SciPy 1.0: fundamental algorithms for scientific computing in Python
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Summary
An overview of the capabilities and development practices of SciPy 1.0 is provided and some recent technical developments are highlighted.
- Type
- article
- Published
- 2019-07-23
- Cited by
- 34,559
- References
- 151
- Access
- Open access
- OpenAlex
- https://openalex.org/W3003257820
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:198229805
Keywords
Python (programming language), Code (set theory), Source code, De facto, Function (biology)
References
- Trust Region Methods
- Note sur la convergence de méthodes de directions conjuguées
- Users' guide for the Harwell-Boeing sparse matrix collection (Release 1)
- User guide for MINPACK-1. [In FORTRAN]
- The nonlinear programming method of Wilson, Han, and Powell with an augmented Lagrangian type line search function
- User Guide for Minpack-1
- ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods
- The High Performance Fortran Handbook
- Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
- Quadpack: A Subroutine Package for Automatic Integration
- Circumventing The Linker: Using SciPy's BLAS and LAPACK Within Cython
- The LIGO Open Science Center
- An overview of SuperLU: Algorithms, implementation, and user interface
- F2PY: a tool for connecting Fortran and Python programs
- NetCDF: an interface for scientific data access
- Python for Scientists and Engineers
- Tracing the meta-level: PyPy's tracing JIT compiler
- A Limited Memory Algorithm for Bound Constrained Optimization
- The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
- Finding scientific topics
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