Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-16T22:06:34.234Z Has data issue: false hasContentIssue false

Wavelet and multiscale methods for operator equations

Published online by Cambridge University Press:  07 November 2008

Wolfgang Dahmen
Affiliation:
Institut für Geometrie und Praktische Mathematik RWTH Aachen Templergraben 55, 52056 Aachen, Germany E-mail: dahmen@igpm.rwth-aachen.de

Extract

More than anything else, the increase of computing power seems to stimulate the greed for tackling ever larger problems involving large-scale numerical simulation. As a consequence, the need for understanding something like the intrinsic complexity of a problem occupies a more and more pivotal position. Moreover, computability often only becomes feasible if an algorithm can be found that is asymptotically optimal. This means that storage and the number of floating point operations needed to resolve the problem with desired accuracy remain proportional to the problem size when the resolution of the discretization is refined. A significant reduction of complexity is indeed often possible, when the underlying problem admits a continuous model in terms of differential or integral equations. The physical phenomena behind such a model usually exhibit characteristic features over a wide range of scales. Accordingly, the most successful numerical schemes exploit in one way or another the interaction of different scales of discretization. A very prominent representative is the multigrid methodology; see, for instance, Hackbusch (1985) and Bramble (1993). In a way it has caused a breakthrough in numerical analysis since, in an important range of cases, it does indeed provide asymptotically optimal schemes. For closely related multilevel techniques and a unified treatment of several variants, such as multiplicative or additive subspace correction methods, see Bramble, Pasciak and Xu (1990), Oswald (1994), Xu (1992), and Yserentant (1993). Although there remain many unresolved problems, multigrid or multilevel schemes in the classical framework of finite difference and finite element discretizations exhibit by now a comparatively clear profile. They are particularly powerful for elliptic and parabolic problems.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adams, R. A. (1978), Sobolev Spaces, Academic Press.Google Scholar
Alpert, A., Beylkin, G., Coifman, R. and Rokhlin, V. (1993), ‘Wavelet-like bases for the fast solution of second-kind integral equations’, SIAM J. Sci. Statist. Comput. 14, 159184.CrossRefGoogle Scholar
Alpert, B. (1993), ‘A class of bases in L 2 for sparse representation of integral operators’, SIAM J. Math. Anal. 24, 246262.CrossRefGoogle Scholar
Andersson, L., Hall, N., Jawerth, B. and Peters, G. (1994), Wavelets on closed subsets of the real line, in Topics in the Theory and Applications of Wavelets (Schumaker, L. L. and Webb, G., eds), Academic Press, Boston, pp. 161.Google Scholar
Angeletti, J. M., Mazet, S. and Tchamitchian, P. (1997), Analysis of second order elliptic operators without boundary conditions and with VMO or Hölderian coefficients, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. J. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Averbuch, A., Beylkin, G., Coifman, R. and Israeli, M. (1995), Multiscale inversion of elliptic operators, in Signal and Image Representation in Combined Spaces (Zeevi, J. and Coifman, R., eds), Academic Press, pp. 116.Google Scholar
Babuška, I. (1973), ‘The finite element method with Lagrange multipliers’, Numer. Math. 20, 179192.CrossRefGoogle Scholar
Babuška, I. and Miller, A. (1987), ‘A feedback finite element method with a posteriori error estimation: Part I. The finite element method and some basic properties of the a posteriori error estimator’, Comput. Meth. Appl. Mech. Eng. 61, 140.CrossRefGoogle Scholar
Babuška, I. and Rheinboldt, W. C. (1978), ‘Error estimates for adaptive finite element computations’, SIAM J. Numer. Anal. 15, 736754.CrossRefGoogle Scholar
Bank, R. E. and Weiser, A. (1985), ‘Some a posteriori error estimates for elliptic partial differential equations’, Math. Comput. 44, 283301.CrossRefGoogle Scholar
Bank, R. E., Sherman, A. H. and Weiser, A. (1983), Refinement algorithms and data structures for regular local mesh refinement, in Scientific Computing (Stepleman, R. and et al. , eds), IMACS, North-Holland, Amsterdam, pp. 317.Google Scholar
