SciPy 1.0 : fundamental algorithms for scientific computing in Python
Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, C. J., Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul, SciPy 1.0 Contributors. (2020). SciPy 1.0 : fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261-272. https://doi.org/10.1038/s41592-019-0686-2
Published inNature Methods
Feng, Yu |
© The Authors 2020
SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
PublisherNature Publishing Group
ISSN Search the Publication Forum1548-7091
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Memory-saving optimization algorithms for systems with limited hardware Iacca, Giovanni (University of Jyväskylä, 2011)
Algorithmic issues in computational intelligence optimization : from design to implementation, from implementation to design Caraffini, Fabio (University of Jyväskylä, 2016)The vertiginous technological growth of the last decades has generated a variety of powerful and complex systems. By embedding within modern hardware devices sophisticated software, they allow the solution of complicated ...
Simple memetic computing structures for global optimization Poikolainen, Ilpo (University of Jyväskylä, 2014)
Artificial Intelligence and Computational Science Neittaanmäki, Pekka; Repin, Sergey (Springer, 2022)In this note, we discuss the interaction between two ways of scientific analysis. The first (classical) way is known as Mathematical Modeling (MM). It is based on a model created by humans and presented in mathematical ...
Parallel global optimization : structuring populations in differential evolution Weber, Matthieu (University of Jyväskylä, 2010)