 \lesssim \Lambda_\ell \|v - z_0\|_{1,k}^2.\] Combining this with Lemma 6 and the bound for \(z_0\) we obtain the result. ◻

It is convenient to introduce here the orthogonal projections onto the local spaces \({P_j^\ell}: V^h \rightarrow V^\ell_j\), with respect to the inner product \((\cdot, \cdot)_{1,k}\), by requiring that \[(P_j^\ell v, w)_{1,k,\Omega^\ell_j} = (v, E_j^\ell w)_{1,k,\Omega} \qquad \text{for all} \;\; v \in V^h, \;\; w \in V_{\Omega_j^\ell}, \quad \text{and} \;\; j = 1, \ldots, Q \label{eq:323951095nested}.\tag{63}\] Similarly, we define the orthogonal projection \(P_0 : V^h \rightarrow V_0\) by requiring \[(P_0 v, w)_{1, k} = (v, w)_{1,k} =(v, E_0w)_{1,k} \qquad \text{for all} \;\; v \in V^h, \;\; w \in V_0. \label{eq:323951095zero}\tag{64}\] Using these we then define the operator \(P: V^h \rightarrow V^h\) by \[P \mathrel{\mathop{:}}=E_0P_0 + \sum_{j=1}^{Q} {E_j^\ell} {P_j^\ell}. \label{eq:3239511}\tag{65}\] The operator \(P\) is an (easier-to-analyse) proxy for the operator \(T\) (which represents our preconditioned matrix — see 37 and Proposition 7). Here, we estimate the field of values of \(P\), and then use this to estimate the field of values of \(T\), leading to our main result (Theorem 10).

Proposition 17 (Field of values of \(P\)). Under the same assumptions as in Theorem 16, let \(C_3\) be as in Lemma 7. Then \[\label{eq:3239512} \|v\|^2_{1,k} \le C_3\bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr) (Pv,v)_{1,k}\qquad{(30)}\] for every \(v\in V^h\).

Proof.   Using Lemma 7, the definitions of \(P_j^\ell\) and \(P_0\) and then Cauchy–Schwarz, we get \[\begin{align} \|v\|_{1,k}^2 &= \Biggl(v, z_0 + \sum_{j=1}^{Q} {E_j^\ell} {z_j^\ell}\Biggr)_{1,k} = (v, z_0)_{1,k} + \Biggl(v,\sum_{j=1}^{Q} {E_j^\ell} {z_j^\ell}\Biggr)_{1,k} \\ &= (P_0v, z_0)_{1,k} + \sum_{j=1}^{Q}( {P_j^\ell} v, {z_j^\ell})_{1,k, {\Omega^\ell_j}} \le \| P_0v\|_{1,k} \|z_0\|_{1,k} + \sum_{j=1}^{Q}\| {P_j^\ell} v \|_{1,k, {\Omega^\ell_j}} \|{z_j^\ell}\|_{1,k, {\Omega^\ell_j}}{.} \end{align}\] We now apply Cauchy–Schwarz and Lemma 7 again to obtain \[\begin{align} \|v\|_{1,k}^2 &\le \left(\| P_0v\|_{1,k}^2 + \sum_{j=1}^{Q}\| {P_j^\ell} v \|_{1,k, {\Omega^\ell_j}}^2 \right)^{1/2} C_3^{1/2}\bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)^{1/2} \|v\|_{1,k} \\[1ex] &= \left(( E_0 P_0v, v)_{1,k} + \sum_{j=1}^{Q} ( {E_j^\ell} {P_j^\ell} v, v)_{1,k} \right)^{1/2} C_3^{1/2}\bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)^{1/2}\|v\|_{1,k} \\[1ex] &= \left( E_0 P_0 v + \sum_{j=1}^{Q}{E_j^\ell} {P_j^\ell} v, v \right)_{1,k}^{1/2} C_3^{1/2}\bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)^{1/2} \|v\|_{1,k} \\ &= (P v,v)_{1,k}^{1/2} \, C_3^{1/2}\bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)^{1/2}\|v\|_{1,k}, \end{align}\] and the inequality ?? follows. ◻

We now study the fundamental properties of the operators \(T_j^\ell\) and \(T_0\).

3.2 Existence and stability of \({T_j^\ell}\), \(j=1,\ldots,Q\)↩︎

Lemma 8 (\(T_j^\ell\) are well defined). If \({H_\ell} k < \sqrt{2}\), then, for each \(j=1, \ldots, Q\), \(b_{{\Omega^\ell_j}}(\cdot,\cdot)\) is positive definite on \(V_{\Omega_j^\ell}\) and the operators \({T_j^\ell}\) in 35 are well defined.

Proof. Using Friedrichs’ inequality ?? and the assumption that \(a_{\mathrm{\textsf{min}}}= 1 = n_{\mathrm{\textsf{max}}}\), we obtain, for \(u\in V_{\Omega_j^\ell}\) \[b_{{\Omega^\ell_j}}(u,u) = a_{{\Omega^\ell_j}}(u,u) - k^2({n}u,u)_{{\Omega^\ell_j}} \ge \frac{2}{{{(H_\ell)}}^2}\|u\|_{{\Omega^\ell_j}}^2 - k^2\|u\|_{{\Omega^\ell_j}}^2 = \frac{2-{{(H_\ell)}}^2k^2}{{{(H_\ell)}}^2}\|u\|_{{\Omega^\ell_j}}^2,\] and hence \(b_{{\Omega^\ell_j}}(\cdot,\cdot)\) is positive definite when \(H_\ell k < \sqrt{2}\). The square system 35 defining \(T_j^\ell v\) is then positive definite and the result follows. ◻

Remark 18. Whilst this is a sufficient condition for the \({T_j^\ell}\) to be well defined, it is far from necessary since the \({T_j^\ell}\) will be well defined when the linear system corresponding to 35 is non-singular. However, it seems hard to guarantee good stability properties for \(T_j^\ell\) without the above condition.

Lemma 9 (Stability of \({T_j^\ell}\), \(j=1,\ldots,Q\)). Suppose that \(\sqrt{2} {H_\ell}k\le 1\). Then \[\label{eq:3239531} \|{T_j^\ell} v\|_{1,k,{\Omega^\ell_j}} \le 2\big\|v|_{{\Omega^\ell_j}}\big\|_{1,k,{\Omega^\ell_j}} \qquad \text{for all} \;\; v \in V^h.\qquad{(31)}\]

