Research Article  Open Access
Xuqing Zhang, Yidu Yang, Hai Bi, "Spectral Method with the TensorProduct Nodal Basis for the Steklov Eigenvalue Problem", Mathematical Problems in Engineering, vol. 2013, Article ID 650530, 9 pages, 2013. https://doi.org/10.1155/2013/650530
Spectral Method with the TensorProduct Nodal Basis for the Steklov Eigenvalue Problem
Abstract
This paper discusses spectral method with the tensorproduct nodal basis at the LegendreGaussLobatto points for solving the Steklov eigenvalue problem. A priori error estimates of spectral method are discussed, and based on the work of Melenk and Wohlmuth (2001), a posterior error estimator of the residual type is given and analyzed. In addition, this paper combines the shiftedinverse iterative method and spectral method to establish an efficient scheme. Finally, numerical experiments with MATLAB program are reported.
1. Introduction
Since Steklov eigenvalue problems have important physical background and many applications, for instance, they appear in the analysis of stability of mechanical oscillators immersed in a viscous fluid (see [1] and the references therein), in the analysis of the antiplane shearing on a system of collinear faults under slipdependent friction law (see [2]), in the study of surface waves (see [3]), in the study of the vibration modes of a structure in contact with an incompressible fluid (see [4]) and vibration of a pendulum (see [5]), and in the eigenoscillations of mechanical systems with boundary conditions containing frequency [6], and thus, numerical methods for solving Steklov eigenvalue problems have received increasing attention in recent years. Bramble and Osborn [7] studied the Galerkin approximation of a Steklov eigenvalue problem of nonselfadjoint second order elliptic operators in smooth domain, Andreev and Todorov [8] discussed the isoparametric finite element method for the approximation of the Steklov eigenvalue problem of secondorder selfadjoint elliptic differential operators, Armentano and Padra [9] introduced and analyzed the conforming linear finite element approximation of the Steklov eigenvalue problem in a bounded polygonal domain, Alonso and Russo [10], Yang et al. [11], and Li et al. [12] studied nonconforming finite elements approximation of the Steklov eigenvalue problem, Li and Yang [13] and Bi and Yang [14] discussed a twogrid method of the conforming and nonconforming finite element method, respectively, Li et al. [15] studied the extrapolation and superconvergence of the Steklov eigenvalue problem, Tang et al. [16] studied the boundary element approximation, and Cao et al. [17] discussed multiscale asymptotic method for Steklov eigenvalue equations in composite medias. However, to the best of our knowledge, there have been no reports on spectral method for Steklov eigenvalue problems.
Spectral method is an important numerical method for solving differential equations developed after finitedifference and finiteelement methods. Because spectral method has the characteristics of superior accuracy, it is widely used in the field of meteorology, physics, and mechanics (see [18, 19]). This paper discusses spectral method with the tensorproduct nodal basis at the LegendreGaussLobatto (LGL for short) points for solving the Steklov eigenvalue problem. We use spectral approximation theory (see [20]) to give a priori error estimates. Based on the work of [21], we discuss a posterior error estimator of the residual type. Moreover, inspired by the work of [22] this paper combines the shiftedinverse iterative method and spectral method to propose an efficient computation scheme. Finally, by using program with Matlab, we implement the numerical experiments and get satisfactory results.
The rest of the paper is organized as follows. In Section 2, some preliminaries needed in this paper are presented. In Section 3, we discuss a priori and a posterior error estimates for spectral method. In Section 4, we establish an efficient scheme combining the shiftedinverse iteration and spectral method. In Section 5, the numerical experiments on the square domain are reported.
2. Preliminaries
We consider the following boundary value problem: where is a rectangular domain and is the outward normal derivative on . We denote the Sobolev space with norm and seminorm by . Let and be the norms in the space and , respectively.
Throughout this paper, denotes a generic positive constant independent of the polynomial degrees , which may not be the same at each occurrence.
The variational problem associated with (1) is given by the following.
Find , such that where Obviously, the bilinear form is continuous and elliptic, that is, there exists constants and independent of , , such that
From the LaxMilgram theorem we know that there exists a unique solution to (2).
The following regularity results (see [4]) will be needed in the sequel: for , the solution of problem (1) , and furthermore, if , then and where , are constants independent of , and if is convex and if is concave (with being the largest inner angle of ).
Define
The spectralGalerkin approximation to (2) is the following.
Find , such that
For and each integer , we define the interpolation operator with the LGL interpolation nodes in . We quote from [18] the following interpolation estimates for spectral method at the tensorproduct LGL points
Let and be the solution of (2) and (8), respectively. Then we derive from Céa Lemma that Notice that (11) is also valid for special method with modal basis (see [18, 19]).
