ARPACK

ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems. The package is designed to compute a few eigenvalues and corresponding eigenvectors of a general n by n matrix A. It is most appropriate for large sparse or structured matrices A where structured means that a matrix-vector product w <- Av requires order n rather than the usual order n2 floating point operations. This software is based upon an algorithmic variant of the Arnoldi process called the Implicitly Restarted Arnoldi Method (IRAM). When the matrix A is symmetric it reduces to a variant of the Lanczos process called the Implicitly Restarted Lanczos Method (IRLM). These variants may be viewed as a synthesis of the Arnoldi/Lanczos process with the Implicitly Shifted QR technique that is suitable for large scale problems. For many standard problems, a matrix factorization is not required. Only the action of the matrix on a vector is needed. ARPACK software is capable of solving large scale symmetric, nonsymmetric, and generalized eigenproblems from significant application areas. The software is designed to compute a few (k) eigenvalues with user specified features such as those of largest real part or largest magnitude. Storage requirements are on the order of n*k locations. No auxiliary storage is required. A set of Schur basis vectors for the desired k-dimensional eigen-space is computed which is numerically orthogonal to working precision. Numerically accurate eigenvectors are available on request.


References in zbMATH (referenced in 631 articles , 1 standard article )

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  1. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  2. Arndt, Daniel; Bangerth, Wolfgang; Davydov, Denis; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, version 8.5 (2017)
  3. Canton, J.; Auteri, F.; Carini, M.: Linear global stability of two incompressible coaxial jets (2017)
  4. Che, Maolin; Li, Guoyin; Qi, Liqun; Wei, Yimin: Pseudo-spectra theory of tensors and tensor polynomial eigenvalue problems (2017)
  5. Embree, Mark; Keeler, Blake: Pseudospectra of matrix pencils for transient analysis of differential-algebraic equations (2017)
  6. Gomes, F.M.; Martínez, J.M.; Raydan, M.: On the computation of large-scale self-consistent-field iterations (2017)
  7. Guglielmi, Nicola; Lubich, Christian; Mehrmann, Volker: On the nearest singular matrix pencil (2017)
  8. Guglielmi, Nicola; Rehman, Mutti-Ur; Kressner, Daniel: A novel iterative method to approximate structured singular values (2017)
  9. Haferssas, R.; Jolivet, P.; Nataf, F.: An additive Schwarz method type theory for Lions’s algorithm and a symmetrized optimized restricted additive Schwarz method (2017)
  10. Haferssas, Ryadh; Jolivet, Pierre; Nataf, Frédéric: An adaptive coarse space for P. L. Lions algorithm and optimized Schwarz methods (2017)
  11. Hall, Edward; Houston, Paul; Murphy, Steven: $hp$-adaptive discontinuous Galerkin methods for neutron transport criticality problems (2017)
  12. Heil, Matthias; Rosso, Jordan; Hazel, Andrew L.; Brøns, Morten: Topological fluid mechanics of the formation of the Kármán-vortex street (2017)
  13. Horger, Thomas; Wohlmuth, Barbara; Dickopf, Thomas: Simultaneous reduced basis approximation of parameterized elliptic eigenvalue problems (2017)
  14. Kahl, K.; Rittich, H.: The deflated conjugate gradient method: convergence, perturbation and accuracy (2017)
  15. Lee, Seungwoo; Kwak, Do Y.; Sim, Imbo: Immersed finite element method for eigenvalue problem (2017)
  16. Lu, Ding; Vandereycken, Bart: Criss-cross type algorithms for computing the real pseudospectral abscissa (2017)
  17. Noschese, Silvia; Reichel, Lothar: Approximated structured pseudospectra. (2017)
  18. Wen, Zaiwen; Zhang, Yin: Accelerating convergence by augmented Rayleigh-Ritz projections for large-scale eigenpair computation (2017)
  19. Wu, Lingfei; Romero, Eloy; Stathopoulos, Andreas: PRIMME_SVDS: a high-performance preconditioned SVD solver for accurate large-scale computations (2017)
  20. Zwaan, Ian N.; Hochstenbach, Michiel E.: Krylov-Schur-type restarts for the two-sided Arnoldi method (2017)

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