Proof.   Using 35 , 10 , the fact that \(n_{\mathrm{\textsf{max}}}= 1 = a_{\mathrm{\textsf{min}}}\), and Friedrichs’ inequality ?? , we obtain \[\begin{align} \|{T_j^\ell} v\|^2_{1,k,{\Omega^\ell_j}} &= b_{{\Omega^\ell_j}}({T_j^\ell} v,{T_j^\ell} v) + 2k^2\|T_j^\ell v\|_{n,\Omega_i^\ell}^2 = b_{\Omega_i^\ell}\bigl(v|_{\Omega^\ell_j},{T_j^\ell} v\bigr) + 2k^2\|T_j^\ell v\|_{n,\Omega^\ell_j}^2 \\ &\le \big\|v|_{\Omega^\ell_j}\big\|_{1,k,\Omega^\ell_j}\|T_j^\ell v\|_{1,k,\Omega^\ell_j} + 2k^2\|T_j^\ell v\|_{\Omega^\ell_j}^2 \\ &\le \big\|v|_{\Omega^\ell_j}\big\|_{1,k,\Omega^\ell_j}\|{T_j^\ell} v\|_{1,k,\Omega^\ell_j} + k^2(H_\ell)^2\|T_j^\ell v\|^2_{1,k,\Omega^\ell_j}, \end{align}\] which implies \(\bigl(1-k^2 (H_\ell)^2\bigr)\|T_j^\ell v\|_{1,k,\Omega^\ell_j} \le \big\|v|_{\Omega^\ell_j}\big\|_{1,k,\Omega^\ell_j}\), and the result follows. ◻

3.3 Existence and stability of \(T_0\)↩︎

Lemma 10 (\(T_0\) is well-defined). Let \(C_2>0\) be as in Theorem 16. If \[\label{395795a} C_2 \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k\Theta^{1/2} (1 + C_{\mathrm{\textsf{stab}}}) < \frac{1}{\sqrt{2}\,},\qquad{(32)}\] then there exists \(h_1 > 0\) such that, for all \(h\in(0,h_1)\), the operator \(T_0\) is well defined.

Proof.   Let \(C\) and \(\Theta\) be as in Lemma 1. Then there exists \(h_1>0\) such that \[C\,\Theta\Bigl(\frac{h}{r}\Bigr) \le \min\biggl\{\frac{1}{2},\frac{k_0n_{\mathrm{\textsf{min}}}^{1/2}}{2}\biggr\} \qquad \text{for all} \;\; h\in(0,h_1).\] Let \(h\in(0,h_1)\) and assume (for a contradiction) that there exists a \(w_0 \in V_0\backslash \lbrace 0 \rbrace {\subseteq V^h}\) such that \[\label{eq:w95095sol} b(w_0,z) = 0 \qquad \text{for all} \;\; z \in V_0.\tag{66}\] Let \(w \in H^1_0(\Omega)\) be the solution of \(b(w,v)=(w_0,v)\) for all \(v \in H^1_0(\Omega)\), which exists and is unique by Assumption 2. It follows from Lemma 1 that \(\beta\le\min\bigl\{1/2,k_0n_{\mathrm{\textsf{min}}}^{1/2}/2\bigr\}<1/\sqrt{2}\) (where \(\beta\) is defined as in Lemma 1) and there exists \(w_h \in V^h\) such that \[b(w_h, v) = (w_0,v) \quad \text{for all} \;\; v \in V^h, \quad \text{and} \quad \|w - w_h\|_{1,k} \le \frac{2 \beta}{k n_{\mathrm{\textsf{min}}}^{1/2}} \|w_0\| \le \|w_0\|. \label{ScottWang}\tag{67}\] Let \(z\in V_0\) be arbitrary. Inserting \(v=w_0\) in 67 and combining with 66 and then 10 , we obtain \[\|w_0\|^2 = b(w_h, w_0) = b(w_h - z, w_0) \le \|w_h - z\|_{1,k} \|w_0\|_{1,k} \qquad\text{for all} \;\; z \in V_0.\] Since this is true for all \(z \in V_0\), Lemma 6 yields \[\label{eq:32395595f95sq} \|w_0\|^2 \le \inf_{z \in V_0}\|w_h-z\|_{1,k} \|w_0\|_{1,k} \le C_2\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} \Theta^{\frac{1}{2}}\|w_h\|_{1,k} \, \|w_0\|_{1,k}.\tag{68}\] Also, equation 66 with \(z=w_0\) shows that \(0 = b(w_0,w_0) = \|w_0\|_{1,k}^2-2k^2\|w_0\|_n^2\), which, together with 68 , implies \[\begin{align} \|w_0\|^2 &\le C_2 \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} \Theta^{\frac{1}{2}} \|w_h\|_{1,k}\sqrt{2}\, k \|w_0\|_n \\ &\le \sqrt{2}\, C_2 \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{\frac{1}{2}} \|w_h\|_{1,k} \|w_0\|, \end{align}\] and hence \[\label{eq:323955951} \|w_0\| \le \sqrt{2}\, C_2 \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{\frac{1}{2}} \|w_h\|_{1,k}.\tag{69}\] Then we use the triangle inequality, 67 and ?? to obtain \[\|w_h\|_{1,k} \le \|w-w_h\|_{1,k} + \|w\|_{1,k} \le (1 + C_{\mathrm{\textsf{stab}}})\|w_0\|. \label{eq:32w95h95comb}\tag{70}\] With 70 used in 69 , we arrive at \[\|w_0\| \le \sqrt{2}\, C_2 \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{\frac{1}{2}} (1+C_{\mathrm{\textsf{stab}}})\|w_0\|.\] Together with ?? this leads to a contradiction. ◻

Lemma 11 (Stability of \(T_0\)). With \(C_2\) as in Theorem 16, assume that ?? is satisfied. Then there exists \(h_1>0\) such that, for \(h\in(0,h_1)\), \[\|T_0 u - u\| \le {C_2}\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} \Theta^{1/2} (1 + C_{\mathrm{\textsf{stab}}})\|T_0 u - u\|_{1,k} \qquad \text{for all} \;u \in V^h. \label{395795b}\qquad{(33)}\] Suppose, in addition, that \[\label{395795c} C_2\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{1/2} (1 + C_{\mathrm{\textsf{stab}}}) \le \frac{1}{4};\qquad{(34)}\] then \[\label{395795d} \|u - T_0 u\|_{1,k} \le 2 \|u\|_{1,k} \qquad \text{for all} \;u \in V^h.\qquad{(35)}\]

Proof.   Let \(h_1>0\) be as in Lemma 10. Further, let \(u \in V^h\) and consider the auxiliary problem: \[\label{eq323957954} \text{find } w_h \in V^h \;\; \text{such that} \quad b(w_h, v) = \bigl(T_0 u - u,v\bigr) \quad \text{for all} \;\; v \in V^h.\tag{71}\] Analogously to 70 , problem 71 has a unique solution \(w_h\) for \(h\in(0,h_1)\), and \[\label{estwh} \|w_h\|_{1,k} \le (1+C_{\mathrm{\textsf{stab}}}) \|T_0 u - u\|.\tag{72}\] Now, by the definition 64 of \(T_0\), we have \(b(T_0 u-u,z)=0\) for all \(z \in V_0\). Inserting \(v = T_0 u - u \in V^h\) into 71 and using 10 , we obtain, for every \(z\in V_0\), \[\|T_0 u - u\|^2 = b(w_h, T_0 u - u) = b(w_h - z, T_0 u - u) \le \|w_h-z\|_{1,k}\,\|T_0 u - u\|_{1,k}.\] Combining this with Lemma 6 we obtain \[\label{ineq:T0u-u} \|T_0 u - u\|^2 \le \|T_0 u - u\|_{1,k}\inf_{z \in V_0}\|w_h-z\|_{1,k} \le \|T_0 u - u\|_{1,k} {C_2}\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} \Theta^{1/2} \|w_h\|_{1,k}.\tag{73}\] Together with 72 , this proves ?? .