Using AubinNitsche technique, we deduce the a priori error estimate from (5) and (10)(11):
3. Spectral Method and Its Error Estimates for the Steklov Eigenvalue Problem
3.1. Spectral Method for the Steklov Eigenvalue Problem
We consider the following Steklov eigenvalue problem: where is a rectangular domain.
The variational problem associated with (13) is given by the following.
Find , , such that
Let be an eigenpair of (14); then from (6) we know that .
The spectralGalerkin approximation to (14) is as the follows.
Find , , such that
Let and be the solution operators defined by Obviously, is completely continuous and is a finite ranked operator. Equations (14) and (15) have the following equivalent operator forms, respectively:
3.2. A Prior Error Estimates of Spectral Method for the Steklov Eigenvalue Problem
Let be an eigenpair of (14), let be the space spanned by all eigenfunctions corresponding to the eigenvalue , and let be the direct sum of the eigenspace corresponding to the eigenvalues of (15) that converge to .
Denote where characterizes the degree of the space approximating , and characterize the degrees of approximating the generalized solution of (2).
Lemma 1. Let ; then
Proof. We deduce from the interpolation estimate (10) that Using (10) and (6) we have From (10) and regularity estimate (5) we get The proof is completed.
Theorem 2. Let ; then let and ; then there exists , such that let and ; then there exists , such that
Proof. From [20, 23] we have that let and ; then there exists , such that let and ; then there exists , such that Combining Lemma 1 we obtain the desired results.
3.3. A Posterior Error Estimates for the Steklov Eigenvalue Problem
Theorem 2.1 in [24] gave the following results.
Lemma 3. There exists a positive constant and a linear bounded operator , such that for all and an arbitrary edge of , and there holds
Analysis of the error indicators requires polynomial inverse estimates in weighted Sobolev spaces. Define on the interval the weight function ; then there holds the following polynomial inverse estimates.
Lemma 4. Let , ; then there exists , , and such that for every univariate polynomial of degree , there holds
Proof. See Lemma 2.4 in [21].
Consider the domain and the weight function . From [21] we have the twodimensional polynomial inverse estimates.
Lemma 5. Let , ; then there exists , , and such that for all polynomials there hold
Proof. See Lemma 2.5 in [21].
Lemma 6. Let , set , and let be defined as the same as , that is, . Then there exists , such that for every and every univariate polynomial of degree , there exists an extension , such that the following holds:
Proof. See Lemma 2.6 in [21].
Remark 7. Because there has an affine transformation from rectangular domain to the unit square, Lemma 5 is suited to rectangular domain, and Lemmas 4 and 6 are valid for all edges of rectangular.
Define the global error indicator , , which is expressed as the sum of two terms: where the first term is the weighted internal residual given by and the second term is the weighted edge residual given by where the weight functions and are scaled transformations of the weight functions and , respectively.
By using the proof of [21], we can get the following results.
Theorem 8. Let . Then there exists independent of , such that
Proof. We denote where is an interpolation operator satisfying Lemma 3. We derive from ellipticity of that Since thus By using CauchySchwartz inequality, the trace theorem, and Lemma 3, we get Plugging (47) into (46) gives Setting , in (32) and (35), respectively, we obtain and , and thus The proof is completed.
Lemma 9. Let . Then there exists independent of , such that
Proof. Denote , ; then
Now, we consider . Using (35)(36), we find for that
noticing that (35) is valid since the assumption implies , hence we take , in (35) and the above third inequality holds. By the definition , we obtain
To get an upper bound of the error indicator in the case of , using (35) we find for that
Therefore,
We obtain the desired result immediately.
Lemma 10. Let , . Then there exists independent of , such that
Proof. In order to obtain an upper bound for the error indicator , we will use weight function and a proper extension operator. Let be an edge of ; we construct a function and .
Let . We extend to by using Lemma 6 where the polynomial corresponds to , and from (37) we know that is zero on . We extend to such that on ; then
Now we estimate , , in turn. First,
In the case of we have
Combining (58) and (59), we get
By the definition of and setting in Lemma 9, we have that
Setting yields the assertion for in (61).
For the case , setting and using (35), we find
therefore,
From the above discussion we complete the proof.
Combining Lemmas 9 and 10, we obtain the following theorem.
Theorem 11. Let . Then there exists independent of , such that
Remark 12. The term is a term of higher order. In fact, for any , we have Thus, taking and using the a priori estimates (23) and (25), we conclude that
4. Spectral Method Based on the ShiftedInverse Iteration
High efficient finite element schemes have been researched further in recent years; for example, see [22, 25–31]. Based on these works, this paper combines the shiftedinverse iteration and spectral method to propose an efficient scheme.
