Skip to main content Accessibility help
Numerical Linear Algebra
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 2
  • Export citation
  • Recommend to librarian
  • Buy the print book

Book description

This self-contained introduction to numerical linear algebra provides a comprehensive, yet concise, overview of the subject. It includes standard material such as direct methods for solving linear systems and least-squares problems, error, stability and conditioning, basic iterative methods and the calculation of eigenvalues. Later chapters cover more advanced material, such as Krylov subspace methods, multigrid methods, domain decomposition methods, multipole expansions, hierarchical matrices and compressed sensing. The book provides rigorous mathematical proofs throughout, and gives algorithms in general-purpose language-independent form. Requiring only a solid knowledge in linear algebra and basic analysis, this book will be useful for applied mathematicians, engineers, computer scientists, and all those interested in efficiently solving linear problems.


'Wendland delivers an introductory textbook on numerical linear algebra intended for advanced undergraduate and graduate students in applied mathematics. The book covers fairly standard material in this area; it includes error analysis and ill-conditioning, direct and iterative methods for the solution of linear systems of equations, least squares problems, and eigenvalue problems. Additional advanced topics that are not usually covered in introductory textbooks include multipole expansions, domain decomposition methods, and compressive sensing. The book is generally theoretical and mathematically rigorous in its approach. Algorithms are given only in pseudocode. … The text will be of interest primarily to instructors and students in graduate numerical linear algebra courses.'

B. Borchers Source: Choice

'Wendland’s book provides the reader with rigorous and clean proofs throughout the text. There are a lot of new concepts being presented that can spark the interest of a student who wishes to take numerical linear algebra and can also serve as an excellent resource for an independent study. If you are considering a new text for your numerical linear algebra class or wish to supplement with another resource, I would recommend giving this book a review.'

Peter Olszewski Source: MAA Reviews

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.


[1] Allaire, G., and Kaber, S. M. 2008. Numerical linear algebra. New York: Springer. Translated from the 2002 French original by Karim Trabelsi.
[2] Arnoldi, W. E. 1951. The principle of minimized iteration in the solution of the matrix eigenvalue problem. Quart. Appl. Math., 9, 17–29.
[3] Arya, S., and Mount, D. M. 1993. Approximate nearest neighbor searching. Pages 271–280 of: Proceedings of the 4th Annual ACM-SIAM Symposium on Discrete Algorithms. New York: ACM Press.
[4] Arya, S., and Mount, D.M. 1995. Approximate range searching. Pages 172–181 of: Proceedings of the 11th Annual ACM Symposium on Computational Geometry. New York: ACM Press.
[5] Axelsson, O. 1985. A survey of preconditioned iterative methods for linear systems of algebraic equations. BIT, 25(1), 166–187.
[6] Axelsson, O. 1994. Iterative solution methods. Cambridge: Cambridge University Press.
[7] Axelsson, O., and Barker, V. A. 1984. Finite element solution of boundary value problems. Orlando FL: Academic Press.
[8] Axelsson, O., and Lindskog, G. 1986. On the rate of convergence of the preconditioned conjugate gradient method. Numer. Math., 48(5), 499–523.
[9] Bandeira, A. S., Fickus, M., Mixon, D. G., and Wong, P. 2013. The road to deterministic matrices with the restricted isometry property. J. Fourier Anal. Appl., 19(6), 1123–1149.
[10] Baraniuk, R., Davenport, M., DeVore, R., and Wakin, M. 2008. A simple proof of the restricted isometry property for random matrices. Constr. Approx., 28(3), 253–263.
[11] Beatson, R. K., and Greengard, L. 1997. A short course on fast multipole methods. Pages 1–37 of: Ainsworth, M., Levesley, J., Light, W., and Marletta, M. (eds.), Wavelets, multilevel methods and elliptic PDEs. 7th EPSRC numerical analysis summer school, University of Leicester, Leicester, GB, July 8–19, 1996. Oxford: Clarendon Press.
[12] Bebendorf, M. 2000. Approximation of boundary element matrices. Numer. Math., 86(4), 565–589.
