Octave

GNU Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command line interface for solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with Matlab. It may also be used as a batch-oriented language. Octave has extensive tools for solving common numerical linear algebra problems, finding the roots of nonlinear equations, integrating ordinary functions, manipulating polynomials, and integrating ordinary differential and differential-algebraic equations. It is easily extensible and customizable via user-defined functions written in Octave’s own language, or using dynamically loaded modules written in C++, C, Fortran, or other languages.

This software is also referenced in ORMS.


References in zbMATH (referenced in 176 articles , 1 standard article )

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  1. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  2. Cuong, Dao Huy; Thanh, Mai Duc: A high-resolution van Leer-type scheme for a model of fluid flows in a nozzle with variable cross-section (2017)
  3. Fu, Ying; Turinici, Gabriel: Quantum Hamiltonian and dipole moment identification in presence of large control perturbations (2017)
  4. Quarteroni, Alfio; Saleri, Fausto; Gervasio, Paola: Scientific computing. Exercises and solved problems with MATLAB and Octave. (to appear) (2017)
  5. Rackl, Michael; Hanley, Kevin J.; Günthner, Willibald A.: Verification of an automated work flow for discrete element material parameter calibration (2017)
  6. Rogers, Simon; Girolami, Mark: A first course in machine learning (2017)
  7. Russell, Stephen; Madden, Niall: An introduction to the analysis and implementation of sparse grid finite element methods (2017)
  8. Sangwine, Stephen J.; Hitzer, Eckhard: Clifford multivector toolbox (for MATLAB) (2017)
  9. Sauer, Tomas: Prony’s method in several variables (2017)
  10. Ala-Luhtala, Juha; Piché, Robert: Gaussian scale mixture models for robust linear multivariate regression with missing data (2016)
  11. Aşici, Emel; Karaçal, Funda: Incomparability with respect to the triangular order. (2016)
  12. Babolian, Esmail; Javadi, Shahnam; Moradi, Eslam: Error analysis of reproducing kernel Hilbert space method for solving functional integral equations (2016)
  13. Beyrami, Hossein; Lotfi, Taher; Mahdiani, Katayoun: A new efficient method with error analysis for solving the second kind Fredholm integral equation with Cauchy kernel (2016)
  14. Blanes, Sergio; Casas, Fernando: A concise introduction to geometric numerical integration (2016)
  15. Boček, Pavel; Šiman, Miroslav: Directional quantile regression in Octave (and MATLAB). (2016)
  16. Botchev, Mikhail A.: Krylov subspace exponential time domain solution of Maxwell’s equations in photonic crystal modeling (2016)
  17. Boulton, Lyonell: Spectral pollution and eigenvalue bounds (2016)
  18. Cuvelier, François; Japhet, Caroline; Scarella, Gilles: An efficient way to assemble finite element matrices in vector languages (2016)
  19. Davis, Jon H.: Methods of applied mathematics with a software overview (2016)
  20. Jensen, Kristian Ejlebjerg; Gorman, Gerard: Details of tetrahedral anisotropic mesh adaptation (2016)

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