PyOpt: a python-based object-oriented framework for nonlinear constrained optimization. We present pyOpt, an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses object-oriented 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.

References in zbMATH (referenced in 14 articles , 1 standard article )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Ghantasala, Aditya; Najian Asl, Reza; Geiser, Armin; Brodie, Andrew; Papoutsis, Efthymios; Bletzinger, Kai-Uwe: Realization of a framework for simulation-based large-scale shape optimization using vertex morphing (2021)
  2. Kontogiannis, Spyridon G.; Savill, Mark A.: A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis (2020)
  3. Wu, N., Kenway, G., Mader, C. A., Jasa, J., Martins, J. R. R. A.: pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems (2020) not zbMATH
  4. He, Ping; Filip, Grzegorz; Martins, Joaquim R. R. A.; Maki, Kevin J.: Design optimization for self-propulsion of a bulk carrier hull using a discrete adjoint method (2019)
  5. Garcia, D.; Ghommem, M.; Collier, N.; Varga, B. O. N.; Calo, V. M.: PyFly: a fast, portable aerodynamics simulator (2018)
  6. He, Ping; Mader, Charles A.; Martins, Joaquim R. R. A.; Maki, Kevin J.: An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM (2018)
  7. Zahr, M. J.; Persson, P.-O.: An optimization-based approach for high-order accurate discretization of conservation laws with discontinuous solutions (2018)
  8. Arreckx, Sylvain; Lambe, Andrew; Martins, Joaquim R. R. A.; Orban, Dominique: A matrix-free augmented Lagrangian algorithm with application to large-scale structural design optimization (2016)
  9. Hickey, Jean-Pierre; Hussain, Fazle; Wu, Xiaohua: Compressibility effects on the structural evolution of transitional high-speed planar wakes (2016)
  10. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  11. Zahr, Matthew J.; Farhat, Charbel: Progressive construction of a parametric reduced-order model for PDE-constrained optimization (2015)
  12. Lee, Edmund; James, Kai A.; Martins, Joaquim R. R. A.: Stress-constrained topology optimization with design-dependent loading (2012)
  13. Lee, Edmund; Martins, Joaquim R. R. A.: Structural topology optimization with design-dependent pressure loads (2012)
  14. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)