[13] Bebendorf, M. 2008. Hierarchical matrices – A means to efficiently solve elliptic boundary value problems. Berlin: Springer.
[14] Bebendorf, M. 2011. Adaptive cross approximation of multivariate functions. Constr. Approx., 34(2), 149–179.
[15] Bebendorf, M., Maday, Y., and Stamm, B. 2014. Comparison of some reduced representation approximations. Pages 67–100 of: Reduced order methods for modeling and computational reduction. Cham: Springer.
[16] Benzi, M. 2002. Preconditioning techniques for large linear systems: a survey. J. Comput. Phys., 182(2), 418–477.
[17] Benzi, M., and Tůma, M. 1999. A comparative study of sparse approximate inverse preconditioners. Appl. Numer. Math., 30(2–3), 305–340.
[18] Benzi, M., Cullum, J. K., and Tůma, M. 2000. Robust approximate inverse preconditioning for the conjugate gradient method. SIAM J. Sci.Comput., 22(4), 1318–1332.
[19] Benzi, M., Meyer, C. D., and Tůma, M. 1996. A sparse approximate inverse preconditioner for the conjugate gradient method. SIAM J. Sci. Comput., 17(5), 1135–1149.
[20] Björck, A. 1996. Numerical methods for least squares problems. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[21] Bjöorck, A. 2015. Numerical methods in matrix computations. Cham: Springer.
[22] Boyd, S., and Vandenberghe, L. 2004. Convex optimization. Cambridge: Cambridge University Press.
[23] Brandt, A. 1977. Multi-level adaptive solutions to boundary-value problems. Math. Comp., 31(138), 333–390.
[24] Brandt, A., McCormick, S., and Ruge, J. 1985. Algebraic multigrid (AMG) for sparse matrix equations. Pages 257–284 of: Sparsity and its applications (Loughborough, 1983). Cambridge: Cambridge University Press.
[25] Brenner, S., and Scott, L. 1994. The Mathematical Theory of Finite Element Methods. 3rd edn. New York: Springer.
[26] Briggs, W., and McCormick, S. 1987. Introduction. Pages 1–30 of: Multigrid methods. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[27] Briggs, W. L., Henson, V. E., and McCormick, S. F. 2000. A multigrid tutorial. 2nd edn. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[28] Bruaset, A. M. 1995. A survey of preconditioned iterative methods. Harlow: Longman Scientific & Technical.
[29] Candès, E. J. 2006. Compressive sampling. Pages 1433–1452 of: International Congress of Mathematicians. Vol. III. Zürich: European Mathematical Society.
[30] Candès, E. J. 2008. The restricted isometry property and its implications for compressed sensing. C. R. Math. Acad. Sci. Paris, 346(9–10), 589–592.
[31] Candès, E. J., and Tao, T. 2005. Decoding by linear programming. IEEE Trans. Inform. Theory, 51(12), 4203–4215.
[32] Candès, E. J., and Wakin, M. B. 2008. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21–30.
[33] Candès, E. J., Romberg, J. K., and Tao, T. 2006. Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math., 59(8), 1207–1223.
[34] Chan, T. F., Gallopoulos, E., Simoncini, V., Szeto, T., and Tong, C. H. 1994. A quasi-minimal residual variant of the Bi-CGSTAB algorithm for nonsymmetric systems. SIAM J. Sci. Comput., 15(2), 338–347.
[35] Cherrie, J. B., Beatson, R. K., and Newsam, G. N. 2002. Fast evaluation of radial basis functions: Methods for generalised multiquadrics in Rn. SIAM J. Sci. Comput., 23, 1272–1310.
[36] Chow, E., and Saad, Y. 1998. Approximate inverse preconditioners via sparse– sparse iterations. SIAM J. Sci. Comput., 19(3), 995–1023.
[37] Coppersmith, D., and Winograd, S. 1990. Matrix multiplication via arithmetic progressions. J. Symboli. Comput., 9(3), 251–280.
