MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java™. You can use MATLAB for a range of applications, including signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology. More than a million engineers and scientists in industry and academia use MATLAB, the language of technical computing.

This software is also referenced in ORMS.

References in zbMATH (referenced in 10885 articles , 8 standard articles )

Showing results 1 to 20 of 10885.
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  1. Shafai, Bahram: System identification and adaptive control (to appear) (2024)
  2. Balk, Bert M.; Barbero, Javier; Zofío, José L.: A toolbox for calculating and decomposing total factor productivity indices (2020)
  3. Bashier, Eihab B. M.: Practical numerical and scientific computing with MATLAB and Python (to appear) (2020)
  4. Bhaumik, Shovan; Date, Paresh: Nonlinear estimation. Methods and applications with deterministic sample points (2020)
  5. Biermé, Hermine; Lacaux, Céline: Fast and exact synthesis of some operator scaling Gaussian random fields (2020)
  6. Botchev, M. A.; Knizhnerman, L. A.: ART: adaptive residual-time restarting for Krylov subspace matrix exponential evaluations (2020)
  7. Bünger, Florian: A Taylor model toolbox for solving ODEs implemented in Matlab/INTLAB (2020)
  8. Buvoli, Tommaso: A class of exponential integrators based on spectral deferred correction (2020)
  9. Cavoretto, Roberto; De Rossi, Alessandra: Error indicators and refinement strategies for solving Poisson problems through a RBF partition of unity collocation scheme (2020)
  10. Chan, Joshua; Koop, Gary; Poirer, Dale J.; Tobias, Justin L.: Bayesian econometric methods (2020)
  11. Colapinto, Cinzia; Jayaraman, Raja; La Torre, Davide: Goal programming models for managerial strategic decision making (2020)
  12. Dattatreya Rao, A. V.; Girija, S. V. S.: Angular statistics (2020)
  13. Dimitrios Tsiotas; Avraam Charakopoulos: VisExpA: Visibility expansion algorithm in the topology of complex networks (2020) not zbMATH
  14. Diniz, Paulo S. R.: Adaptive filtering. Algorithms and practical implementation (2020)
  15. Fotopoulos, G.; Karachalios, N. I.; Koukouloyannis, V.; Vetas, K.: The linearly damped nonlinear Schrödinger equation with localized driving: spatiotemporal decay estimates and the emergence of extreme wave events (2020)
  16. Graham, Bryan; de Paula, Aureo: The econometric analysis of network data (to appear) (2020)
  17. Hermann, Martin: Numerical analysis. Volume 1. Algebra problems (2020)
  18. Ingolfsson, Armann; Almehdawe, Eman; Pedram, Ali; Tran, Monica: Comparison of fluid approximations for service systems with state-dependent service rates and return probabilities (2020)
  19. Kumar, Sandeep; Pathak, Ashish; Khan, Debashis: Mathematical theory of subdivision. Finite element and wavelet methods (2020)
  20. Li, Jichun; Chen, Yi-Tung: Computational partial differential equations using MATLAB (2020)

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