OPQ

Orthogonal polynomials. Computation and approximation. Orthogonal polynomials are a widely used class of mathematical functions that are helpful in the solution of many important technical problems. This book provides, for the first time, a systematic development of computational techniques, including a suite of computer programs in Matlab downloadable from the Internet, to generate orthogonal polynomials of a great variety: OPQ: A MATLAB SUITE OF PROGRAMS FOR GENERATING ORTHOGONAL POLYNOMIALS AND RELATED QUADRATURE RULES.


References in zbMATH (referenced in 182 articles , 1 standard article )

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  1. Andreev, Roman: On long time integration of the heat equation (2016)
  2. Bespalov, Alex; Silvester, David: Efficient adaptive stochastic Galerkin methods for parametric operator equations (2016)
  3. Bigoni, Daniele; Engsig-Karup, Allan P.; Marzouk, Youssef M.: Spectral tensor-train decomposition (2016)
  4. da Rocha, Zélia: A general method for deriving some semi-classical properties of perturbed second degree forms: the case of the Chebyshev form of second kind (2016)
  5. Deaño, Alfredo; Huertas, Edmundo J.; Román, Pablo: Asymptotics of orthogonal polynomials generated by a Geronimus perturbation of the Laguerre measure (2016)
  6. Djukić, Dušan Lj.; Reichel, Lothar; Spalević, Miodrag M.: Truncated generalized averaged Gauss quadrature rules (2016)
  7. Druskin, Vladimir; Mamonov, Alexander V.; Thaler, Andrew E.; Zaslavsky, Mikhail: Direct, nonlinear inversion algorithm for hyperbolic problems via projection-based model reduction (2016)
  8. Frommer, Andreas; Schweitzer, Marcel: Error bounds and estimates for Krylov subspace approximations of Stieltjes matrix functions (2016)
  9. Mahdian, M.; Arjmandi, M.B.; Marahem, F.: Chain mapping approach of Hamiltonian for FMO complex using associated, generalized and exceptional Jacobi polynomials (2016)
  10. Mittal, A.; Chen, X.; Tong, A.H.; Iaccarino, G.: A flexible uncertainty propagation framework for general multiphysics systems (2016)
  11. Mustonen, Lauri: Numerical study of a parametric parabolic equation and a related inverse boundary value problem (2016)
  12. Ozen, H.Cagan; Bal, Guillaume: Dynamical polynomial chaos expansions and long time evolution of differential equations with random forcing (2016)
  13. Reichel, Lothar; Spalević, Miodrag M.; Tang, Tunan: Generalized averaged Gauss quadrature rules for the approximation of matrix functionals (2016)
  14. Spalević, M.M.; Cvetković, A.S.: Estimating the error of Gaussian quadratures with simple and multiple nodes by using their extensions with multiple nodes (2016)
  15. Woods, M.P.; Plenio, M.B.: Dynamical error bounds for continuum discretisation via Gauss quadrature rules -- A Lieb-Robinson bound approach (2016)
  16. Xu, Zhenhua; Milovanović, Gradimir V.: Efficient method for the computation of oscillatory Bessel transform and Bessel Hilbert transform (2016)
  17. Bellalij, M.; Reichel, L.; Rodriguez, G.; Sadok, H.: Bounding matrix functionals via partial global block Lanczos decomposition (2015)
  18. Berriochoa Esnaola, E.; Cachafeiro López, A.; Cala Rodríguez, F.; Illán González, J.; Rebollido Lorenzo, J.M.: Gauss rules associated with nearly singular weights (2015)
  19. Calabrò, Francesco; Manni, Carla; Pitolli, Francesca: Computation of quadrature rules for integration with respect to refinable functions on assigned nodes (2015)
  20. De Marchi, S.; Dell’Accio, F.; Mazza, M.: On the constrained mock-Chebyshev least-squares (2015)

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