Barsch, T., Kunoth, A. and Urban, K. (1997), Towards object oriented software tools for numerical multiscale methods for PDEs using wavelets, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. J. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Bergh, J. and Löfström, J. (1976), Interpolation Spaces: An Introduction, Springer.CrossRefGoogle Scholar
Berkooz, G., Elezgaray, J. and Holmes, P. (1993), Wavelet analysis of the motion of coherent structures, in Progress in Wavelet Analysis and Applications (Meyer, Y. and Roques, S., eds), Editions Frontières, pp. 471476.Google Scholar
Bertoluzza, S. (1994), ‘A posteriori error estimates for wavelet Galerkin methods’, Istituto di Analisi Numerica, Pavia. Preprint Nr. 935.Google Scholar
Bertoluzza, S. (1997), An adaptive collocation method based on interpolating wavelets, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. J. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Beylkin, G. (1992), ‘On the representation of operators in bases of compactly supported wavelets’, SIAM J. Numer. Anal. 29, 17161740.CrossRefGoogle Scholar
Beylkin, G. (1993), Wavelets and fast numerical algorithms, in Different Perspectives on Wavelets (Daubechies, I., ed.), Vol. 47 of Proc. Symp. Appl. Math., pp. 89117.CrossRefGoogle Scholar
Beylkin, G. and Keiser, J. M. (1997), An adaptive pseudo-wavelet approach for solving nonlinear partial differential equations, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. J. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Beylkin, G., Coifman, R. R. and Rokhlin, V. (1991), ‘Fast wavelet transforms and numerical algorithms I’, Comm. Pure Appl. Math. 44, 141183.CrossRefGoogle Scholar
Beylkin, G., Keiser, J. M. and Vozovoi, L. (1996), ‘A new class of stable time discretization schemes for the solution of nonlinear PDE's’. Preprint.Google Scholar
Bornemann, F. and Yserentant, H. (1993), ‘A basic norm equivalence for the theory of multilevel methods’, Numer. Math. 64, 455476.CrossRefGoogle Scholar
Bornemann, F., Erdmann, B. and Kornhuber, R. (1996), ‘A posteriori error estimates for elliptic problems in two and three space dimensions’, SIAM J. Numer. Anal. 33, 11881204.CrossRefGoogle Scholar
Braess, D. (1997), Finite Elements: Theory, Fast Solvers and Applications in Solid Mechanics, Cambridge University Press.CrossRefGoogle Scholar
Bramble, J. H. (1981), ‘The Lagrange multiplier method for Dirichlet's problem’, Math. Comput. 37, 111.Google Scholar
Bramble, J. H. (1993), Multigrid Methods, Vol. 294 of Pitman Research Notes in Mathematics, Longman, London. Co-published in the USA with Wiley, New York.Google Scholar
Bramble, J. H. and Pasciak, J. (1988), ‘A preconditioning technique for indefinite systems resulting from mixed approximations for elliptic problems’, Math. Comput. 50, 117.CrossRefGoogle Scholar
Bramble, J. H. and Pasciak, J. (1994), Iterative techniques for time dependent Stokes problems. Preprint.Google Scholar
Bramble, J. H., Leyk, Z. and Pasciak, J. E. (1994), ‘The analysis of multigrid algorithms for pseudo-differential operators of order minus one’, Math. Comput. 63, 461478.CrossRefGoogle Scholar
Bramble, J. H., Pasciak, J. E. and Xu, J. (1990), ‘Parallel multilevel preconditioners’, Math. Comput. 55, 122.CrossRefGoogle Scholar
Brandt, A. and Lubrecht, A. A. (1990), ‘Multilevel matrix multiplication and the fast solution of integral equations’, J. Comput. Phys. 90, 348370.CrossRefGoogle Scholar
Brandt, A. and Venner, K. (preprint), Multilevel evaluation of integral transforms on adaptive grids.Google Scholar
Brewster, M. E. and Beylkin, G. (1995), ‘A multiresolution strategy for numerical homogenization’, Appl. Comput. Harm. Anal. 2, 327349.CrossRefGoogle Scholar
Brezzi, F. and Fortin, M. (1991), Mixed and Hybrid Finite Element Methods, Springer, New York.CrossRefGoogle Scholar
Canuto, C. and Cravero, I. (1996), Wavelet-based adaptive methods for advection–diffusion problems. Preprint, University of Torino.Google Scholar
Carnicer, J. M., Dahmen, W. and Peña, J. M. (1996), ‘Local decomposition of refinable spaces’, Appl. Comput. Harm. Anal. 3, 127153.CrossRefGoogle Scholar
