Boost C++ Libraries
By S. Koranne. 2011
717 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/6e5b3119d88e6ee2c03e9ee6c5b1c31bbba9397b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Paul-Christian Burkner
- Andrew Gelman
- Matthew D. Hoffman
- David M. Blei
- Bob Carpenter
- Michael Betancourt
- Marcus A. Brubaker
- Ben Goodrich
Citations in the corpus
- The Stan Math Library: Reverse-Mode Automatic Differentiation in C++ (2015, background): “Boost’s traits-based metaprogram promote args (Schäling, 2011) is designed to calculate return types for highly templated functions like normal log(); Vandevoorde and Josuttis (2002) provide an excellent overview of trait-based template metaprogramming.”
- The deal.II Library, Version 9.5 (2023, neutral): “II can interface with many other libraries: – ADOL-C [35] – ArborX [52] – ARPACK [53] – Assimp [68] – BLAS and LAPACK [5] – Boost [18] – CGAL [70] – cuSOLVER [25] – cuSPARSE [26] – Gmsh [33] – GSL [31, 36] – Ginkgo [6, 7] – HDF5 [71] – METIS [45] – MUMPS [4, 3] – muparser [60] – OpenCASCADE [61] –…”
- The Spack package manager: bringing order to HPC software chaos (2015, background): “For example, a user may know that a certain version of a library like boost [1] is required, but only cares that other build parameters are set so that the build will succeed.”
- Stan: A Probabilistic Programming Language (2017, background): “Stan depends on several of the Boost C++ libraries (Schäling 2011).”
- Differentially private data aggregation with optimal utility (2014, neutral): “-DDP in the HbC model, and (cid”
- The FEniCS Project Version 1.5 (2015, methodology): “…are known to work), see section 2.11 for details • Boost ≥ 1.48; provides some low-level data structures and algorithms, DOF reordering and mesh coloring [Schling, 2011] • CMake ≥ 2.8; used by DOLFIN build system and optionally by Python JIT chain c© by the authors, 2015 Archive of Numerical…”
- A visual analytics approach to understanding cycling behaviour (2012, neutral): “For that reason, the following subsections refer to the effect of Oh on them.”