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 354 articles , 1 standard article )

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  1. Bardenet, Rémi; Flamant, Julien; Chainais, Pierre: On the zeros of the spectrogram of white noise (2020)
  2. Bardenet, Rémi; Hardy, Adrien: Monte Carlo with determinantal point processes (2020)
  3. Choi, Hee-Sun; Kim, Jin-Gyun; Doostan, Alireza; Park, K. C.: Acceleration of uncertainty propagation through Lagrange multipliers in partitioned stochastic method (2020)
  4. Dominici, Diego: Matrix factorizations and orthogonal polynomials (2020)
  5. Glaubitz, Jan; Öffner, Philipp: Stable discretisations of high-order discontinuous Galerkin methods on equidistant and scattered points (2020)
  6. Hascelik, A. Ihsan: Efficient computation of highly oscillatory Fourier-type integrals with monomial phase functions and Jacobi-type singularities (2020)
  7. Im, Jongho; Morikawa, Kosuke; Ha, Hyung-Tae: A least squares-type density estimator using a polynomial function (2020)
  8. Iserles, Arieh; Webb, Marcus: A family of orthogonal rational functions and other orthogonal systems with a skew-Hermitian differentiation matrix (2020)
  9. Jahanbin, Ramin; Rahman, Sharif: Stochastic isogeometric analysis in linear elasticity (2020)
  10. Karvonen, Toni; Särkkä, Simo: Worst-case optimal approximation with increasingly flat Gaussian kernels (2020)
  11. Kubínová, Marie; Pultarová, Ivana: Block preconditioning of stochastic Galerkin problems: new two-sided guaranteed spectral bounds (2020)
  12. Wang, Xiaolong; Jiang, Yaolin: Time domain model reduction of time-delay systems via orthogonal polynomial expansions (2020)
  13. Alqahtani, Hessah; Reichel, Lothar: Generalized block anti-Gauss quadrature rules (2019)
  14. Álvarez-Vizoso, J.; Arn, Robert; Kirby, Michael; Peterson, Chris; Draper, Bruce: Geometry of curves in (\mathbbR^n) from the local singular value decomposition (2019)
  15. Bespalov, Alex; Praetorius, Dirk; Rocchi, Leonardo; Ruggeri, Michele: Goal-oriented error estimation and adaptivity for elliptic PDEs with parametric or uncertain inputs (2019)
  16. Calabrò, F.; Bravo, D.; Carissimo, C.; Di Fazio, E.; Di Pasquale, A.; Eldray, A. A. M. O.; Fabrizi, C.; Gerges, J. G. S.; Palazzo, S.; Wassef, J. F. F. T.: Null rules for the detection of lower regularity of functions (2019)
  17. Djukić, Dušan Lj.; Reichel, Lothar; Spalević, Miodrag M.; Tomanović, Jelena D.: Internality of generalized averaged Gaussian quadrature rules and truncated variants for modified Chebyshev measures of the second kind (2019)
  18. Durastante, Fabio: Efficient solution of time-fractional differential equations with a new adaptive multi-term discretization of the generalized Caputo-Dzherbashyan derivative (2019)
  19. Erfani, S.; Babolian, E.; Javadi, S.; Shamsi, M.: Stable evaluations of fractional derivative of the Müntz-Legendre polynomials and application to fractional differential equations (2019)
  20. Fermo, Luisa; Russo, Maria Grazia; Serafini, Giada: Numerical methods for Cauchy bisingular integral equations of the first kind on the square (2019)

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