Let us now show ?? . Inserting \(P_0 u - T_0 u \in V_0\) in the definition 36 of \(T_0\) we obtain \[b(u-T_0 u, P_0 u - T_0 u) = 0.\] Using this together with the link between the bilinear forms \((\cdot,\cdot)_{1,k}\) and \(b\), and the Cauchy–Schwarz inequality we obtain \[\begin{align} \|u - T_0 u\|_{1,k}^2 &= (u - T_0 u, u - T_0 u)_{1,k} = b(u - T_0 u, u - T_0 u) + 2k^2(u - T_0 u, u - T_0 u)_n \\[0.5ex] &= b(u - T_0 u, u - T_0 u) - b(u - T_0 u, P_0 u - T_0 u) + 2 k^2 (u - T_0 u, u - T_0 u)_n \\[0.5ex] &= b(u - T_0 u, u - P_0 u) + 2k^2(u - T_0 u, u - T_0 u)_n \\[0.5ex] &= (u - T_0 u, u - P_0 u)_{1,k} - 2k^2(u - T_0 u, u - P_0 u)_n + 2k^2(u - T_0 u, u - T_0 u)_n \\[0.5ex] &= (u - T_0 u, u - P_0 u)_{1,k} + 2k^2(u - T_0 u, P_0 u - T_0 u)_n \\[0.5ex] &\le \|u - T_0 u\|_{1,k}\|u - P_0 u\|_{1,k} + 2k^2\|u - T_0 u\|_n \|T_0 u - P_0 u\|_n \\[0.5ex] &\le \|u - T_0 u\|_{1,k}\|u - P_0 u\|_{1,k} + 2k\|u - T_0 u\|_n \|T_0 u - P_0 u\|_{1,k} \\[0.5ex] &= \|u - T_0 u\|_{1,k}\|u - P_0 u\|_{1,k} + 2k\|u - T_0 u\|_n \|P_0(T_0 u - u)\|_{1,k} \\[0.5ex] &\le \|u - T_0 u\|_{1,k}\|u\|_{1,k} + 2k\|u - T_0 u\|_n \|T_0 u - u\|_{1,k}, \end{align}\] where in the last step we used the fact that \(P_0\) is the orthogonal projection onto \(V_0\) with respect to \((\cdot,\cdot)_{1,k}\). Dividing both sides by \(\|u - T_0 u\|_{1,k}\) and then using ?? , \(n_{\mathrm{\textsf{max}}}= 1\) and the assumption ?? , we arrive at \[\begin{align} \|u - T_0 u\|_{1,k} &\le \|u\|_{1,k} + 2k\|u - T_0 u\|_n \le \|u\|_{1,k} + 2{C_2}\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{1/2} (1 + C_{\mathrm{\textsf{stab}}})\|u - T_0 u\|_{1,k} \\[0.5ex] &\le \|u\|_{1,k} + \frac{1}{2}\|u - T_0 u\|_{1,k}, \end{align}\] which proves ?? . ◻

4 Proof of the main result↩︎

Before Theorem 10 can be proved, it is necessary to state and prove the following key lemma..

Lemma 12 (Core estimates for the main result). Under the assumptions of Theorem 10, let \[c_1 \mathrel{\mathop{:}}= \frac{1-s}{C_3\bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}{n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}} \Theta \bigr)}, \qquad c_2 \mathrel{\mathop{:}}=18 + 8 {\Lambda_{\ell}}^2,\] where \(s\) is given by ?? . Then, for all \(u \in V^h\), \[c_1\|u\|_{1,k}^2 \le (Tu,u)_{1,k}, \label{eq:324954}\qquad{(36)}\] and \[\|Tu\|_{1,k}^2 \le c_2 \|u\|_{1,k}^2. \label{eq:324955}\qquad{(37)}\]

