SciPy (pronounced ”Sigh Pie”) is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!

References in zbMATH (referenced in 797 articles )

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  1. Alex Tritt, Joshua Morris, Joel Hochstetter, R. P. Anderson, James Saunderson, L. D. Turner: Spinsim: a GPU optimized python package for simulating spin-half and spin-one quantum systems (2022) arXiv
  2. Allen, Stephanie; Gabriel, Steven A.; Dickerson, John P.: Using inverse optimization to learn cost functions in generalized Nash games (2022)
  3. Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
  4. Anikin, A. S.; Matyukhin, V. V.; Pasechnyuk, D. A.: Accelerated proximal envelopes: application to componentwise methods (2022)
  5. An, Xin; Majee, Ananta K.; Prohl, Andreas; Tran, Thanh: Optimal control for a coupled spin-polarized current and magnetization system (2022)
  6. Atashgahi, Zahra; Sokar, Ghada; van der Lee, Tim; Mocanu, Elena; Mocanu, Decebal Constantin; Veldhuis, Raymond; Pechenizkiy, Mykola: Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (2022)
  7. Ausas, Roberto Federico; Gebhardt, Cristian Guillermo; Buscaglia, Gustavo Carlos: A finite element method for simulating soft active non-shearable rods immersed in generalized Newtonian fluids (2022)
  8. Belohlavek, Radim; Mikula, Tomas: Typicality: a formal concept analysis account (2022)
  9. Benner, Peter; Heiland, Jan; Werner, Steffen W. R.: Robust output-feedback stabilization for incompressible flows using low-dimensional (\mathcalH_\infty)-controllers (2022)
  10. Besse, Christophe; Duboscq, Romain; Le Coz, Stefan: Numerical simulations on nonlinear quantum graphs with the GraFiDi library (2022)
  11. Bevilacqua, Moreno; Caamaño-Carrillo, Christian; Porcu, Emilio: Unifying compactly supported and Matérn covariance functions in spatial statistics (2022)
  12. Bjørdalsbakke, Nikolai L.; Sturdy, Jacob T.; Hose, David R.; Hellevik, Leif R.: Parameter estimation for closed-loop lumped parameter models of the systemic circulation using synthetic data (2022)
  13. Black, Nolan; Najafi, Ahmad R.: Learning finite element convergence with the multi-fidelity graph neural network (2022)
  14. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: On sparse optimal regression trees (2022)
  15. Bomarito, G. F.; Leser, P. E.; Warner, J. E.; Leser, W. P.: On the optimization of approximate control variates with parametrically defined estimators (2022)
  16. Boussaada, Islam; Mazanti, Guilherme; Niculescu, Silviu-Iulian: Some remarks on the location of non-asymptotic zeros of Whittaker and Kummer hypergeometric functions (2022)
  17. Cai, Shang-Rong; Hwang, Feng-Nan: A hybrid-line-and-curve search globalization technique for inexact Newton methods (2022)
  18. Carrillo, J. A.; Delgadino, M. G.; Frank, R. L.; Lewin, M.: Fast diffusion leads to partial mass concentration in Keller-Segel type stationary solutions (2022)
  19. Chen, Haotian: On locating the zeros and poles of a meromorphic function (2022)
  20. Christoph W. Wagner; Sebastian Semper; Jan Kirchhof: fastmat: Efficient linear transforms in Python (2022) not zbMATH

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