References in zbMATH (referenced in 285 articles )

Showing results 41 to 60 of 285.
Sorted by year (citations)

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  1. Busseti, Enzo; Moursi, Walaa M.; Boyd, Stephen: Solution refinement at regular points of conic problems (2019)
  2. Carrillo, José Antonio; Craig, Katy; Patacchini, Francesco S.: A blob method for diffusion (2019)
  3. Christopher Hahne; Amar Aggoun: PlenoptiSign: An optical design tool for plenoptic imaging (2019) not zbMATH
  4. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in Python using sfepy (2019)
  5. Danny C. Price, J. Emilio Enriquez, Yuhong Chen, Mark Siebert: Blimpy: Breakthrough Listen I/O Methods for Python (2019) not zbMATH
  6. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  7. De Loera, Jesús A.; Petrović, Sonja; Silverstein, Lily; Stasi, Despina; Wilburne, Dane: Random monomial ideals (2019)
  8. D. Huppenkothen, M. Bachetti, A. L. Stevens, S. Migliari, P. Balm, O. Hammad, U. M. Khan, H. Mishra, H. Rashid, S. Sharma, R. V. Blanco, E. M. Ribeiro: Stingray: A Modern Python Library For Spectral Timing (2019) arXiv
  9. Dudziuk, Grzegorz; Lachowicz, Mirosław; Leszczyński, Henryk; Szymańska, Zuzanna: A simple model of collagen remodeling (2019)
  10. Engwer, Christian; Henning, Patrick; Målqvist, Axel; Peterseim, Daniel: Efficient implementation of the localized orthogonal decomposition method (2019)
  11. Erden, Caner; Demir, Halil Ibrahim; Kökçam, Abdullah Hulusi: Solving integrated process planning, dynamic scheduling, and due date assignment using metaheuristic algorithms (2019)
  12. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  13. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  14. Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh, Zhenwen Dai, Marc Henniges, Jakob Drefs, Jörg Lücke: ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions (2019) arXiv
  15. Gevorkyan, Migran N.; Korolkova, Anna V.; Kulyabov, Dmitry S.; Lovetskiy, Konstantin P.: Statistically significant comparative performance testing of Julia and Fortran languages in case of Runge-Kutta methods (2019)
  16. Jakob M. Maljaars, Chris N. Richardson, Nathan Sime: LEoPart: a particle library for FEniCS (2019) arXiv
  17. Janzamin, Majid; Ge, Rong; Kossaifi, Jean; Anandkumar, Anima: Spectral learning on matrices and tensors (2019)
  18. Jeffrey S. Hazboun, Joseph D. Romano, Tristan L. Smith: Hasasia: A Python package for Pulsar Timing Array Sensitivity Curves (2019) not zbMATH
  19. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  20. Jonas Fassbender: libconform v0.1.0: a Python library for conformal prediction (2019) arXiv

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