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 247 articles , 1 standard article )

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  1. Ciripoi, Daniel; Löhne, Andreas; Weißing, Benjamin: Calculus of convex polyhedra and polyhedral convex functions by utilizing a multiple objective linear programming solver (2019)
  2. David Navarro-Gonzalez; Andreu Vigil-Colet; Pere Ferrando; Urbano Lorenzo-Seva: Psychological Test Toolbox: A New Tool to Compute Factor Analysis Controlling Response Bias (2019) not zbMATH
  3. Erofeev, K. Yu.; Khramchenkov, E. M.; Biryal’tsev, E. V.: High-performance processing of covariance matrices using GPU computations (2019)
  4. J. Dölz, H. Harbrecht, S. Kurz, M. Multerer, S. Schöps, F. Wolf: Bembel: The Fast Isogeometric Boundary Element C++ Library for Laplace, Helmholtz, and Electric Wave Equation (2019) arXiv
  5. Lie, Knut-Andreas: An introduction to reservoir simulation using MATLAB/GNU Octave. User guide for the MATLAB reservoir simulation toolbox (MRST) (2019)
  6. Mahboubi, Assia; Melquiond, Guillaume; Sibut-Pinote, Thomas: Formally verified approximations of definite integrals (2019)
  7. Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
  8. O. R. Bingol, A. Krishnamurthy: NURBS-Python: An open-source object-oriented NURBS modeling framework in Python (2019) not zbMATH
  9. Peña, Juan Manuel; Sauer, Tomas: SVD update methods for large matrices and applications (2019)
  10. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  11. Wang, Lizhi; Nikouei Mehr, Maryam: An optimization approach to epistasis detection (2019)
  12. Yang, Lihong; Chen, Zhong; Xie, Kechao: An efficient method for approximate solution of a singular integral equation with Cauchy kernel (2019)
  13. Deschner, Stephan C.; Illenseer, Tobias F.; Duschl, Wolfgang J.: Self-similar solutions to isothermal shock problems (2018)
  14. Díaz-Alvarado, Felipe A.; Miranda-Pérez, Jenny; Grossmann, Ignacio E.: Search for reaction pathways with P-graphs and reaction blocks: methanation of carbon dioxide with hydrogen (2018)
  15. Fortunati, Alessandro; Wiggins, Stephen: Transient invariant and quasi-invariant structures in an example of an aperiodically time dependent fluid flow (2018)
  16. Himpe, Christian; Leibner, Tobias; Rave, Stephan: Hierarchical approximate proper orthogonal decomposition (2018)
  17. Jaust, Alexander; Reuter, Balthasar; Aizinger, Vadym; Schütz, Jochen; Knabner, Peter: FESTUNG: a MATLAB/GNU Octave toolbox for the discontinuous Galerkin method. III: Hybridized discontinuous Galerkin (HDG) formulation (2018)
  18. Johnson, Robert W.: Algorithm 988. AMGKQ: an efficient implementation of adaptive multivariate Gauss-Kronrod quadrature for simultaneous integrands in Octave/MATLAB (2018)
  19. Karl Sjöstrand; Line Clemmensen; Rasmus Larsen; Gudmundur Einarsson; Bjarne Ersbøll: SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling (2018) not zbMATH
  20. Lee, Martin J.; Tsang, Ken: Nonlinear algebra in an ACORN. With applications to deep learning (2018)

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