Matlab

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 10462 articles , 8 standard articles )

Showing results 1 to 20 of 10462.
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  1. Shafai, Bahram: System identification and adaptive control (to appear) (2024)
  2. Bhaumik, Shovan; Date, Paresh: Nonlinear estimation. Methods and applications with deterministic sample points (2020)
  3. Chan, Joshua; Koop, Gary; Poirer, Dale J.; Tobias, Justin L.: Bayesian econometric methods (2020)
  4. Colapinto, Cinzia; Jayaraman, Raja; La Torre, Davide: Goal programming models for managerial strategic decision making (2020)
  5. Diniz, Paulo S. R.: Adaptive filtering. Algorithms and practical implementation (to appear) (2020)
  6. Graham, Bryan; de Paula, Aureo: The econometric analysis of network data (to appear) (2020)
  7. Hermann, Martin: Numerical analysis. Volume 1. Numerical methods for algebra problems (to appear) (2020)
  8. Kumar, Sandeep; Pathak, Ashish; Khan, Debashis: Mathematical theory of subdivision. Finite element and wavelet methods (2020)
  9. Li, Jichun; Chen, Yi-Tung: Computational partial differential equations using MATLAB (to appear) (2020)
  10. Ratnajeevan, S.; Hoole, H.; Hoole, Yovahn Yesuraiyan R.: Finite elements-based optimization. Electromagnetic product design and nondestructive evaluation (2020)
  11. Roul, Pradip; Prasad Goura, V. M. K.: A new higher order compact finite difference method for generalised Black-Scholes partial differential equation: European call option (2020)
  12. Wanhammar, Lars; Saramäki, Tapio: Digital filters using Matlab (to appear) (2020)
  13. Zondervan, Edwin: A numerical primer for the chemical engineer (2020)
  14. Abdi, A.; Jackiewicz, Z.: Towards a code for nonstiff differential systems based on general linear methods with inherent Runge-Kutta stability (2019)
  15. Abuaisha, Tareq; Kertzscher, Jana: Fractional-order modelling and parameter identification of electrical coils (2019)
  16. Adamatzky, Andrew (ed.); Akl, Selim G. (ed.); Sirakoulis, Georgios Ch. (ed.): From parallel to emergent computing (2019)
  17. Agarwal, Deepika; Singh, Pitam; Bhati, Deepak; Kumari, Saru; Obaidat, Mohammad S.: Duality-based branch-bound computational algorithm for sum-of-linear-fractional multi-objective optimization problem (2019)
  18. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  19. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)
  20. Al-Araji, Ahmed S.: An adaptive swing-up sliding mode controller design for a real inverted pendulum system based on culture-bees algorithm (2019)

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