pyOpt
swMATH ID:  6921 
Software Authors:  Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R.R.A. 
Description:  PyOpt: a pythonbased objectoriented framework for nonlinear constrained optimization. We present pyOpt, an objectoriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses objectoriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem. This creates a common interface in a flexible environment where both practitioners and developers alike can solve their optimization problems or develop and benchmark their own optimization algorithms. The framework is developed in the Python programming language, which allows for easy integration of optimization software programmed in Fortran, C, C++, and other languages. A variety of optimization algorithms are integrated in pyOpt and are accessible through the common interface. We solve a number of problems of increasing complexity to demonstrate how a given problem is formulated using this framework, and how the framework can be used to benchmark the various optimization algorithms. 
Homepage:  http://www.pyopt.org/ 
Programming Languages:  Python 
Keywords:  optimization algorithms; constrained optimization; nonlinear programming; objectoriented programming; python; aerostructural optimization; { t PyOpt} 
Related Software:  SNOPT; Python; DAKOTA; DFVLRSQP; SciPy; OpenMDAO; SpalartAllmaras; OpenFOAM; PETSc; SciTools; pyMDO; F2PY; Adjoint; TAO; Ipopt; top.m; OpenAeroStruct; NLopt; pyOptSparse; KRATOS 
Cited in:  12 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

PyOpt: a pythonbased objectoriented framework for nonlinear constrained optimization. Zbl 1274.90008 Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A. 
2012

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Cited by 28 Authors
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Cited in 10 Serials
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