[38] Cosgrove, J. D. F., Díaz, J. C., and Griewank, A. 1992. Approximate inverse preconditionings for sparse linear systems. International Journal of Computer Mathematics, 44(1–4), 91–110.
[39] Cosgrove, J. D. F., Díaz, J. C., and Macedo, Jr., C. G. 1991. Approximate inverse preconditioning for nonsymmetric sparse systems. Pages 101–111 of: Advances in numerical partial differential equations and optimization (Mérida, 1989). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[40] Cullum, J. 1996. Iterative methods for solving Ax = b, GMRES/FOM versus QMR/BiCG. Adv. Comput. Math., 6(1), 1–24.
[41] Datta, B. N. 2010. Numerical linear algebra and applications. 2nd edn. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[42] Davenport, M. A., Duarte, M. F., Eldar, Y. C., and Kutyniok, G. 2012. Introduction to compressed sensing. Pages 1–64 of: Compressed sensing. Cambridge: Cambridge University Press.
[43] de Berg, M., van Kreveld, M., Overmars, M., and Schwarzkopf, O. 1997. Computational Geometry. Berlin: Springer.
[44] Demmel, J. W. 1997. Applied numerical linear algebra. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[45] DeVore, R. A. 2007. Deterministic constructions of compressed sensing matrices. J. Complexity, 23(4–6), 918–925.
[46] Donoho, D. L. 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4), 1289–1306.
[47] Elman, H. C., Silvester, D. J., and Wathen, A. J. 2014. Finite elements and fast iterative solvers: with applications in incompressible fluid dynamics. 2nd edn. Oxford: Oxford University Press.
[48] Escalante, R., and Raydan, M. 2011. Alternating projection methods. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[49] Faber, V., and Manteuffel, T. 1984. Necessary and sufficient conditions for the existence of a conjugate gradient method. SIAM J. Numer. Anal., 21(2), 352–362.
[50] Fischer, B. 2011. Polynomial based iteration methods for symmetric linear systems. Philadelphia, PA: Society for Industrial and AppliedMathematics (SIAM). Reprint of the 1996 original.
[51] Fletcher, R. 1976. Conjugate gradient methods for indefinite systems. Pages 73–89 of: Numerical analysis (Proceedings of the 6th Biennial Dundee Conference, University of Dundee, Dundee, 1975). Berlin: Springer.
[52] Ford, W. 2015. Numerical linear algebra with applications. Amsterdam: Elsevier/ Academic Press.
[53] Fornasier, M., and Rauhut, H. 2011. Compressive sensing. Pages 187–228 of: Scherzer, O. (ed.), Handbook of Mathematical Methods in Imaging. New York: Springer.
[54] Foucart, S., and Rauhut, H. 2013. A mathematical introduction to compressive sensing. New York: Birkhäuser/Springer.
[55] Fox, L. 1964. An introduction to numerical linear algebra. Oxford: Clarendon Press.
[56] Francis, J. G. F. 1961/1962a. The QR transformation: a unitary analogue to the LR transformation. I. Comput. J., 4, 265–271.
[57] Francis, J. G. F. 1961/1962b. The QR transformation. II. Comput. J., 4, 332–345.
[58] Freund, R. W. 1992. Conjugate gradient-type methods for linear systems with complex symmetric coefficient matrices. SIAM J. Sci. Statist. Comput., 13(1), 425–448.
[59] Freund, R.W. 1993. A transpose-free quasi-minimal residual algorithm for non-Hermitian linear systems. SIAM J. Sci. Comput., 14(2), 470–482.
[60] Freund, R. W., and Nachtigal, N. M. 1991. QMR: a quasi-minimal residual method for non-Hermitian linear systems. Numer. Math., 60(3), 315–339.
[61] Freund, R.W., Gutknecht, M. H., and Nachtigal, N. M. 1993. An implementation of the look-ahead Lanczos algorithm for non-Hermitian matrices. SIAM J. Sci. Comput., 14(1), 137–158.
[62] Gasch, J., and Maligranda, L. 1994. On vector-valued inequalities of the Marcinkiewicz–Zygmund, Herz and Krivine type. Math. Nachr., 167, 95–129.