Proof.   Let \(u \in V^h\). We proceed in several steps.
Step 1 (Relation between \((Tu,u)_{1,k}\) and \((Pu,u)_{1,k}\)). Using 65 and the definition of \(b\), we obtain \[\begin{align} (Pu,u)_{1,k} & {= (u, Pu)_{1,k}} = \biggl(u,E_0 P_0u + \sum^{Q}_{j=1} E^{\ell}_j P^{\ell}_j u\biggr)_{1,k} \\ &= b(u,E_0 P_0u) + 2k^2(u,E_0 P_0u)_n + \sum^{Q}_{j=1} \left[ b\bigl(u, E^{\ell}_j P^{\ell}_j u\bigr) + 2k^2\bigl(u,E^{\ell}_j P^{\ell}_j u\bigr)_n \right]. \end{align}\] Then, using 35 and 36 , we obtain \[\begin{align} (Pu,u)_{1,k} &= b(T_0 u,P_0u) + 2k^2(u,E_0 P_0u)_n + \sum^{Q}_{j=1} \left[ b_{\Omega^{\ell}_j}\bigl(T^{\ell}_j u, P^{\ell}_j u\bigr) + 2k^2\bigl(u,E^{\ell}_j P^{\ell}_j u\bigr)_n \right] \\[0.5ex] &= (T_0 u,P_0u)_{1,k} - 2k^2(T_0 u,P_0u)_n + 2k^2(u,E_0 P_0u)_n \\[0.5ex] &\quad + \sum^{Q}_{j=1} \left[ \bigl(T^{\ell}_j u, P^{\ell}_j u\bigr)_{1,k,\Omega^{\ell}_j} - 2k^2\bigl(T^{\ell}_j u, {P^{\ell}_j} u\bigr)_{n,\Omega^{\ell}_j} + 2k^2\bigl(u,E^{\ell}_j P^{\ell}_j u\bigr)_n \right]. \end{align}\] Now using the definitions 63 and 64 of \(P_j^\ell\) and \(P_0\), we have \[\begin{align} (Pu,u)_{1,k} &= (E_0 T_0 u,u)_{1,k} - 2k^2(E_0 T_0 u,E_0 P_0u)_n + 2k^2(u,E_0 P_0u)_n \\[0.5ex] &\quad + \sum^{Q}_{j=1} \left[ (E^{\ell}_j T^{\ell}_j u, u)_{1,k} - 2k^2\bigl(E^{\ell}_j T^{\ell}_j u, E^{\ell}_j P^{\ell}_j u\bigr)_n + 2k^2\bigl(u,E^{\ell}_j P^{\ell}_j u\bigr)_n \right]. \end{align}\] We now rearrange the right-hand side to obtain \[\begin{align} (Pu,u)_{1,k} &= (E_0 T_0 u,u)_{1,k} + \sum^{Q}_{j=1} \bigl(E^{\ell}_j T^{\ell}_j u, u\bigr)_{1,k} - 2k^2 \biggl(\bigl(E_0 T_0 u - u,E_0 P_0u\bigr)_n + \sum^{Q}_{j=1} \bigl(E^{\ell}_j T^{\ell}_j u - u, E^{\ell}_j P^{\ell}_j u\bigr)_n \biggr) \nonumber\\[0.5ex] &= (T u, u)_{1,k} - R, \label{relation} \end{align}\tag{74}\] with \(T\) defined in 37 and \[\begin{align} \label{defR} R \mathrel{\mathop{:}}=2k^2 \biggl( \bigl(E_0 T_0 u - u, E_0 P_0u\bigr)_n + \sum^{Q}_{j=1} \bigl(E^{\ell}_j T^{\ell}_j u - u, E^{\ell}_j P^{\ell}_j u\bigr)_n \biggr). \end{align}\tag{75}\] Step 2 (Bounding the first term in \(R\)). Recalling that \(E_0 w_0 = w_0\) for all \(w_0\in V_0\), and noting that the assumption in ?? implies that ?? is satisfied, we can use ?? , ?? and the fact that \(P_0\) is the orthogonal projection onto \(V_0\) with respect to \((\cdot, \cdot)_{1,k}\), to obtain \[\begin{align} k^2\bigl(E_0 T_0 u - u, E_0 P_0 u\bigr)_n &\le k^2 \|E_0 T_0 u - u\|_n \|E_0 P_0 u\|_n = k^2\| T_0 u -u\|_n \| P_0 u\|_n \nonumber\\[0.5ex] &\le k\|T_0 u -u\|\, \|P_0 u\|_{1,k} \nonumber\\[0.5ex] &\le C_2\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{1/2} (1+C_{\mathrm{\textsf{stab}}})\|T_0 u - u\|_{1,k}\|P_0 u\|_{1,k} \nonumber\\[0.5ex] &\le 2C_2\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{1/2} (1+C_{\mathrm{\textsf{stab}}})\|u\|_{1,k}\|P_0 u\|_{1,k} \nonumber\\[0.5ex] &\le 2C_2\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{1/2} (1+C_{\mathrm{\textsf{stab}}})\|u\|_{1,k}^2. \label{eq:10950} \end{align}\tag{76}\] Step 3 (Bounding the second term in \(R\)). For each \(j\), we use Friedrichs’ inequality ?? to arrive at \[\begin{align} k^2\bigl(E^{\ell}_j T^{\ell}_j u-u,E^{\ell}_j P^{\ell}_j u\bigr)_n &= k^2\bigl({T^{\ell}_j} u-u|_{\Omega^{\ell}_j}, P^{\ell}_j u\bigr)_{n,\Omega^{\ell}_j} \le k^2 \big\|T^{\ell}_j u-u|_{\Omega^{\ell}_j}\big\|_{n,\Omega^{\ell}_j}\big\|P^{\ell}_j u\big\|_{n,\Omega^{\ell}_j} \nonumber\\[0.5ex] &\le k\bigl\|T^{\ell}_j u-u|_{\Omega^{\ell}_j}\big\|_{1,k,\Omega^{\ell}_j}\big\|{P^{\ell}_j} u\big\|_{n,\Omega^{\ell}_j} \le k\bigl\|{T^{\ell}_j} u-u|_{\Omega^{\ell}_j}\big\|_{1,k,\Omega^{\ell}_j} \frac{H_{\ell}}{\sqrt{2}\,}\big\|{P^{\ell}_j} u\big\|_{1,k,\Omega^{\ell}_j} \nonumber\\[0.5ex] &\le \frac{k H_{\ell}}{\sqrt{2}\,} \Bigl(\|{T^{\ell}_j} u\|_{1,k,\Omega^{\ell}_j}+\big\|u|_{\Omega^{\ell}_j}\big\|_{1,k,{\Omega^{\ell}_j}}\Bigr) \big\|P^{\ell}_j u\big\|_{1,k,\Omega^{\ell}_j}. \label{lemma411} \end{align}\tag{77}\] Now, note that the hypothesis ?? implies that \(\sqrt{2} k {H_{\ell}} < \sqrt{2}/(3 \Lambda_\ell) < 1\); so we can apply Lemma 9 to obtain from 77 that \[k^2\bigl(E^{\ell}_j T^{\ell}_j u-u, E^{\ell}_j P^{\ell}_j u\bigr)_n \le \frac{3kH_{\ell}}{\sqrt{2}\,}\big\|u|_{\Omega^{\ell}_j}\big\|_{1,k,\Omega^{\ell}_j}\big\|P^{\ell}_j u\big\|_{1,k,\Omega^{\ell}_j} < 3kH_{\ell}\big\|u|_{\Omega^{\ell}_j}\big\|_{1,k,\Omega^{\ell}_j}\big\|P^{\ell}_j u\big\|_{1,k,\Omega^{\ell}_j}.\] Summing over \(j\), applying Cauchy–Schwarz and 28 , we obtain \[\begin{align} & k^2 \sum_{j=1}^{Q} \bigl(E^{\ell}_j T^{\ell}_j u-u, E^{\ell}_j P^{\ell}_j u\bigr)_n \le 3kH_{\ell} \sum_{j=1}^{Q} \big\|u|_{\Omega^{\ell}_j}\big\|_{1,k,\Omega^{\ell}_j}\|P^{\ell}_j u\|_{1,k,\Omega^{\ell}_j} \nonumber\\[0.5ex] &\le 3kH_{\ell} \Biggl(\sum_{j=1}^{Q} \big\|u|_{\Omega^{\ell}_j}\big\|_{1,k,\Omega^{\ell}_j}^2\Biggr)^{1/2} \Biggl( \sum_{j=1}^{Q} \|P^{\ell}_j u\|_{1,k,\Omega^{\ell}_j}^2\Biggr)^{1/2} \le 3kH_{\ell} \Lambda_{\ell}^{1/2} \|u\|_{1,k}\Biggl( \sum_{j=1}^{Q} \|P^{\ell}_j u\|_{1,k,\Omega^{\ell}_j}^2\Biggr)^{1/2}. \label{eq:10951} \end{align}\tag{78}\] To estimate the sum on the right-hand side of 78 , note that \[\|E_j^\ell P_j^\ell u \|_{1,k}^2 = (P_j^\ell u,P_j^\ell u)_{1,k,\Omega_j^\ell} = (P_j^\ell u, u)_{1,k,\Omega_j^\ell} = (E_j^\ell P_j^\ell u, u)_{1,k},\] and so, using 30 , we have \[\begin{align} \Bigg\|\sum_{j=1}^{Q} E^{\ell}_j P^{\ell}_j u\Bigg\|_{1,k}^2 &\le \Lambda_{\ell} \sum_{j=1}^{Q} \|E^{\ell}_j P^{\ell}_j u\|_{1,k}^2 = \Lambda_{\ell} \sum_{j=1}^{Q} (E^{\ell}_j P^{\ell}_j u,u)_{1,k} \\[0.5ex] &= \Lambda_{\ell} \Biggl(\sum_{j=1}^{Q} E^{\ell}_j P^{\ell}_j u,\,u\Biggr)_{1,k} \le \Lambda_{\ell} \,\Bigg\|\sum_{j=1}^{Q} E^{\ell}_j P^{\ell}_j u\Bigg\|_{1,k} \|u\|_{1,k}. \end{align}\] Thus \[\begin{align} \sum_{j=1}^{Q} \|P^{\ell}_j u\|_{1,k,\Omega^{\ell}_j}^2 &= \sum_{j=1}^{Q} (E^{\ell}_j P^{\ell}_j u,u)_{1,k} = \Biggl(\sum_{j=1}^{Q} E^{\ell}_j P^{\ell}_j u,\,u\Biggr)_{1,k} \le \Bigg\|\sum_{j=1}^{Q} E^{\ell}_j P^{\ell}_j u\Bigg\|_{1,k}\|u\|_{1,k} \le \Lambda_{\ell} \|u\|_{1,k}^2. \end{align}\] This, together with 78 , leads to \[\label{eq:10952} k^2 \sum_{j=1}^{Q} \bigl(E^{\ell}_j T^{\ell}_j u-u, E^{\ell}_j P^{\ell}_j u\bigr)_n \le 3kH_{\ell} \Lambda_{\ell} \|u\|_{1,k}^2.\tag{79}\] Step 4 (Obtaining ?? ). We can now combine 75 , 76 and 79 to obtain \[\begin{align} |R| &\le 2 \Bigl(2 C_2\, \Lambda_c (a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast})^{1/2} k \Theta^{1/2} (1+C_{\mathrm{\textsf{stab}}}) + 3 k \Lambda_\ell H_\ell\Bigr) \|u\|_{1,k}^2 \nonumber\\[0.5ex] &= \frac{s}{C_3 \bigl(1+\Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)} \|u\|_{1,k}^2, \end{align}\] with \(s\) and \(C_3\) as defined in Theorem 10. Hence, using this, 74 and Proposition 17, we obtain \[\begin{align} (Tu,u)_{1,k} &= (Pu,u)_{1,k} + R \ge C_3^{-1} \bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)^{-1} \|u\|_{1,k}^2 - |R| \nonumber\\[0.5ex] &\ge C_3^{-1} \bigl(1 + \Lambda_\ell \Lambda_c^2 a_{\mathrm{\textsf{max}}}n_{\mathrm{\textsf{min}}}^{-1} C_{\ast}\Theta \bigr)^{-1} (1-s) \|u\|_{1,k}^2, \end{align}\] as required.
Step 5 (Obtaining ?? ). Since \(E_0T_0 u = T_0u\), we have \[\label{eq:3249518} \|Tu\|_{1,k}^2 = \Bigg\|T_0 u + \sum_{j=1}^{Q} E^{\ell}_j T^{\ell}_j u\Biggr\|_{1,k}^2 \le 2\|T_0 u\|_{1,k}^2 + 2\Bigg\| \sum_{j=1}^{Q} E^{\ell}_j T^{\ell}_j u\Bigg\|_{1,k}^2.\tag{80}\] For the first term on the right-hand side of 80 , Cauchy–Schwarz and ?? yield \[\begin{align} \|T_0 u\|_{1,k}^2 &= (T_0 u, T_0 u)_{1,k} = (T_0 u - u, T_0 u)_{1,k} + (u, T_0 u)_{1,k} \nonumber\\[0.5ex] &\le \|T_0 u - u\|_{1,k}\|T_0 u\|_{1,k} + \|u\|_{1,k}\|T_0 u\|_{1,k} \nonumber\\[0.5ex] &\le 2\|u\|_{1,k}\|T_0 u\|_{1,k} + \|u\|_{1,k}\|T_0 u\|_{1,k} = 3\|u\|_{1,k}\|T_0 u\|_{1,k}. \label{eq:3249519} \end{align}\tag{81}\] For the second term on the right-hand side of 80 we use 30 , Lemma 9 and 28 to obtain \[\label{eq:second} \Bigg\|\sum_{j=1}^{Q} E^{\ell}_j T^{\ell}_j u\Bigg\|_{1,k}^2 \le \Lambda_{\ell} \sum_{j=1}^{Q} \|T^{\ell}_j u\|_{1,k,\Omega^{\ell}_j}^2 \le \Lambda_{\ell} \sum_{j=1}^{Q} 4\big\|u|_{\Omega_i}\big\|_{1,k,\Omega^{\ell}_j}^2 \le 4 \Lambda_{\ell}^2\|u\|_{1,k}^2.\tag{82}\] Combining 81 and 82 with 80 we arrive at \[\|Tu\|_{1,k}^2 \le 2\times9\|u\|_{1,k}^2 + 2\times4 \Lambda_{\ell}^2\|u\|_{1,k}^2 = (18 + 8\Lambda_{\ell}^2)\|u\|^2_{1,k},\] which proves ?? . ◻

