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 265 articles , 1 standard article )

Showing results 1 to 20 of 265.
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  1. Delourme, Bérangère; Duyckaerts, Thomas; Lerner, Nicolas: On integrals over a convex set of the Wigner distribution (2020)
  2. Luz, Carlos J.: New results for recognizing convex-(QP) adverse graphs (2020)
  3. Vítor S. Ramos: SIHR: a MATLAB/GNU Octave toolbox for single image highlight removal (2020) not zbMATH
  4. Ciripoi, Daniel; Löhne, Andreas; Weißing, Benjamin: Calculus of convex polyhedra and polyhedral convex functions by utilizing a multiple objective linear programming solver (2019)
  5. 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
  6. Erofeev, K. Yu.; Khramchenkov, E. M.; Biryal’tsev, E. V.: High-performance processing of covariance matrices using GPU computations (2019)
  7. Fortunati, Alessandro; Wiggins, Stephen: A Lie transform approach to the construction of Lyapunov functions in autonomous and non-autonomous systems (2019)
  8. Gander, Martin J.; Kulchytska-Ruchka, Iryna; Niyonzima, Innocent; Schöps, Sebastian: A new parareal algorithm for problems with discontinuous sources (2019)
  9. Girardin, Léo; Calvez, Vincent; Débarre, Florence: Catch me if you can: a spatial model for a brake-driven gene drive reversal (2019)
  10. Gómez, Víctor: Linear time series with MATLAB and OCTAVE (2019)
  11. Haußer, Frank; Luchko, Yuri: Mathematical modelling with MATLAB and Octave. A practice-oriented introduction (2019)
  12. Holder, Allen; Eichholz, Joseph: An introduction to computational science. With a foreword by Robert J. Vanderbei (2019)
  13. 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
  14. Johnson, Nels G.; Kim, Inyoung: Semiparametric approaches for matched case-control studies with error-in-covariates (2019)
  15. Lie, Knut-Andreas: An introduction to reservoir simulation using MATLAB/GNU Octave. User guide for the MATLAB reservoir simulation toolbox (MRST) (2019)
  16. Mahboubi, Assia; Melquiond, Guillaume; Sibut-Pinote, Thomas: Formally verified approximations of definite integrals (2019)
  17. Nepomuceno, Erivelton G.; Guedes, Priscila F. S.; Barbosa, Alípio M.; Perc, Matjaž; Repnik, Robert: Soft computing simulations of chaotic systems (2019)
  18. Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
  19. O. R. Bingol, A. Krishnamurthy: NURBS-Python: An open-source object-oriented NURBS modeling framework in Python (2019) not zbMATH
  20. Peña, Juan Manuel; Sauer, Tomas: SVD update methods for large matrices and applications (2019)

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