[63] Goldberg, M., and Tadmor, E. 1982. On the numerical radius and its applications. Linear Algebra Appl., 42, 263–284.
[64] Golub, G., and Kahan, W. 1965. Calculating the singular values and pseudoinverse of a matrix. J. Soc. Indust. Appl. Math. Ser. B Numer. Anal., 2, 205–224.
[65] Golub, G. H., and Reinsch, C. 1970. Singular value decomposition and least squares solutions. Numer. Math., 14(5), 403–420.
[66] Golub, G. H., and Van Loan, C. F. 2013. Matrix computations. 4th edn. Baltimore, MD: Johns Hopkins University Press.
[67] Golub, G. H., Heath, M., and Wahba, G. 1979. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21(2), 215–223.
[68] Gould, N. I. M., and Scott, J. A. 1998. Sparse approximate-inverse preconditioners using norm-minimization techniques. SIAM J. Sci. Comput., 19(2), 605–625.
[69] Greenbaum, A. 1997. Iterative methods for solving linear systems. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[70] Griebel, M. 1994. Multilevelmethoden als Iterationsverfahren über Erzeugendensystemen. Stuttgart: B. G. Teubner.
[71] Griebel, M., and Oswald, P. 1995. On the abstract theory of additive and multiplicative Schwarz algorithms. Numer. Math., 70(2), 163–180.
[72] Grote, M. J., and Huckle, T. 1997. Parallel preconditioning with sparse approximate inverses. SIAM J. Sci. Comput., 18(3), 838–853.
[73] Gutknecht, M. H. 2007. A brief introduction to Krylov space methods for solving linear systems. Pages 53–62 of: Kaneda, Y., Kawamura, H., and Sasai, M. (eds.), Frontiers of Computational Science. Berlin: Springer.
[74] Hackbusch, W. 1985. Multi-grid methods and applications. Berlin: Springer.
[75] Hackbusch, W. 1994. Iterative solution of large sparse systems of equations. New York: Springer. Translated and revised from the 1991 German original.
[76] Hackbusch, W. 1999. A sparse matrix arithmetic based on H-matrices. I. Introduction to H-matrices. Computing, 62(2), 89–108.
[77] Hackbusch, W. 2015. Hierarchical matrices: algorithms and analysis. Heidelberg: Springer.
[78] Hackbusch, W., and Börm, S. 2002. Data-sparse approximation by adaptive H2- matrices. Computing., 69(1), 1–35.
[79] Hackbusch, W., Grasedyck, L., and Börm, S. 2002. An introduction to hierarchical matrices. Mathematic. Bohemica, 127(2), 229–241.
[80] Hackbusch, W., Khoromskij, B., and Sauter, S. A. 2000. On H2-matrices. Pages 9–29 of: Lectures on applied mathematics (Munich, 1999). Berlin: Springer.
[81] Hansen, P. C. 1992. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Rev., 34(4), 561–580.
[82] Henrici, P. 1958. On the speed of convergence of cyclic and quasicyclic Jacobi methods for computing eigenvalues of Hermitian matrices. J. Soc. Indust. Appl. Math., 6, 144–162.
[83] Hestenes, M. R., and Stiefel, E. 1952. Methods of conjugate gradients for solving linear systems. J. Research Nat. Bur. Standards, 49, 409–436 (1953).
[84] Higham, N. J. 1990. Exploiting fast matrix multiplication within the level 3 BLAS. ACM Trans. Math. Software, 16(4), 352–368.
[85] Higham, N. J. 2002. Accuracy and stability of numerical algorithms. 2nd edn. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[86] Householder, A. S. 1958. Unitary triangularization of a nonsymmetric matrix. J. Assoc. Comput. Mach., 5, 339–342.
[87] Kolotilina, L. Y., and Yeremin, A. Y. 1993. Factorized sparse approximate inverse preconditionings. I. Theory. SIAM J. Matrix Anal. Appl., 14(1), 45–58.
[88] Krasny, R., and Wang, L. 2011. Fast evaluation of multiquadric RBF sums by a Cartesian treecode. SIAM J. Sci. Comput., 33(5), 2341–2355.
