A Product Integration type Method for solving Nonlinear Integral Equations in L

This paper deals with nonlinear Fredholm integral equations of the second kind. We study the case of a weakly singular kernel and we set the problem in the space L 1 ([a, b], C). As numerical method, we extend the product integration scheme from C 0 ([a, b], C) to L 1 ([a, b], C)

A note on spectral approximation of linear operations

AbstractThis work deals on sufficient conditions for the spectral convergence of a sequence of linear operators. The general context is a complex separable Banach space and the pointwise limit of the sequence is a continuous linear operator which is not supposed to be compact. By spectral convergence is meant the self-range-uniform convergence of the approximate spectral projections. This implies the gap convergence of the approximate maximal invariant subspaces to those of the limit operator corresponding to a nonzero isolated eigenvalue (or a subset of close nonzero isolated eigenvalues) with finite algebraic multiplicity. Neither the exact nor the approximate eigenvalues are supposed to be semisimple

A Jacobi-Davidson type method with a correction equation tailored for integral operators

The final publication is available at Springer via http://dx.doi.org/10.1007/s11075-012-9656-9We propose two iterative numerical methods for eigenvalue computations of large dimensional problems arising from finite approximations of integral operators, and describe their parallel implementation. A matrix representation of the problem on a space of moderate dimension, defined from an infinite dimensional one, is computed along with its eigenpairs. These are taken as initial approximations and iteratively refined, by means of a correction equation based on the reduced resolvent operator and performed on the moderate size space, to enhance their quality. Each refinement step requires the prolongation of the correction equation solution back to a higher dimensional space, defined from the infinite dimensional one. This approach is particularly adapted for the computation of eigenpair approximations of integral operators, where prolongation and restriction matrices can be easily built making a bridge between coarser and finer discretizations. We propose two methods that apply a Jacobi–Davidson like correction: Multipower Defect-Correction (MPDC), which uses a single-vector scheme, if the eigenvalues to refine are simple, and Rayleigh–Ritz Defect-Correction (RRDC), which is based on a projection onto an expanding subspace. Their main advantage lies in the fact that the correction equation is performed on a smaller space while for general solvers it is done on the higher dimensional one. We discuss implementation and parallelization details, using the PETSc and SLEPc packages. Also, numerical results on an astrophysics application, whose mathematical model involves a weakly singular integral operator, are presented.This work was partially supported by European Regional Development Fund through COMPETE, FCT-Fundacao para a Ciencia e a Tecnologia through CMUP-Centro de Matematica da Universidade do Porto and Spanish Ministerio de Ciencia e Innovacion under projects TIN2009-07519 and AIC10-D-000600.Vasconcelos, PB.; D'almeida, FD.; Román Moltó, JE. (2013). A Jacobi-Davidson type method with a correction equation tailored for integral operators. Numerical Algorithms. 64(1):85-103. doi:10.1007/s11075-012-9656-9S85103641Absil, P.A., Mahony, R., Sepulchre, R., Dooren, P.V.: A Grassmann–Rayleigh quotient iteration for computing invariant subspaces. SIAM Rev. 44(1), 57–73 (2002)Ahues, M., Largillier, A., Limaye, B.V.: Spectral Computations with Bounded Operators. Chapman and Hall, Boca Raton (2001)Ahues, M., d’Almeida, F.D., Largillier, A., Titaud, O., Vasconcelos, P.: An L 1 refined projection approximate solution of the radiation transfer equation in stellar atmospheres. J. Comput. Appl. Math. 140(1–2), 13–26 (2002)Ahues, M., d’Almeida, F.D., Largillier, A., Vasconcelos, P.B.: Defect correction for spectral computations for a singular integral operator. Commun. Pure Appl. Anal. 5(2), 241–250 (2006)Bai, Z., Demmel, J., Dongarra, J., Ruhe, A., van der Vorst, H. (eds.): Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. Society for Industrial and Applied Mathematics, Philadelphia (2000)Balay, S., Buschelman, K., Eijkhout, V., Gropp, W.D., Kaushik, D., Knepley, M., McInnes, L.C., Smith, B.F., Zhang, H.: PETSc Users Manual. Tech. Rep. ANL-95/11 - Revision 3.1, Argonne National Laboratory (2010)Chatelin, F.: Spectral Approximation of Linear Operators. SIAM, Philadelphia (2011)d’Almeida, F.D., Vasconcelos, P.B.: Convergence of multipower defect correction for spectral computations of integral operators. Appl. Math. Comput. 219(4), 1601–1606 (2012)Falgout, R.D., Yang, U.M.: Hypre: A library of high performance preconditioners. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A.G. (eds.) Computational Science - ICCS 2002, International Conference, Amsterdam, The Netherlands, April 21–24, 2002. Proceedings, Part III, Lecture Notes in Computer Science, vol. 2331, pp. 632–641. Springer (2002)Henson, V.E., Yang, U.M.: BoomerAMG: A parallel algebraic multigrid solver and preconditioner. Appl. Numer. Math. 41(1), 155–177 (2002)Hernandez, V., Roman, J.E., Vidal, V.: SLEPc: A scalable and flexible toolkit for the solution of eigenvalue problems. ACM Trans. Math. Softw. 31(3), 351–362 (2005)Hernandez, V., Roman, J.E., Tomas, A., Vidal, V.: SLEPc Users Manual. Tech. Rep. DSIC-II/24/02 - Revision 3.1, D. Sistemas Informáticos y Computación, Universidad Politécnica de Valencia (2010)Saad, Y.: Iterative methods for sparse linear systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2003)Simoncini, V., Eldén, L.: Inexact Rayleigh quotient-type methods for eigenvalue computations. BIT 42(1), 159–182 (2002)Sleijpen, G.L.G., van der Vorst, H.A.: A Jacobi–Davidson iteration method for linear eigenvalue problems. SIAM Rev. 42(2), 267–293 (2000)Sorensen, D.C.: Implicit application of polynomial filters in a k-step Arnoldi method. SIAM J. Matrix Anal. Appl. 13, 357–385 (1992)Stewart, G.W.: A Krylov–Schur algorithm for large eigenproblems. SIAM J. Matrix Anal. Appl. 23(3), 601–614 (2001