We now complete the proof of Theorem 10.

Proof of Theorem 10. Using ?? with ?? we obtain \[c_1 \|u\|_{1,k}^2 \le (Tu,u)_{1,k} = \bigl\langle \mathbf{M}_{AS,2}^{-1}\mathbf{B}\mathbf{u},\mathbf{u}\bigr\rangle_{\mathbf{D}_{k}},\] which can be written as \[c_1 \le \frac{\bigl\langle\mathbf{M}_{AS,2}^{-1}\mathbf{B}\mathbf{u},\mathbf{u}\bigr\rangle_{\mathbf{D}_{k}}}{\|\mathbf{u}\|_{\mathbf{D}_{k}}^2 }.\] Also, combining ?? with ?? , we get \[\|\mathbf{M}_{AS,2}^{-1}\mathbf{B}\mathbf{u}\|_{\mathbf{D}_{k}}^2 = \|Tu\|_{1,k}^2 \le c_2\|u\|_{1,k}^2 = c_2\|\mathbf{u}\|^2_{\mathbf{D}_{k}}, \qquad \text{i.e.} \quad \|\mathbf{M}_{AS,2}^{-1}\mathbf{B}\|_{\mathbf{D}_k}^2 \le c_2.\] The result on GMRES convergence now follows directly from the Elman theory [15] with \(\gamma=\frac{c_1}{c_2}\). ◻

5 Numerical results↩︎

This section illustrates the effectiveness of the the two-level \(H_k\)-GenEO preconditioner 33 , by testing its robustness to wavenumber \(k\), scalability with respect to the number of coarse subdomains \(N\) and sensitivity to the spectral threshold \(\tau\), with the latter controlling the dimension of the coarse space. All computations are performed in FreeFEM, using the ffddm framework to provide MPI-parallel tools for domain decomposition.

The domain \(\Omega \mathrel{\mathop{:}}=(0,1)^2\) is partitioned into local and coarse subdomains (see 27 ), each of which are here taken to be non-overlapping squares, extended by one finite element layer on each side of the interface to create minimal overlap. Unless otherwise stated, minimal overlap is used throughout numerical experiments. The local matrices \(\mathbf{B}^{\ell}_j\) on \(\Omega_j^\ell\) in 33 are factorised using MUMPS with the factors reused throughout the iterative solve. To obtain the coarse matrix \(\mathbf{B}_0\) the generalised eigenproblem ?? is solved on each coarse subdomain \(\Omega_i^c\) using SLEPc, and eigenmodes with eigenvalues below a prescribed threshold \(\tau\) are retained (see 7 ). The corresponding coarse basis functions are assembled into a distributed coarse matrix, factorised with MUMPS using a dedicated pool of MPI processes. The preconditioned system 34 is solved with GMRES (no restart) until the relative residual is reduced below \(10^{-6}\) or 200 iterations are reached. We distinguish clearly between the setup phase (assembly, eigenproblems, factorisations) and the application phase (local and coarse solves at each iteration). Except for §5.4, the local and coarse covers in 27 are taken to be identical, so that \(N=Q\) and \(H_\ell = H_c\). Two representative test cases are considered.