[89] Lanczos, C. 1950. An iteration method for the solution of the eigenvalue problem of linear differential and integral operators. J. Research Nat. Bur. Standards, 45, 255–282.
[90] Lanczos, C. 1952. Solution of systems of linear equations by minimizediterations. J. Research Nat. Bur. Standards, 49, 33–53.
[91] Le Gia, Q. T., and Tran, T. 2010. An overlapping additive Schwarz preconditioner for interpolation on the unit sphere with spherical basis functions. Journal of Complexity, 26, 552–573.
[92] Liesen, J., and Strakoš, Z. 2013. Krylov subspace methods – Principles and analysis. Oxford: Oxford University Press.
[93] Maligranda, L. 1997. On the norms of operators in the real and the complex case. Pages 67–71 of: Proceedings of the Second Seminar on Banach Spaces and Related Topics. Kitakyushu: Kyushu Institute of Technology.
[94] Meijerink, J. A., and van der Vorst, H. A. 1977. An iterative solution method for linear systems of which the coefficient matrix is a symmetric M-matrix. Math. Comp., 31(137), 148–162.
[95] Meister, A. 1999. Numerik linearer Gleichungssysteme – Eine Einführung in moderne Verfahren. Braunschweig: Friedrich Vieweg & Sohn.
[96] Meister, A., and Vömel, C. 2001. Efficient preconditioning of linear systems arising from the discretization of hyperbolic conservation laws. Adv. Comput. Math., 14(1), 49–73.
[97] Morozov, V. A. 1984. Methods for solving incorrectly posed problems. New York: Springer. Translated from the Russian by A. B. Aries, Translation edited by Z. Nashed.
[98] Ostrowski, A. M. 1959. A quantitative formulation of Sylvester's law of inertia. Proc. Nat. Acad. Sci. U.S.A., 45, 740–744.
[99] Paige, C. C., and Saunders, M. A. 1975. Solutions of sparse indefinite systems of linear equations. SIAM J. Numer. Anal., 12(4), 617–629.
[100] Parlett, B. N. 1998. The symmetric eigenvalue problem. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM). Corrected reprint of the 1980 original.
[101] Pearcy, C. 1966. An elementary proof of the power inequality for the numerical radius. Michigan Math. J., 13, 289–291.
[102] Quarteroni, A., and Valli, A. 1999. Domain decomposition methods for partial differential equations. New York: Clarendon Press.
[103] Saad, Y. 1994. Highly parallel preconditioners for general sparse matrices. Pages 165–199 of: Recent advances in iterative methods. New York: Springer.
[104] Saad, Y. 2003. Iterative methods for sparse linear systems. 2nd edn. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[105] Saad, Y., and Schultz, M. H. 1986. GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Sci. Statist. Comput., 7(3), 856–869.
[106] Saad, Y., and van der Vorst, H. A. 2000. Iterative solution of linear systems in the 20th century. J. Comput. Appl. Math., 123(1-2), 1–33.
[107] Schaback, R., and Wendland, H. 2005. Numerische Mathematik. 5th edn. Berlin: Springer.
[108] Schatzman, M. 2002. Numerical Analysis: A Mathematical Introduction. Oxford: Oxford University Press.
[109] Simoncini, V., and Szyld, D. B. 2002. Flexible inner–outer Krylov subspace methods. SIAM J. Numer. Anal., 40(6), 2219–2239.
[110] Simoncini, V., and Szyld, D. B. 2005. On the occurrence of superlinear convergence of exact and inexact Krylov subspace methods. SIAM Rev., 47(2), 247–272.
[111] Simoncini, V., and Szyld, D. B. 2007. Recent computational developments in Krylov subspace methods for linear systems. Numer. Linear Algebr. Appl., 14(1), 1–59.
[112] Sleijpen, G. L. G., and Fokkema, D. R. 1993. BiCGstab(l) for linear equations involving unsymmetric matrices with complex spectrum. Electron. Trans. Numer. Anal., 1(Sept.), 11–32 (electronic only).