Carrier, J., Greengard, L. and Rokhlin, V. (1988), ‘A fast adaptive multipole algorithm for particle simulations’, SIAM J. Sci. Statist. Comput. 9, 669686.CrossRefGoogle Scholar
Carstensen, C. (1996), ‘Efficiency of a posteriori BEM-error estimates for first-kind integral equations on quasi-uniform meshes’, Math. Comput. 65, 6984.CrossRefGoogle Scholar
Cavaretta, A. S., Dahmen, W. and Micchelli, C. A. (1991), Stationary Subdivision. Mem. Amer. Math. Soc., 93, #453.Google Scholar
Chan, T. and Mathew, T. (1994), Domain decomposition algorithms, in Acta Numerica, Vol. 3, Cambridge University Press, pp. 61143.Google Scholar
Charton, P. and Perrier, V. (1995), Towards a wavelet-based numerical scheme for the two-dimensional Navier–Stokes equations, in Proceedings of the ICIAM, Hamburg.Google Scholar
Charton, P. and Perrier, V. (1996), A pseudo-wavelet scheme for the two-dimensional Navier–Stokes equations. Preprint.Google Scholar
Chui, C. K. and Quak, E. (1992), Wavelets on a bounded interval, in Numerical Methods of Approximation Theory (Braess, D. and Schumaker, L., eds), Birkhäuser, Basel, pp. 124.Google Scholar
Ciesielski, Z. and Figiel, T. (1983), ‘Spline bases in classical function spaces on compact C manifolds, parts I & II’, Studia Math. 76, 158, 95–136.CrossRefGoogle Scholar
Cohen, A. (1994). Private communication.Google Scholar
Cohen, A. and Daubechies, I. (1993), ‘Non-separable bidimensional wavelet bases’, Revista Mat. Iberoamericana 9, 51137.CrossRefGoogle Scholar
Cohen, A. and Schlenker, J.-M. (1993), ‘Compactly supported bi-dimensional wavelet bases with hexagonal symmetry’, Constr. Appr. 9, 209236.CrossRefGoogle Scholar
Cohen, A., Dahmen, W. and DeVore, R. (1995), ‘Multiscale decompositions on bounded domains’. IGPM-Report 113, RWTH Aachen. To appear in Trans. Amer. Math. Soc.Google Scholar
Cohen, A., Daubechies, I. and Feauveau, J.-C. (1992), ‘Biorthogonal bases of compactly supported wavelets’, Comm. Pure Appl. Math. 45, 485560.CrossRefGoogle Scholar
Cohen, A., Daubechies, I. and Vial, P. (1993), ‘Wavelets on the interval and fast wavelet transforms’, Appl. Comput. Harm. Anal. 1, 5481.CrossRefGoogle Scholar
Coifman, R. R., Meyer, Y. and Wickerhauser, M. V. (1992), Size properties of the wavelet packets, in Wavelets and their Applications (Beylkin, G., Coifman, R. R., Daubechies, I., Mallat, S., Meyer, Y., Raphael, L. A. and Ruskai, M. B., eds), Jones and Bartlett, Cambridge, MA, pp. 453470.Google Scholar
Coifman, R. R., Meyer, Y., Quake, S. R. and Wickerhauser, M. V. (1993), Signal processing and compression with wavelet packets, in Progress in Wavelet Analysis and Applications (Meyer, Y. and Roques, S., eds), Editions Frontières, Paris, pp. 7793.Google Scholar
Dahlke, S. (1996), ‘Wavelets: Construction principles and applications to the numerical treatment of operator equations’. Habilitationsschrift, Aachen.Google Scholar
Dahlke, S. and DeVore, R. (1995), ‘Besov regularity for elliptic boundary value problems’. IGPM-Report 116, RWTH Aachen.Google Scholar
Dahlke, S. and Weinreich, I. (1993), ‘Wavelet-Galerkin methods: An adapted biorthogonal wavelet basis’, Constr. Approx. 9, 237262.CrossRefGoogle Scholar
Dahlke, S. and Weinreich, I. (1994), ‘Wavelet bases adapted to pseudo-differential operators’, Appl. Comput. Harm. Anal. 1, 267283.CrossRefGoogle Scholar
Dahlke, S., Dahmen, W. and DeVore, R. (1997 a), Nonlinear approximation and adaptive techniques for solving elliptic operator equations, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Dahlke, S., Dahmen, W. and Latour, V. (1995), ‘Smooth refinable functions and wavelets obtained by convolution products’, Appl. Comput. Harm. Anal. 2, 6884.CrossRefGoogle Scholar
Dahlke, S., Dahmen, W., Hochmuth, R. and Schneider, R. (1997 b), ‘Stable multiscale bases and local error estimation for elliptic problems’, Appl. Numer. Math. 23, 2148.CrossRefGoogle Scholar
Dahmen, W. (1994), Some remarks on multiscale transformations, stability and biorthogonality, in Wavelets, Images and Surface Fitting (Laurent, P. J., le Méhauté, A. and Schumaker, L. L., eds), A. K. Peters, Wellesley, MA, pp. 157188.Google Scholar
Dahmen, W. (1995), Multiscale analysis, approximation, and interpolation spaces, in Approximation Theory VIII, Wavelets and Multilevel Approximation (Chui, C. K. and Schumaker, L. L., eds), World Scientific, pp. 4788.Google Scholar