5.0.0.1 Homogeneous problem.

  We study problem 1 with \(A = I\) and \(n=1\) but with \(k\) increasing. The function \(f\) is a point source modelled by the Gaussian: \(f(x,y)=10^4 \exp(-10^3[(x-\frac{1}{2})^2+(y-\frac{1}{2})^2])\). The discretisation space \(V^h\) consists of standard Lagrange elements of degree 1 on a uniform Cartesian grid with spacing \(h \sim 1/k\), using alternating diagonals to create a conforming simplicial mesh.

5.0.0.2 Heterogenous problem.

  This is the same as the homogenous case, except that the diffusion coefficient \(A = I\) is here replaced by \(A(\mathbf{x}) = a(\mathbf{x})I\) where the heterogeneous function \(a\) represents a layered medium as depicted in Figure [fig:HetPlot95Hk]. Here \(a\) takes two values, either \(a \equiv a_{\mathrm{\textsf{min}}}= 1\) (in the white strips) or \(a \equiv a_{\mathrm{\textsf{max}}}> 1\) (in the grey strips). The contrast is thus controlled by the single parameter \(a_{\mathrm{\textsf{max}}}\).

Figure 1: The function a: in the dark regions a \equiv a_{\mathrm{\textsf{max}}}, where a_{\mathrm{\textsf{max}}}> 1 is a parameter,and in the white regions a \equiv 1.

In Tables 1 and 2 we show (for the homogeneous and heterogeneous cases), the dimension of the fine mesh (dim(Fine)), the maximum number of negative eigenvalues per subdomain (Neg), the minimum eigenvalue (\(\lambda_\text{min}\)), the dimension of the coarse space (dim(CS)), and the GMRES iteration count (It) for varying \(k\), \(N\) and \(\tau \in\{0,2,0,4,0.6\}\).

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Table 1: Homogeneous case: spectral data, and GMRES iterations for varying \(k,N,\tau\)
Problem parameters Spectral data \(\tau=0.2\) \(\tau=0.4\) \(\tau=0.6\)
\(N\) \(k\) \(h^{-1}\) Dim(Fine) Neg. \(\lambda_{\min}\) Dim(CS) It. Dim(CS) It. Dim(CS) It.
16 20 240 58081 4 -0.324293 144 21 240 15 392 12
60 720 519841 22 -0.849475 668 23 1160 16 1984 12
100 1200 1442401 58 -0.941228 1612 27 2660 14 4572 11
36 20 240 58081 3 -0.178013 220 23 344 18 600 13
60 720 519841 13 -0.703879 844 29 1528 18 2576 13
100 1200 1442401 28 -0.875285 1876 34 3260 16 5584 12
64 20 240 58081 1 -0.125924 224 35 448 20 740 15
60 720 519841 8 -0.553713 1060 33 1924 18 3232 13
100 1200 1442401 17 -0.793739 2276 35 3840 21 6508 13
100 20 240 58081 1 -0.100482 324 32 684 18 1044 13
60 720 519841 4 -0.423257 1144 42 2200 19 3748 14
100 1200 1442401 9 -0.721832 1846 45 3588 22 5976 15
144 20 240 58081 1 -0.0852859 484 28 672 21 1096 16
60 720 519841 4 -0.324293 1584 33 2448 23 4232 14
100 1200 1442401 7 -0.648756 2594 50 4186 25 6982 16

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Table 2: Heterogeneous case (\(a_{\mathrm{\textsf{max}}}=10\) and \(1000\)): spectral data, and GMRES iterations for varying \(k,N,\tau\)
Problem parameters Spectral data \(\tau=0.2\) \(\tau=0.4\) \(\tau=0.6\)
\(N\) \(a_{\mathrm{\textsf{max}}}\) \(k\) \(h^{-1}\) Dim(Fine) Neg. \(\lambda_{\min}\) Dim(CS) It. Dim(CS) It. Dim(CS) It.
16 10 20 240 58081 2 -0.0795278 112 25 196 19 340 16
60 720 519841 15 -0.620619 474 68 866 19 1468 15
100 1200 1442401 35 -0.840064 1058 35 1860 20 3132 18
16 1000 20 240 58081 2 -0.0010342 112 25 190 19 332 16
60 720 519841 12 -0.523291 452 32 826 20 1392 17
100 1200 1442401 31 -0.796087 974 26 1758 18 2974 15
36 10 20 240 58081 2 -0.0444599 180 28 322 19 556 16
60 720 519841 7 -0.512559 650 30 1220 19 2066 15
100 1200 1442401 17 -0.785888 1350 35 2456 19 4182 16
36 1000 20 240 58081 2 -0.000860171 178 26 320 19 544 17
60 720 519841 7 -0.473817 640 28 1180 20 2018 16
100 1200 1442401 15 -0.767375 1288 62 2382 49 4026 22
64 10 20 240 58081 1 -0.0381661 220 31 446 19 732 17
60 720 519841 6 -0.437747 854 56 1580 50 2660 46
100 1200 1442401 13 -0.738717 1658 34 3100 18 5160 16
64 1000 20 240 58081 1 -0.00392729 218 27 458 20 736 17
60 720 519841 5 -0.417451 830 35 1578 20 2638 17
100 1200 1442401 11 -0.730374 1634 34 3012 21 5000 17
100 10 20 240 58081 1 -0.0364662 318 27 572 19 932 17
60 720 519841 4 -0.37355 1130 32 2094 19 3392 16
100 1200 1442401 12 -0.689838 2142 37 3882 19 6242 16
100 1000 20 240 58081 1 -0.000587616 360 23 548 18 932 16
60 720 519841 3 -0.361218 1194 28 2122 18 3352 16
100 1200 1442401 10 -0.687193 2232 25 3872 19 6200 16
144 10 20 240 58081 1 -0.0513371 410 30 646 21 1120 17
60 720 519841 4 -0.295028 1202 82 2250 23 3894 18
100 1200 1442401 8 -0.601849 2198 53 4328 21 7116 16
144 1000 20 240 58081 1 -0.016502 408 25 660 20 1150 17
60 720 519841 3 -0.288366 1222 68 2268 21 3856 17
100 1200 1442401 8 -0.599871 2162 114 4266 119 7032 22

5.1 Behaviour of the method with respect to different parameters↩︎

5.1.0.1 Influence of the threshold \(\tau\).

As expected, for both homogeneous and heterogeneous problems, \(\tau\) regulates the size of the coarse space and robustness of the preconditioner.

\(\bullet\)

For \(\tau=0.2\), only a limited number of eigenfunctions are retained. This leads to smaller coarse spaces but a clear deterioration in robustness as \(k\) increases. As \(k\) increases, the GMRES iterations grow substantially, especially for larger subdomain counts \(N\).

For \(\tau=0.4\), a favourable balance is observed. The coarse space dimension grows moderately with \(k\) and \(N\), while the iteration counts mostly remain stable and essentially independent of \(k\). This represents an effective compromise between setup cost and iteration count.