[113] Smith, K. T., Solmon, D. C., and Wagner, S. L. 1977. Practical and mathematical aspects of the problem of reconstructing objects from radiographs. Bull. Amer. Math. Soc., 83, 1227–1270.
[114] Sonneveld, P. 1989. CGS, a fast Lanczos-type solver for nonsymmetric linear systems. SIAM J. Sci. Statist. Comput., 10(1), 36–52.
[115] Sonneveld, P., and van Gijzen, M. B. 2008/09. IDR(s): a family of simple and fast algorithms for solving large nonsymmetric systems of linear equations. SIAM J. Sci. Comput., 31(2), 1035–1062.
[116] Steinbach, O. 2005. Lösungsverfahren für lineare Gleichungssysteme. Wiesbaden: Teubner.
[117] Stewart, G. W. 1998. Matrix algorithms. Volume I: Basic decompositions. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[118] Stewart, G.W. 2001. Matrix algorithms. Volume II: Eigensystems. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[119] Stewart, G. W., and Sun, J. G. 1990. Matrix perturbation theory. Boston, MA: Academic Press.
[120] Strassen, V. 1969. Gaussian elimination is not optimal. Numer. Math., 13, 354–356.
[121] Süli, E., and Mayers, D. F. 2003. An introduction to numerical analysis. Cambridge: Cambridge University Press.
[122] Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B, 58(1), 267–288.
[123] Toselli, A., and Widlund, O. 2005. Domain decomposition methods – algorithms and theory. Berlin: Springer.
[124] Trefethen, L. N., and Bau, III, D. 1997. Numerical linear algebra. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
[125] Trottenberg, U., Oosterlee, C. W., and Schüller, A. 2001. Multigrid. San Diego, CA: Academic Press. With contributions by A. Brandt P., Oswald and K. Stüben.
[126] Van de Velde, E. F. 1994. Concurrent scientific computing. New York: Springer.
[127] van der Vorst, H. A. 1992. Bi-CGSTAB: a fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems. SIAM J. Sci. Statist. Comput., 13(2), 631–644.
[128] van der Vorst, H. A. 2009. Iterative Krylov methods for large linear systems. Cambridge: Cambridge University Press. Reprint of the 2003 original.
[129] Varga, R. S. 2000. Matrix iterative analysis. Expanded edn. Berlin: Springer.
[130] Wathen, A. 2007. Preconditioning and convergence in the right norm. Int. J. Comput. Math., 84(8), 1199–1209.
[131] Wathen, A. J. 2015. Preconditioning. Acta Numer., 24, 329–376.
[132] Watkins, D. S. 2010. Fundamentals of matrix computations. 3rd edn. Hoboken, NJ: John Wiley & Sons.
[133] Wendland, H. 2005. Scattered data approximation. Cambridge: Cambridge University Press.
[134] Werner, J. 1992a. Numerische Mathematik. Band 1: Lineare und nichtlineare Gleichungssysteme, Interpolation, numerische Integration. Braunschweig: Friedrich Vieweg & Sohn.
[135] Werner, J. 1992b. Numerische Mathematik. Band 2: Eigenwertaufgaben, lineare Optimierungsaufgaben, unrestringierte Optimierungsaufgaben. Braunschweig: Friedrich Vieweg & Sohn.
[136] Wimmer, H. K. 1983. On Ostrowski's generalization of Sylvester's law of inertia. Linear Algebra Appl., 52/53, 739–741.
[137] Xu, J. 1992. Iterative methods by space decomposition and subspace correction. SIAM Rev., 34(4), 581–613.
[138] Yin, W., Osher, S., Goldfarb, D., and Darbon, J. 2008. Bregman iterative algorithms for l1-minimization with applications to compressed sensing. SIAM J. Imagin. Sci., 1(1), 143–168.
[139] Young, D. M. 1970. Convergence properties of the symmetric and unsymmetric successive overrelaxation methods and related methods. Math. Comp., 24, 793–807.
[140] Young, D. M. 1971. Iterative Solution of Large Linear Systems. New York: Academic Press.


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.