Dahmen, W. (1996), ‘Stability of multiscale transformations’, Journal of Fourier Analysis and Applications 2, 341361.Google Scholar
Dahmen, W. and Kunoth, A. (1992), ‘Multilevel preconditioning’, Numer. Math. 63, 315344.CrossRefGoogle Scholar
Dahmen, W. and Micchelli, C. A. (1993), ‘Using the refinement equation for evaluating integrals of wavelets’, SIAM J. Numer. Anal. 30, 507537.CrossRefGoogle Scholar
Dahmen, W. and Schneider, R. (1996), ‘Composite wavelet bases’. IGPM-Report 133, RWTH Aachen.Google Scholar
Dahmen, W. and Schneider, R. (1997 a), Wavelets on manifolds I. Construction and domain decomposition. In preparation.Google Scholar
Dahmen, W. and Schneider, R. (1997 b), Wavelets on manifolds II. Applications to boundary integral equations. In preparation.Google Scholar
Dahmen, W., Kleemann, B., Prößdorf, S. and Schneider, R. (1994 a), A multiscale method for the double layer potential equation on a polyhedron, in Advances in Computational Mathematics (Dikshit, H. P. and Micchelli, C. A., eds), World Scientific, pp. 1557.Google Scholar
Dahmen, W., Kleemann, B., Prößdorf, S. and Schneider, R. (1996 a), Multiscale methods for the solution of the Helmholtz and Laplace equation, in Boundary Element Methods: Report from the Final Conference of the Priority Research Programme 1989–1995 of the German Research Foundation, Oct. 2–4, 1995 in Stuttgart (Wendland, W., ed.), Springer, pp. 180211.Google Scholar
Dahmen, W., Kunoth, A. and Schneider, R. (1997), Operator equations, multiscale concepts and complexity, in Mathematics of Numerical Analysis: Real Number Algorithms (Renegar, J., Shub, M. and Smale, S., eds), Vol. 32 of Lectures in Applied Mathematics, AMS, Providence, RI, pp. 225261.Google Scholar
Dahmen, W., Kunoth, A. and Urban, K. (1996 b), ‘Biorthogonal spline-wavelets on the interval: Stability and moment conditions’. IGPM-Report 129, RWTH Aachen.Google Scholar
Dahmen, W., Kunoth, A. and Urban, K. (1996 c), ‘A wavelet Galerkin method for the Stokes problem’, Computing 56, 259302.CrossRefGoogle Scholar
Dahmen, W., Müller, S. and Schlinkmann, T. (199x), Multiscale techniques for convection-dominated problems. In preparation.Google Scholar
Dahmen, W., Oswald, P. and Shi, X.-Q. (1993 a), ‘C 1-hierarchical bases’, J. Comput. Appl. Math. 9, 263281.Google Scholar
Dahmen, W., Pröß;dorf, S. and Schneider, R. (1993 b), ‘Wavelet approximation methods for pseudodifferential equations II: Matrix compression and fast solution’, Advances in Computational Mathematics 1, 259335.CrossRefGoogle Scholar
Dahmen, W., Prößdorf, S. and Schneider, R. (1994 b), Multiscale methods for pseudo-differential equations on smooth manifolds, in Proceedings of the International Conference on Wavelets: Theory, Algorithms, and Applications (Chui, C. K., Montefusco, L. and Puccio, L., eds), Academic Press, pp. 385424.Google Scholar
Dahmen, W., Pröß;dorf, S. and Schneider, R. (1994 c), ‘Wavelet approximation methods for pseudodifferential equations I: Stability and convergence’, Math. Z. 215, 583620.CrossRefGoogle Scholar
Danchin, R. (1997), PhD thesis, Université Paris VI. In preparation.Google Scholar
Daubechies, I. (1988), ‘Orthonormal bases of compactly supported wavelets’, Comm. Pure Appl. Math. 41, 909996.CrossRefGoogle Scholar
Daubechies, I. (1992), Ten Lectures on Wavelets, Vol. 61 of CBMS-NSF Regional Conference Series in Applied Math., SIAM, Philadelphia.Google Scholar
de Boor, C., DeVore, R. and Ron, A. (1993), ‘On the construction of multivariate (pre-) wavelets’, Constr. Approx. 9(2), 123166.CrossRefGoogle Scholar
DeVore, R. and Lucier, B. (1992), Wavelets, in Acta Numerica, Vol. 1, Cambridge University Press, pp. 156.Google Scholar
DeVore, R. and Popov, V. (1988 a), ‘Interpolation of Besov spaces’, Trans. Amer. Math. Soc. 305, 397414.CrossRefGoogle Scholar
DeVore, R. and Popov, V. (1988 b), Interpolation spaces and nonlinear approximation, in Function Spaces and Approximation (Cwikel, M., Peetre, J., Sagher, Y. and Wallin, H., eds), Lecture Notes in Math., Springer, pp. 191205.CrossRefGoogle Scholar
DeVore, R. and Sharpley, B. (1993), ‘Besov spaces on domains in ℝd, Trans. Amer. Math. Soc. 335, 843864.Google Scholar
DeVore, R., Jawerth, B. and Popov, V. (1992), ‘Compression of wavelet decompositions’, Amer. J. Math. 114, 737785.CrossRefGoogle Scholar