Scheme 13 (spectral method based on the shiftedinverse iteration).
Step 1. Solve (13) in : Find , , such that
Step 2. Solve a linear system in . Find , such that
Step 3. Compute the Rayleigh quotient
Let be the th eigenpair of (67) then is regarded as the th approximate eigenpair of (13).
Theorem 14. Suppose that and is properly large. Let be the th approximate eigenpair obtained by Scheme 13; then there exists the th eigenpair of (13), such that
Proof. From the proof of Theorem 4.1 in [22] we can deduce that which together with Lemma 1 yields (70).
Theorem 14 tells us, when , the degree of precision (DOP) of and obtained by Scheme 13 is and , respectively; that is to say, have the same DOP with .
5. Numerical Experiments
Consider the Steklov eigenvalue problem (13) where .
We use spectral method with tensorproduct nodal basis at the LGL points (LGL spectral method) to solve (13). We construct twodimensional basis functions by using the tensor product of onedimensional basis functions. More precisely, let be the Lagrange basis polynomials associated with the LGL points; then , and let ; then (15) is equivalent to the following linear system: where is called stiffness matrix, is called mass matrix, and We program with Matlab to solve (72), and the numerical results are presented in Table 1.

In the tables in this section, DOF denotes the degree of freedom (number of nodes).
From Table 1 we can see that
We also use the bilinear finite element to solve (13), and the numerical results are listed in Table 2.

From Table 2 we see that
We use Scheme 13 to solve (13), and let be the th approximate eigenvalue , and the numerical results are presented in Table 3.

From Table 3 we find the eigenvalues obtained by Scheme 13 have the same accuracy with which is computed directly in by LGL spectral method.
Since we do not know the exact eigenvalues of the Steklov eigenvalue problem, many research studies reported the approximate eigenvalues; for example, see [15, 22]. This paper uses the LGL spectral method to solve (13), and in Table 1 we give We think these approximate eigenvalues have high accuracy.
Acknowledgment
This work is supported by the National Natural Science Foundation of China (Grant no. 11201093).
References
 C. Conca, J. Planchard, and M. Vanninathan, Fluids and Periodic Structures, vol. 38, John Wiley & Sons, New york, NY, USA, 1995. View at: MathSciNet
 D. Bucur and I. R. Ionescu, “Asymptotic analysis and scaling of friction parameters,” Zeitschrift für Angewandte Mathematik und Physik, vol. 57, no. 6, pp. 1042–1056, 2006. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 S. Bergman and M. Schiffer, Kernel Functions and Elliptic Differential Equations in Mathematical Physics, Academic Press, New York, NY, USA, 1953. View at: MathSciNet
 A. Bermúdez, R. Rodríguez, and D. Santamarina, “A finite element solution of an added mass formulation for coupled fluidsolid vibrations,” Numerische Mathematik, vol. 87, no. 2, pp. 201–227, 2000. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 H. J. Ahn, “Vibrations of a pendulum consisting of a bob suspended from a wire: the method of integral equations,” Quarterly of Applied Mathematics, vol. 39, no. 1, pp. 109–117, 1981. View at: Google Scholar  MathSciNet
 D. B. Hinton and J. K. Shaw, “Differential operators with spectral parameter incompletely in the boundary conditions,” Funkcialaj Ekvacioj, vol. 33, no. 3, pp. 363–385, 1990. View at: Google Scholar  Zentralblatt MATH  MathSciNet
 J. H. Bramble and J. E. Osborn, “Approximation of Steklov eigenvalues of nonselfadjoint second order elliptic operators,” in The Mathematical Foundations of the Finite Element Method with Applications to Partial Differential Equations, A. K. Aziz, Ed., Academic Press, San Diego, Calif, USA, 1972. View at: Google Scholar  Zentralblatt MATH  MathSciNet
 A. B. Andreev and T. D. Todorov, “Isoparametric finiteelement approximation of a Steklov eigenvalue problem,” IMA Journal of Numerical Analysis, vol. 24, no. 2, pp. 309–322, 2004. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 M. G. Armentano and C. Padra, “A posteriori error estimates for the Steklov eigenvalue problem,” Applied Numerical Mathematics, vol. 58, no. 5, pp. 593–601, 2008. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 A. Alonso and A. D. Russo, “Spectral approximation of variationallyposed eigenvalue problems by nonconforming methods,” Journal of Computational and Applied Mathematics, vol. 223, no. 1, pp. 177–197, 2009. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Y. Yang, Q. Li, and S. Li, “Nonconforming finite element approximations of the Steklov eigenvalue problem,” Applied Numerical Mathematics, vol. 59, no. 10, pp. 2388–2401, 2009. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Q. Li, Q. Lin, and H. Xie, “Nonconforming finite element approximations of the Steklov eigenvalue problem and its lower bound approximations,” Applications of Mathematics, vol. 58, no. 2, pp. 129–151, 2013. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Q. Li and Y. Yang, “A twogrid discretization scheme for the Steklov eigenvalue problem,” Journal of Applied Mathematics and Computing, vol. 36, no. 12, pp. 129–139, 2011. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 H. Bi and Y. Yang, “A twogrid method of the nonconforming CrouzeixRaviart element for the Steklov eigenvalue problem,” Applied Mathematics and Computation, vol. 217, no. 23, pp. 9669–9678, 2011. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 M. Li, Q. Lin, and S. Zhang, “Extrapolation and superconvergence of the Steklov eigenvalue problem,” Advances in Computational Mathematics, vol. 33, no. 1, pp. 25–44, 2010. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 W. Tang, Z. Guan, and H. Han, “Boundary element approximation of Steklov eigenvalue problem for Helmholtz equation,” Journal of Computational Mathematics, vol. 16, no. 2, pp. 165–178, 1998. View at: Google Scholar  Zentralblatt MATH  MathSciNet
 L. Cao, L. Zhang, W. Allegretto, and Y. Lin, “Multiscale asymptotic method for Steklov eigenvalue equations in composite media,” SIAM Journal on Numerical Analysis, vol. 51, no. 1, pp. 273–296, 2013. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 C. Canuto, M. Y. Hussaini, A. Quarteroni, and T. A. Zang, Spectral Methods Evolution to Complex Geometries and Applications to Fluid Dynamics, Springer, Berlin, Germany, 2007. View at: MathSciNet
 J. Shen, T. Tang, and L.L. Wang, Spectral Methods Algorithms, analysis and applications, vol. 41, Springer, Heidelberg, Germany, 2011. View at: Publisher Site  MathSciNet
 I. Babuska and J. Osborn, “Eigenvalue Problems,” in Handbook of Numerical Analysis, Finite Element Methods (Part 1), P. G. Ciarlet and J. L. Lions, Eds., vol. 2, pp. 640–787, Elsevier Science Publishers, 1991. View at: Google Scholar
 J. M. Melenk and B. I. Wohlmuth, “On residualbased a posteriori error estimation in $hp$FEM,” Advances in Computational Mathematics, vol. 15, no. 1–4, pp. 311–331, 2001. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Yang and H. Bi, “Twogrid finite element discretization schemes based on shiftedinverse power method for elliptic eigenvalue problems,” SIAM Journal on Numerical Analysis, vol. 49, no. 4, pp. 1602–1624, 2011. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Y. Yang, Finite Element Methods for Eigenvalue Problems, Science Press, Beijing, China, 2012.
 J. M. Melenk, “$hp$interpolation of nonsmooth functions and an application to $hp$a posteriori error estimation,” SIAM Journal on Numerical Analysis, vol. 43, no. 1, pp. 127–155, 2005. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Yang and W. Jiang, “Upper spectral bounds and a posteriori error analysis of several mixed finite element approximations for the Stokes eigenvalue problem,” Science China Mathematics, vol. 56, no. 6, pp. 1313–1330, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 J. Hu and Y. Huang, “The correction operator for the canonical interpolation operator of the Adini element and the lower bounds of eigenvalues,” Science China Mathematics, vol. 55, no. 1, pp. 187–196, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 A. Naga and Z. Zhang, “Function value recovery and its application in eigenvalue problems,” SIAM Journal on Numerical Analysis, vol. 50, no. 1, pp. 272–286, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Z. Si, Y. He, and K. Wang, “A defectcorrection method for unsteady conduction convection problems I: spatial discretization,” Science China Mathematics, vol. 54, no. 1, pp. 185–204, 2011. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Y. Yang, W. Jiang, Y. Zhang, W. J. Wang, and H. Bi, “A twoscale discretization scheme for mixed variational formulation of eigenvalue problems,” Abstract and Applied Analysis, vol. 2012, Article ID 812914, 29 pages, 2012. View at: Publisher Site  Google Scholar
 Y. Yang, Y. Zhang, and H. Bi, “Multigrid discretization and iterative algorithm for mixed variational formulation of the eigenvalue problem of electric field,” Abstract and Applied Analysis, vol. 2012, Article ID 190768, 25 pages, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 H. Bi and Y. Yang, “Multiscale discretization scheme based on the Rayleigh quotient iterative method for the Steklov eigenvalue problem,” Mathematical Problems in Engineering, vol. 2012, Article ID 487207, 18 pages, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
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