For \(\tau=0.6\), more eigenmodes are included in the coarse space. and increases (typically by a factor between \(1.5\) and \(2\) compared to \(\tau=0.4\)), but GMRES convergence becomes nearly independent of both \(k\) and \(N\). Iteration counts flatten, demonstrating enhanced robustness.

5.1.0.2 Dependence on frequency \(k\) and number of subdomains \(N\).

For all values of \(\tau\) tested, the number of negative eigenvalues of ?? increases with \(k\), reflecting the growing indefiniteness of the operator. Nevertheless, when \(\tau\) is chosen moderately or generously (\(\tau=0.4\) or \(\tau=0.6\)), the resulting coarse space appears to capture the problematic modes which, if left out, yield poor convergence. We observe that the GMRES iteration counts for \(\tau = 0.4, 0.6\) remain essentially bounded as \(k\) grows. This observed behaviour appears somewhat better than the theoretical estimate for \(\tau\) given in 8 , which grows with \(k\). However, we should be aware that here we have only tested values of \(k\) within numerical reach.

While increases with \(k\), its rate of increase is moderate compared to the order \(k^2\) increase rate of . For example, simple extrapolation of the results for \(\tau = 0.4\) in Table 1 show a growth rate of order about \(k^{1.4}\) for \(N= 16\), \(k^{1.3}\) for \(N=64\) and only \(k^{1.1}\) for \(N=144\).

5.1.0.3 Effect of heterogeneity.

Table 2 exhibits broadly similar behaviour to Table 1, although the precise coarse space dimensions and iteration counts do depend (but only mildly) on the contrast \(a_{\mathrm{\textsf{max}}}\). This suggests that there may be scope to sharpen the theory in the heterogeneous case: the lower bound for the convergence rate in ?? contains an explicit dependence on \(a_{\mathrm{\textsf{max}}}\), whereas this dependence is not strongly visible in the present numerical tests. In some moderate-frequency cases, heterogeneity even slightly reduces iteration counts, consistent with a reduced magnitude of the most negative local eigenvalues. In general, the method demonstrates robustness not only with respect to frequency \(k\) and subdomain count \(N\), but also with respect to coefficient variation.

5.2 Spectral Complexity of Local Eigenproblems↩︎

Table 3 reports, for \(\tau=0.4\), the number of local eigenvalues per subdomain lying below \(\tau\), separated into negative and positive contributions and maximised (with \(a_{\mathrm{\textsf{max}}}=1000\) in the heterogeneous case). We observe:

\(\bullet\)

Spectral growth with \(k\): For fixed \(N\), both Max.Neg and Max.Pos increase as \(k\) grows, reflecting the increasing indefiniteness of the GEVP ?? .

Effect of subdomain size (\(N\)): For fixed \(k\), increasing \(N\) (i.e.reducing subdomain size) significantly decreases both negative and small positive eigenvalues. For instance, at \(k=60\), Max.Neg decreases from \(22\) (for \(N=16\)) to \(4\) (for \(N=144\)). This agrees with theory: when \(H_c\) is small enough, the GEVPs ?? will become positive definite or only weakly indefinite (cf.Lemma 8).

Table 3: For \(\tau=0.4\), maximum number of negative eigenvalues and maximum number of positive eigenvaluesbelow the threshold per subdomain, for the homogeneous and heterogeneous cases.
Homogeneous Heterogeneous
\(N\) \(k\) \(h^{-1}\) Dim(Fine) Max.Neg. Max.Pos Max.Neg. Max.Pos
16 20 240 58081 4 14 2 16
40 480 231361 13 36 7 35
60 720 519841 22 64 12 61
80 960 923521 41 91 18 90
100 1200 1442401 58 132 31 120
36 20 240 58081 3 9 2 12
40 480 231361 6 22 3 24
60 720 519841 13 36 7 40
80 960 923521 20 52 9 55
100 1200 1442401 28 71 15 75
64 20 240 58081 1 7 1 8
40 480 231361 4 14 3 17
60 720 519841 8 26 5 29
80 960 923521 13 36 8 40
100 1200 1442401 17 48 11 55
100 20 240 58081 1 7 1 7
40 480 231361 3 13 2 17
60 720 519841 4 20 3 26
80 960 923521 8 26 6 38
100 1200 1442401 13 36 10 47
144 20 240 58081 1 4 1 6
40 480 231361 3 9 2 11
60 720 519841 4 14 3 19
80 960 923521 6 22 6 24
100 1200 1442401 8 31 8 34

5.3 Eigenfunction behaviour and threshold Selection↩︎

A striking feature of Tables 1 and 2 is that \(\lambda_{\min}\) always appears bounded below by \(-1\), whereas in Theorem 15 we could only prove the lower bound \(-C_{\mathrm{\textsf{eig}}}\) with a fairly complicated \(C_{\mathrm{\textsf{eig}}}\). Some support for the conjecture \(C_{\mathrm{\textsf{eig}}}= 1\) could be found by looking at 6 , simplified by ignoring the POU: \[\label{cheat} - \nabla\cdot(A \nabla u) - k^2 u = \lambda\bigl(-\nabla\cdot(A \nabla u) + k^2 u\bigr), \quad \text{with Neumann boundary condition}.\tag{83}\] An easy manipulation shows that \(-\nabla\cdot( A \nabla u) = ((1+ \lambda)/(1-\lambda))k^2 u\), with \(\lambda = 1\) not possible since it would imply \(u = 0\). From this it follows that \[\lambda = \frac{\mu_j - k^2}{\mu_j + k^2} = -1 + \frac{2 \mu_j}{\mu_j + k^2 } \ge -1,\] where \(\mu_j \ge 0\) is the \(j\)th Neumann eigenvalue of the operator \(- \nabla\cdot(A \nabla u)\). Interestingly, this also suggests that, for \(\lambda\) near \(-1\), the eigenfunctions of 83 are (non-oscillatory) Neumann eigenfunctions corresponding to small eigenvalues of \(- \nabla\cdot(A \nabla u)\), while \(\lambda \approx 0\) corresponds to \(\mu_j \approx k^2\) with corresponding eigenfunctions oscillating (roughly) with period \(\sim k^{-1}\). Confirmation of this for the homogeneous case is given in Figure 2, which displays eigenfunctions of problem ?? (with the POU) on a floating subdomain (i.e.one that does not intersect the global boundary). We can clearly see that as \(\lambda\) increases from about \(-0.94\) to \(6.5 \times 10^{-5}\), the eigenfunctions become increasingly oscillatory.

Figure 2: Eigenvector plots for k=100 and h^{-1}=1200 (homogeneous case). Eigenfunctions are normalised in \ell_1 norm. Red/blue colours correspond to positive values and yellow/green to negative ones

The plots in Figure 2 for \(\lambda\) near zero are reminiscent of previous work on plane wave based coarse spaces for Helmholtz problems (e.g.[35] and the references therein). Given this, it seems reasonable to expect that the ‘symmetric strategy’ of retaining only \(\lambda \in [-\tau, \tau]\) rather than the ‘upper bound’ strategy \(\lambda \le \tau\) may reduce coarse space size without risking a loss of \(k\)-robustness. In Table 4, we compare the symmetric and upper bound strategies when applied to the homogeneous problem for \(\tau = 0.6\). We see that in this case the symmetric strategy works well with little degradation of iteration count and a modest reduction of . However, we found the symmetric strategy worked less well when applied with \(\tau = 0.2\). So, while the symmetric strategy can be useful in practice, the choice of \(\tau\) has to be chosen after some experimentation.