Dörfler, W. (1996), ‘A convergent adaptive algorithm for Poisson's equation’, SIAM J. Numer. Anal. 33, 11061124.CrossRefGoogle Scholar
Dorobantu, M. (1995), Wavelet-Based Algorithms for Fast PDE Solvers, PhD thesis, Royal Institute of Technology, Stockholm University.Google Scholar
Elezgaray, J., Berkooz, G., Dankowicz, H., Holmes, P. and Myers, M. (1997), Local models and large scale statistics of the Kuramoto–Sivansky equation, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. J. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Enquist, B., Osher, S. and Zhong, S. (1994), ‘Fast wavelet-based algorithms for linear evolution operators’, SIAM J. Sci. Comput. 15, 755775.CrossRefGoogle Scholar
Eriksson, K., Estep, D., Hansbo, P. and Johnson, C. (1995), Introduction to adaptive methods for differential equations, in Acta Numerica, Vol. 4, Cambridge University Press, pp. 105158.Google Scholar
Farge, M., Goirand, E., Meyer, Y., Pascal, F. and Wickerhauser, M. V. (1992), ‘Improved predictability of two-dimensional turbulent flows using wavelet packet compression’, Fluid Dynam. Res. 10, 229250.CrossRefGoogle Scholar
Fornberg, B. and Whitham, G. B. (1978), ‘A numerical and theoretical study of certain nonlinear wave phenomena’, Philos. Trans. R. Soc. London, Ser. A 289, 373404.Google Scholar
Fortin, M. (1977), ‘An analysis of convergence of mixed finite element methods’, R.A.I.R.O. Anal. Numer. 11 R3, 341354.Google Scholar
Fröhlich, J. and Schneider, K. (1995), ‘An adaptive wavelet-vaguelette algorithm for the solution of nonlinear PDEs’. Preprint SC 95–28, ZIB.Google Scholar
Fröhlich, J. and Schneider, K. (1996), ‘Numerical simulation of decaying turbulence in an adaptive wavelet basis’. Preprint, Universität Kaiserslautern, Fachbereich Chemie.CrossRefGoogle Scholar
Girault, V. and Raviart, P.-A. (1986), Finite Element Methods for Navier–Stokes Equations: Theory and Algorithms, Series in Computational Mathematics, Springer.CrossRefGoogle Scholar
Glowinski, R., Pan, T. W., Wells, R. O. and Zhou, X. (1996), ‘Wavelet and finite element solutions for the Neumann problem using fictitious domains’, J. Comput. Phys. 126, 4051.CrossRefGoogle Scholar
Glowinski, R., Rieder, A., Wells, R. O. and Zhou, X. (1993), A wavelet multigrid preconditioner for Dirichlet boundary value problems in general domains, Technical Report 93–06, Rice University, Houston.Google Scholar
Gottschlich-Müller, B. and Müller, S. (1996), ‘Multiscale concept for conservation laws’. IGPM-Report 128, RWTH Aachen.Google Scholar
Greengard, L. and Rokhlin, V. (1987), ‘A fast algorithm for particle simulations’, J. Comput. Phys. 73, 325348.CrossRefGoogle Scholar
Griebel, M. (1994), Multilevelmethoden als Iterationsverfahren über Erzeugenden-systemen, Teubner Skripten zur Numerik, Teubner, Stuttgart.CrossRefGoogle Scholar
Griebel, M. and Oswald, P. (1995 a), ‘Remarks on the abstract theory of additive and multiplicative Schwarz algorithms’, Numer. Math. 70, 163180.CrossRefGoogle Scholar
Griebel, M. and Oswald, P. (1995 b), ‘Tensor product type subspace splittings and multilevel iterative methods for anisotropic problems’, Advances in Computational Mathematics 4, 171206.CrossRefGoogle Scholar
Gröchenich, K. H. and Madych, W. R. (1992), ‘Haar bases and self-afnne tilings of ℝn’, IEEE Trans. Inform. Theory 38, 556568.Google Scholar
Hackbusch, W. (1985), Multigrid Methods and Applications, Springer, New York.CrossRefGoogle Scholar
Hackbusch, W. (1989), ‘The frequency-decomposition multigrid method I, Applications to anisotropic equations’, Numer. Math. 56, 229245.CrossRefGoogle Scholar
Hackbusch, W. (1992), ‘The frequency-decomposition multigrid method II, Convergence analysis based on the additive Schwarz method’, Numer. Math. 63, 433453.CrossRefGoogle Scholar
Hackbusch, W. and Nowak, Z. P. (1984), ‘On the fast matrix multiplication in the boundary element method by panel clustering’, Numer. Math. 54, 463491.CrossRefGoogle Scholar
Hackbusch, W. and Sauter, S. (1993), ‘On the efficient use of the Galerkin method to solve Fredholm integral equations’, Appl. Math. 38, 301322.CrossRefGoogle Scholar
Harten, A. (1995), ‘Multiresolution algorithms for the numerical solution of hyperbolic conservation laws’, Comm. Pure Appl. Math. 48, 13051342.CrossRefGoogle Scholar