Computation of spectral subspaces for weakly singular integral operators

This paper deals with finding bases for finite-dimensional spectral subspaces of a bounded operator on the linear space of all complex-valued continuous functions defined on a compact Hausdorff space. This goal is achieved by computing an exact basis for a spectral subspace of an approximate operator which is not of finite rank. The theoretical framework allows a wide class of approximations, and a special emphasis is given to Kantorovich's singularity subtraction discretization of weakly singular compact integral operators. An application to Hopf's operator in the context of the transfer equation in stellar atmospheres illustrates the numerical computation of a bidimensional spectral subspace corresponding to a cluster of eigenvalues

An L 1-Product-Integration Method in Astrophysics

International audienc

A NOTE ON ITERATIVE REFINEMENT SCHEMES FOR SYLVESTER OPERATOR-EQUATIONS

We propose two iterative schemes to refine an approximate solution of a Sylvester operator equation Kx underbar - xtheta = y, where K is a bounded linear operator in a Banach space B, K underbar its extension to the product space X = B(m), and theta is-an-element-of C(m x m). An approximate solution x(n) is obtained by means of an approximation K(n) to K. Then, x(n) is refined by two iterative processes involving the resolution, for e(n), of K(n)e(n)underbar - e(n)theta = r(n), with different second members r(n). In these processes, K is only used for evaluations

Iterated Kantorovich versus Kulkarni method for Fredholm integral equations

From all standard projection approximations of a bounded linear operator in a Banach space, iterated Kantorovich and Kulkarni’s discretization exhibit a global superconvergent error bound. In this work we compare these discretizations, focusing on the implementation details and the computational cost, when applied to a Fredholm integral equation of the second kind with weakly singular kernel.The first author was partially supported by CMat (UID/MAT/00013/2013) and the second author was partially supported by CMUP (UID/MAT/00144/2013), which are funded by FCT (Portugal) with national (MEC) and European structural funds (FEDER), under the partnership agreement PT2020.info:eu-repo/semantics/publishedVersio

Error bounds for low-rank approximations of the first exponential integral kernel

A hierarchical matrix is an efficient data-sparse representation of a matrix, especially useful for large dimensional problems. It consists of low-rank subblocks leading to low memory requirements as well as inexpensive computational costs. In this work, we discuss the use of the hierarchical matrix technique in the numerical solution of a large scale eigenvalue problem arising from a finite rank discretization of an integral operator. The operator is of convolution type, it is defined through the first exponential-integral function and, hence, it is weakly singular. We develop analytical expressions for the approximate degenerate kernels and deduce error upper bounds for these approximations. Some computational results illustrating the efficiency and robustness of the approach are presented.This work was partially supported by CRUP-Acções Universitárias Integradas Luso-Francesas PAUILF 2011 under project F-TCO3/11 and by PROTEC from FCT under project SFRH/BD/49394/2009