Table 4: Homogeneous problem: symmetric and upper bound strategies
\(\lambda \in[-0.6,0.6]\) \(\lambda \leq 0.6\)
Dim Max.Num. Dim It. Max.Num. Dim It.
\(N\) \(k\) \(h^{-1}\) (Fine) Neg. \(\lambda_\text{min}\) (CS) Count Neg. \(\lambda_\text{min}\) (CS) Count
16 20 240 58081 4 -0.324293 392 14 4 -0.324293 392 14
60 720 519841 19 -0.52068 1936 15 22 -0.849475 1984 15
100 1200 1442401 50 -0.595937 4444 30 58 -0.941228 4572 29
36 20 240 58081 3 -0.178013 600 15 3 -0.178013 600 15
60 720 519841 12 -0.419254 2540 15 13 -0.703879 2576 15
100 1200 1442401 25 -0.587053 5476 54 28 -0.875285 5584 56
64 20 240 58081 1 -0.125924 740 15 1 -0.125924 740 15
60 720 519841 8 -0.553713 3232 15 8 -0.553713 3232 15
100 1200 1442401 16 -0.562646 6444 15 17 -0.793739 6508 15
100 20 240 58081 1 -0.100482 1044 17 1 -0.100482 1044 17
60 720 519841 4 -0.423257 3748 16 4 -0.423257 3748 16
100 1200 1442401 12 -0.419254 7436 16 13 -0.703879 7536 15
144 20 240 58081 1 -0.0852859 1096 17 1 -0.0852859 1096 17
60 720 519841 4 -0.324293 4232 16 4 -0.324293 4232 16
100 1200 1442401 7 -0.599849 8400 16 8 -0.612531 8500 16

5.4 Decoupled decomposition↩︎

We now investigate the effect of constructing the coarse space on a decomposition that differs from that used for the one-level method. Specifically, the one-level preconditioner is built on \(Q=144\) subdomains, while the coarse space is constructed using a separate decomposition with \(N \in \{16,25,36,64,100,144\}\) subdomains. The generalised eigenproblems are solved on these coarse patches. In this part we will limit our investigation to the homogeneous case. Table 5 reports the coarse space dimension (CS) and GMRES iteration counts for \(\tau=0.6\). Several observations can be made:

Table 5: Coarse space size (CS) and iteration count (It.) for a one-level decomposition of \(Q=144\) subdomains and varying coarse-level decompositions \(N\) with threshold parameter \(\tau=0.6\) in the homogeneous domain. The final row reports the empirical growth of the coarse space size with respect to the wavenumber \(k\), obtained from a least-squares fit of \(\log(\mathrm{CS})\) against \(\log(k)\). In all cases, the coarse space size grows substantially more slowly than the fine-space dimension, which exhibits \(\mathcal{O}(k^2)\) growth.
\(N = 144\) \(N = 100\) \(N = 64\) \(N = 36\) \(N = 25\) \(N_c = 16\)
\(k\) \(h^{-1}\) Dim(Fine) CS It. CS It. CS It. CS It. CS It. CS It.
20 240 58081 1096 17 1044 20 740 24 600 19 530 18 392 20
40 480 231361 2640 20 2360 23 1800 24 1404 25 1230 21 1068 23
60 720 519841 4232 16 3748 23 3232 20 2576 20 2285 17 1984 20
80 960 923521 6048 33 5360 52 4732 45 3908 42 3527 44 3152 41
100 1200 1442401 8500 16 7536 19 6508 20 5584 19 5065 18 4572 19
Growth \(\mathcal{O}(k^{1.2})\) \(\mathcal{O}(k^{1.2})\) \(\mathcal{O}(k^{1.4})\) \(\mathcal{O}(k^{1.4})\) \(\mathcal{O}(k^{1.4})\) \(\mathcal{O}(k^{1.5})\)

\(\bullet\)

For most values of \(k\), the iteration counts remain comparable across all coarse decompositions, even when the coarse space dimension is reduced by \(40\)\(60\%\). For the coarsest decompositions, the increase in iterations is modest (typically \(1\)\(4\) iterations), indicating that substantial memory savings can be achieved with minimal loss of robustness.

The case \(k=80\) exhibits unusually high iteration counts across all decompositions, including \(N=144\). This behaviour is more pronounced when smaller thresholds are used and is likely linked to the proximity of \(k\) to a Dirichlet eigenvalue of the Laplacian, amplifying indefiniteness effects.

Overall, in the homogeneous setting, decoupling the decompositions provides an effective trade-off: reducing the coarse space size leads only to a mild increase in iteration counts. When memory constraints are relevant, coarser coarse-level decompositions are therefore attractive.

6 Conclusion↩︎

We have developed and analysed a two-level domain decomposition preconditioner for indefinite Helmholtz-type problems, in which the coarse space (\(H_k\)-GenEO) is constructed directly from local generalised eigenvalue problems involving the full indefinite operator. This contrasts with \(\Delta\)-GenEO and \(\Delta_k\)-GenEO approaches, which rely on nearby SPD formulations.

A central contribution of this work is the theoretical framework for the underlying indefinite eigenproblem and the resulting \(k\)-explicit sufficient conditions for robustness. Practically, this requires the fine subdomain diameter to scale with the wavelength and the spectral threshold \(\tau\) to retain all negative and sufficiently small positive local modes. Notably, the coarse diameter \(H_c\) does not enter the robustness conditions, allowing decoupling of the one-level and coarse decompositions.

The numerical experiments on homogeneous and layered heterogeneous media confirm the theoretical findings. Once \(\tau\) is chosen sufficiently large, iteration counts remain stable with respect to both frequency \(k\) and subdomain count \(N\). For \(\tau=0.4\), GMRES typically converges in approximately \(14\)\(25\) iterations over broad parameter ranges, while \(\tau=0.6\) further flattens iteration counts to roughly \(11\)\(16\) at the expense of a larger coarse space. In contrast, overly aggressive truncation (e.g.\(\tau=0.2\)) reduces coarse space size but compromises robustness at higher frequencies.

Two-sided thresholds offer limited benefit for moderate-to-large \(\tau\), as excluding mildly negative modes has negligible impact on convergence. However, for small \(\tau\), imposing a negative lower bound can remove oscillatory unstable modes and significantly degrade performance.

Finally, decoupled decompositions prove effective in homogeneous settings, enabling substantial coarse-space reduction with only minor iteration increases. In heterogeneous media, however, robustness depends on aligning the coarse partition with the coefficient structure, highlighting the importance of geometric compatibility between decomposition and heterogeneity.

Overall, by constructing the coarse space from the indefinite operator itself and establishing explicit robustness criteria, \(H_k\)-GenEO provides a practical and scalable preconditioner whose convergence is stable with respect to frequency, heterogeneity, and subdomain count.

6.0.0.1 Acknowledgement

IGG thanks Alastair Spence for valuable discussions on the material in §2.2. MF was funded by a studentship of the Engineering and Physical Sciences Research Council.

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