Hildebrandt, S. and Wienholtz, E. (1964), ‘Constructive proofs of representation theorems in separable Hilbert spaces’, Comm. Pure Appl. Math. 17, 369373.CrossRefGoogle Scholar
Hochmuth, R. (1996), ‘A posteriori estimates and adaptive schemes for transmission problems’. IGPM Report 131, RWTH Aachen.Google Scholar
Jaffard, S. (1992), ‘Wavelet methods for fast resolution of elliptic equations’, SIAM J. Numer. Anal. 29, 965986.CrossRefGoogle Scholar
Jia, R. Q. and Micchelli, C. A. (1991), Using the refinement equation for the construction of pre-wavelets II: Powers of two, in Curves and Surfaces (Laurent, P. J., le Méhauté, A. and Schumaker, L. L., eds), Academic Press, pp. 209246.CrossRefGoogle Scholar
Johnen, H. and Scherer, K. (1977), On the equivalence of the K-functional and moduli of continuity and some applications, in Constructive Theory of Functions of Several Variables, Vol. 571 of Lecture Notes in Math., Springer, pp. 119140.CrossRefGoogle Scholar
Joly, P., Maday, Y. and Perrier, V. (1994), ‘Towards a method for solving partial differential equations by using wavelet packet bases’, Comput. Meth. Appl. Mech. Engrg. 116, 301307.CrossRefGoogle Scholar
Joly, P., Maday, Y. and Perrier, V. (1997), A dynamical adaptive concept based on wavelet packet best bases: Application to convection diffusion partial differential equations, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Jouini, A. (1992), ‘Constructions de bases d'ondelettes sur les variétés’. Dissertation, Université Paris Sud–Centre d'Orsay.Google Scholar
Jouini, A. and Lemarié-Rieusset, P. G. (1992), Ondelettes sur un ouvert borné du plan. Preprint.Google Scholar
Jouini, A. and Lemarié-Rieusset, P. G. (1993), ‘Analyses multirésolutions biorthogonales et applications’, Ann. Inst. Henri Poincaré, Anal. Non Lineaire 10, 453476.CrossRefGoogle Scholar
Ko, J., Kurdila, A. J. and Oswald, P. (1997), Scaling function and wavelet preconditioners for second order elliptic problems, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Kotyczka, U. and Oswald, P. (1996), Piecewise linear pre-wavelets of small support, in Approximation Theory VIII, vol. 2 (Chui, C. K. and Schumaker, L. L., eds), World Scientific, Singapore, pp. 235242.Google Scholar
Kumano-go, H. (1981), Pseudo-Differential Operators, MIT Press, Boston.Google Scholar
Kunoth, A. (1994), Multilevel Preconditioning, PhD thesis, FU Berlin. Shaker, Aachen.Google Scholar
Kunoth, A. (1995), ‘Multilevel preconditioning: Appending boundary conditions by Lagrange multipliers’, Advances in Computational Mathematics 4, 145170.CrossRefGoogle Scholar
Ladyshenskaya, O. A. (1969), The Mathematical Theory of Viscous Incompressible Flow, 2nd edn, Gordon and Breach, New York.Google Scholar
Latto, A., Resnikoff, H. L. and Tenenbaum, E. (1992), The evaluation of connection coefficients of compactly supported wavelets, in Proceedings of French–USA Workshop on Wavelets and Turbulence (Maday, Y., ed.), Springer.Google Scholar
Lazaar, S. (1995), Algorithmes à base d'ondelettes et résolution numérique de problèmes elliptiques à coefficients variables, PhD thesis, Université d'Aix- Marseille I.Google Scholar
Lazaar, S., Liandrat, J. and Tchamitchian, P. (1994), ‘Algorithme à base d'ondelettes pour la résolution numérique d'équations aux dérivées partielles à coefficients variables’, C.R. Acad. Sci., Série I 319, 11011107.Google Scholar
Lemarié, P. G. (1984), Algèbre d'opérateurs et semi-groupes de Poisson sur un espace de nature homogène, Publ. Math. d'Orsay.Google Scholar
Lemarié-Rieusset, P. G. (1992), ‘Analyses, multi-résolutions nonorthogonales, commutation entre projecteurs et derivation et ondelettes vecteurs à divergence nulle’, Revista Mat. Iberoamericana 8, 221236.CrossRefGoogle Scholar
Lemarié-Rieusset, P. G. (1994), ‘Un théorème d'inexistence pour des ondelettes vecteurs à divergence nulle’, C. R. Acad. Sci. Paris I 319, 811813.Google Scholar
Liandrat, J. and Tchamitchian, P. (1997), ‘Elliptic operators, adaptivity and wavelets’, SIAM J. Numer. Anal. To appear.Google Scholar
Lorentz, R. and Oswald, P. (1996), ‘Constructing ‘economic’ Riesz bases for Sobolev spaces’, GMD-Birlinghoven. Preprint.Google Scholar
Lorentz, R. and Oswald, P. (1997), Multilevel finite element Riesz bases in Sobolev spaces, in DD9 Proceedings (Bjorstad, P., Espedal, M. and Keyes, D., eds), Wiley. To appear.Google Scholar
