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 257 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. Fortunati, Alessandro; Wiggins, Stephen: A Lie transform approach to the construction of Lyapunov functions in autonomous and non-autonomous systems (2019)
  5. Gander, Martin J.; Kulchytska-Ruchka, Iryna; Niyonzima, Innocent; Schöps, Sebastian: A new parareal algorithm for problems with discontinuous sources (2019)
  6. Haußer, Frank; Luchko, Yuri: Mathematical modelling with MATLAB and Octave. A practice-oriented introduction (2019)
  7. Holder, Allen; Eichholz, Joseph: An introduction to computational science. With a foreword by Robert J. Vanderbei (2019)
  8. 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
  9. Lie, Knut-Andreas: An introduction to reservoir simulation using MATLAB/GNU Octave. User guide for the MATLAB reservoir simulation toolbox (MRST) (2019)
  10. Mahboubi, Assia; Melquiond, Guillaume; Sibut-Pinote, Thomas: Formally verified approximations of definite integrals (2019)
  11. Nepomuceno, Erivelton G.; Guedes, Priscila F. S.; Barbosa, Alípio M.; Perc, Matjaž; Repnik, Robert: Soft computing simulations of chaotic systems (2019)
  12. Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
  13. O. R. Bingol, A. Krishnamurthy: NURBS-Python: An open-source object-oriented NURBS modeling framework in Python (2019) not zbMATH
  14. Peña, Juan Manuel; Sauer, Tomas: SVD update methods for large matrices and applications (2019)
  15. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  16. Wang, Lizhi; Nikouei Mehr, Maryam: An optimization approach to epistasis detection (2019)
  17. Yang, Lihong; Chen, Zhong; Xie, Kechao: An efficient method for approximate solution of a singular integral equation with Cauchy kernel (2019)
  18. Beyrami, Hossein; Lotfi, Taher: On the local superconvergence of the fully discretized multiprojection method for weakly singular Volterra integral equations of the second kind (2018)
  19. Bielecki, Tomasz R.; Jeanblanc, Monique; Sezer, Ali Devin: Joint densities of hitting times for finite state Markov processes (2018)
  20. Deschner, Stephan C.; Illenseer, Tobias F.; Duschl, Wolfgang J.: Self-similar solutions to isothermal shock problems (2018)

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