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 202 articles , 1 standard article )

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  1. Deschner, Stephan C.; Illenseer, Tobias F.; Duschl, Wolfgang J.: Self-similar solutions to isothermal shock problems (2018)
  2. Batselier, Kim; Wong, Ngai: Inverse multivariate polynomial root-finding: numerical implementations of the affine and projective Buchberger-Möller algorithm (2017)
  3. Bean, Maranda; Lipnikov, Konstantin; Yi, Son-Young: A block-diagonal preconditioner for a four-field mixed finite element method for Biot’s equations (2017)
  4. Beyrami, Hossein; Lotfi, Taher; Mahdiani, Katayoun: Stability and error analysis of the reproducing kernel Hilbert space method for the solution of weakly singular Volterra integral equation on graded mesh (2017)
  5. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  6. Boček, Pavel; Šiman, Miroslav: On weighted and locally polynomial directional quantile regression (2017)
  7. 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)
  8. Fu, Ying; Turinici, Gabriel: Quantum Hamiltonian and dipole moment identification in presence of large control perturbations (2017)
  9. Güttel, Stefan; Tisseur, Françoise: The nonlinear eigenvalue problem (2017)
  10. Montúfar, Guido; Rauh, Johannes: Hierarchical models as marginals of hierarchical models (2017)
  11. Nerovny, Nikolay; Zimin, Vladimir; Fedorchuk, Sergey; Golubev, Evgeny: Representation of light pressure resultant force and moment as a tensor series (2017)
  12. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  13. Quarteroni, Alfio; Saleri, Fausto; Gervasio, Paola: Scientific computing. Exercises and solved problems with MATLAB and Octave (2017)
  14. Rackl, Michael; Hanley, Kevin J.; Günthner, Willibald A.: Verification of an automated work flow for discrete element material parameter calibration (2017)
  15. Rogers, Simon; Girolami, Mark: A first course in machine learning (2017)
  16. Russell, Stephen; Madden, Niall: An introduction to the analysis and implementation of sparse grid finite element methods (2017)
  17. Sangwine, Stephen J.; Hitzer, Eckhard: Clifford multivector toolbox (for MATLAB) (2017)
  18. Sauer, Tomas: Prony’s method in several variables (2017)
  19. Stetsyuk, Petr Ivanovich: Subgradient methods ralgb5 and ralgb4 for minimization of ravine-like convex functions (2017)
  20. Ala-Luhtala, Juha; Piché, Robert: Gaussian scale mixture models for robust linear multivariate regression with missing data (2016)

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