Maday, Y., Perrier, V. and Ravel, J. C. (1991), ‘Adaptivité dynamique sur base d'ondelettes pour l'approximation d'équations aux dérivées partielles’, C. R. Acad. Sci. Paris Sér. I Math. 312, 405410.Google Scholar
Mallat, S. (1989), ‘Multiresolution approximations and wavelet orthonormal bases of L 2(ℝ)’, Trans. Amer. Math. Soc. 315, 6987.Google Scholar
Meyer, Y. (1990), Ondelettes et opérateurs 1–3: Ondelettes, Hermann, Paris.Google Scholar
Meyer, Y. (1994). Private communication.Google Scholar
Micchelli, C. A. and Xu, Y. (1994), ‘Using the matrix refinement equation for the construction of wavelets on invariant set’, Appl. Comput. Harm. Anal. 1, 391401.CrossRefGoogle Scholar
Michlin, S. G. (1965), Multidimensional Singular Integral Equations, Pergamon Press, Oxford.Google Scholar
Nedelec, J. C. (1982), ‘Integral equations with non-integrable kernels’, Integral Equations Operator Theory 5, 562572.CrossRefGoogle Scholar
Nepomnyaschikh, S. V. (1990), ‘Fictitious components and subdomain alternating methods’, Sov. J. Numer. Anal. Math. Modelling 5, 5368.Google Scholar
Nieß;en, G. (1995), ‘An explicit norm representation for the analysis of multilevel methods’. IGPM-Preprint 115, RWTH Aachen.Google Scholar
Oswald, P. (1990), ‘On function spaces related to finite element approximation theory’, Z. Anal. Anwendungen 9, 4364.CrossRefGoogle Scholar
Oswald, P. (1992), On discrete norm estimates related to multilevel preconditioners in the finite element method, in Constructive Theory of Functions (Proc. Int. Conf. Varna, 1991) (Ivanov, K. G., Petrushev, P. and Sendov, B., eds), Bulg. Acad. Sci., Sofia, pp. 203214.Google Scholar
Oswald, P. (1994), Multilevel Finite Element Approximations, Teubner Skripten zur Numerik, Teubner, Stuttgart.CrossRefGoogle Scholar
Oswald, P. (1997), Multilevel solvers for elliptic problems on domains, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Peetre, J. (1978), New Thoughts on Besov Spaces, Duke University Press, Durham, NC.Google Scholar
Perrier, V. (1996), Numerical schemes for 2D-Navier–Stokes equations using wavelet bases. Preprint.Google Scholar
Ponenti, P. J. (1994), Algorithmes en ondelettes pour la résolution d'équations aux dérivées partielles, PhD thesis, Université Aix-Marseille I.Google Scholar
Quartapelle, L. (1993), Numerical Solution of the Incompressible Navier–Stokes Equations, Vol. 113 of International Series of Numerical Mathematics, Birkhäuser.CrossRefGoogle Scholar
Rokhlin, V. (1985), ‘Rapid solution of integral equations of classical potential theory’, J. Comput. Phys. 60, 187207.CrossRefGoogle Scholar
Sauter, S. (1992), Über die effiziente Verwendung des Galerkinverfahrens zur Lösung Fredholmscher Integralgleichungen, PhD thesis, Universität Kiel.Google Scholar
Schneider, R. (1995), ‘Multiskalen- und Wavelet-Matrixkompression: Analysis-basierte Methoden zur effizienten Lösung großer vollbesetzter Gleichungs-systeme’. Habilitationsschrift, Technische Hochschule Darmstadt.Google Scholar
Schröder, P. and Sweldens, W. (1995), Spherical wavelets: Efficiently representing functions on the sphere, in Computer Graphics Proceedings (SIGGRAPH 95), ACM SIGGRAPH, pp. 161172.Google Scholar
Schwab, C. (1994), ‘Variable order composite quadrature of singular and nearly singular integrals’, Computing 53, 173194.CrossRefGoogle Scholar
Sjögreen, B. (1995), ‘Numerical experiments with the multiresolution scheme for the compressible Euler equations’, J. Comput. Phys. 117, 251261.CrossRefGoogle Scholar
Sonar, T. (1995), ‘Multivariate Rekonstruktionsverfahren zur numerischen Berechnung hyperbolischer Erhaltungsgleichungen’. Habilitationsschrift, Technische Hochschule Darmstadt.Google Scholar
Stevenson, R. P. (1995 a), A robust hierarchical basis preconditioner on general meshes. Preprint, University of Nijmegen.Google Scholar
Stevenson, R. P. (1995 b), ‘Robustness of the additive and multiplicative frequency decomposition multilevel method’, Computing 54, 331346.CrossRefGoogle Scholar
Stevenson, R. P. (1996), ‘The frequency decomposition multilevel method: A robust additive hierarchical basis preconditioner’, Math. Comput. 65, 983997.CrossRefGoogle Scholar
