Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C

DOI10.1080/10556788.2018.1425861zbMath1453.90109OpenAlexW2801411152WikidataQ122905273 ScholiaQ122905273MaRDI QIDQ4685604

Sri Hari Krishna Narayanan, Kshitij Kulshreshtha, K. MacIntyre, Julie Bessac

Publication date: 9 October 2018

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2018.1425861

zbMATH Keywords

Rstochastic optimizationdata analysismachine learningalgorithmic differentiationPythonscriptingADOL-CautodiffSWIG

Mathematics Subject Classification ID

Symbolic computation and algebraic computation (68W30) Stochastic programming (90C15) Software, source code, etc. for problems pertaining to operations research and mathematical programming (90-04)

Uses Software

• ADOL-C
• R
• SymPy
• ADIC
• DOLFIN
• FEniCS
• SWIG
• CppAD
• Yacas
• Theano
• SciPy
• TAPENADE
• ADMB
• OpenAD/F
• ADIC2
• numDeriv
• Python
• PyCppAD
• AlgoPy
• dco/c++
• Ryacas
• CoDiPack
• pyadolc
• Deriv
• radx
• Numdifftools
• madness

Cites Work

• Unnamed Item
• A Stochastic Quasi-Newton Method for Large-Scale Optimization
• On the numerical stability of algorithmic differentiation
• Derivative-based global sensitivity measures: general links with Sobol' indices and numerical tests
• DOLFIN
• The Tapenade automatic differentiation tool
• Evaluating Derivatives
• OpenAD/F
• Fast Reverse-Mode Automatic Differentiation using Expression Templates in C++

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