Sweldens, W. (1996), ‘The lifting scheme: A custom-design construction of biorthogonal wavelets’, Appl. Comput. Harm. Anal. 3, 186200.CrossRefGoogle Scholar
Sweldens, W. (1997), ‘The lifting scheme: A construction of second generation wavelets’, SIAM J. Math. Anal. To appear.Google Scholar
Sweldens, W. and Piessens, R. (1994), ‘Quadrature formulae and asymptotic error expansions for wavelet approximations of smooth functions’, SIAM J. Num. Anal. 31, 21402164.CrossRefGoogle Scholar
Tchamitchian, P. (1987), ‘Biorthogonalité et théorie des opérateurs’, Revista Mat. Iberoamericana 3, 163189.CrossRefGoogle Scholar
Tchamitchian, P. (1996), Wavelets, functions, and operators, in Wavelets: Theory and Applications (Erlebacher, G., Hussaini, M. Y. and Jameson, L., eds), ICASE/LaRC Series in Computational Science and Engineering, Oxford University Press, pp. 83181.CrossRefGoogle Scholar
Tchamitchian, P. (1997), ‘Inversion de certains opérateurs elliptiques à coefficients variables’, SIAM J. Math. Anal. To appear.Google Scholar
Triebel, H. (1978), Interpolation Theory, Function Spaces, and Differential Operators, North-Holland, Amsterdam.Google Scholar
Urban, K. (1995 a), Multiskalenverfahren für das Stokes-Problem und angepaßte Wavelet-Basen, PhD thesis, RWTH Aachen. Aachener Beiträge zur Mathematik.Google Scholar
Urban, K. (1995 b), ‘On divergence free wavelets’, Advances in Computational Mathematics 4, 5182.CrossRefGoogle Scholar
Urban, K. (1995 c), A wavelet-Galerkin algorithm for the driven cavity Stokes problem in two space dimension, in Numerical Modelling in Continuum Mechanics (Feistauer, M., Rannacher, R. and Korzel, K., eds), Charles University, Prague, pp. 278289.Google Scholar
Urban, K. (1996), Using divergence free wavelets for the numerical solution of the Stokes problem, in AMLI '96: Proceedings of the Conference on Algebraic Multilevel Iteration Methods with Applications (Axelsson, O. and Polman, B., eds), University of Nijmegen, pp. 261278.Google Scholar
Vassilevski, P. S. and Wang, J. (1997 a), ‘Stabilizing the hierarchical basis by approximate wavelets, I: Theory’, Numer. Lin. Alg. Appl. To appear.3.0.CO;2-J>CrossRefGoogle Scholar
Vassilevski, P. S. and Wang, J. (1997 b), ‘Stabilizing the hierarchical basis by approximate wavelets, II: Implementation’, SIAM J. Sci. Comput. To appear.Google Scholar
Verfürth, R. (1994), ‘A posteriori error estimation and adaptive mesh refinement techniques’, J. Comput. Appl. Math. 50, 6783.CrossRefGoogle Scholar
Villemoes, L. F. (1993), Sobolev regularity of wavelets and stability of iterated filter banks, in Progress in Wavelet Analysis and Applications (Meyer, Y. and Roques, S., eds), Editions Frontières, Paris, pp. 243251.Google Scholar
von Petersdorff, T. and Schwab, C. (1997 a), Fully discrete multiscale Galerkin BEM, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. and Oswald, P., eds), Academic Press. To appear.Google Scholar
von Petersdorff, T. and Schwab, C. (1997 b), ‘Wavelet approximation for first kind integral equations on polygons’, Numer. Math. To appear.Google Scholar
von Petersdorff, T., Schneider, R. and Schwab, C. (1997), ‘Multiwavelets for second kind integral equations’, SIAM J. Numer. Anal. To appear.CrossRefGoogle Scholar
Wendland, W. L. (1987), Strongly elliptic boundary integral equations, in The State of the Art in Numerical Analysis (Iserles, A. and Powell, M. J. D., eds), Clarendon Press, Oxford, pp. 511561.Google Scholar
Wickerhauser, M. V., Farge, M. and Goirand, E. (1997), Theoretical dimension and the complexity of simulated turbulence, in Multiscale Wavelet Methods for PDEs (Dahmen, W., Kurdila, A. J. and Oswald, P., eds), Academic Press. To appear.Google Scholar
Xu, J. (1992), ‘Iterative methods by space decomposition and subspace correction’, SIAM Review 34, 581613.CrossRefGoogle Scholar
Yserentant, H. (1986), ‘On the multilevel splitting of finite element spaces’, Numer. Math. 49, 379412.CrossRefGoogle Scholar
Yserentant, H. (1990), ‘Two preconditioners based on the multi-level splitting of finite element spaces’, Numer. Math. 58, 163184.CrossRefGoogle Scholar
Yserentant, H. (1993), Old and new proofs for multigrid algorithms, in Acta Numerica, Vol. 2, Cambridge University Press, pp. 285326.Google Scholar
Zhang, X. (1992), ‘Multilevel Schwarz methods’, Numer. Math. 63, 521539.CrossRefGoogle Scholar