pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization

By Ruben E. Perez, P. Jansen, Joaquim R. Martins. Structural And Multidisciplinary Optimization, 2011

482 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/39b23bd57833bb9506788a266a5eda85340dbaa1

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

1. Gaetan K. W. Kenway
2. Zhoujie Lyu
3. Graeme J. Kennedy
4. Andrew B. Lambe
5. Joaquim R. Martins

In Computational Theory and Mathematics

1. Gaetan K. W. Kenway
2. Andrew B. Lambe
3. Joaquim R. Martins
4. Zhoujie Lyu
5. Graeme J. Kennedy
6. Cody Paige
7. John T. Hwang
8. Martin P. Bendsøe

Citations in the corpus

• Buffet-Onset Constraint Formulation for Aerodynamic Shape Optimization (2017, methodology): “We use SNOPT (sparse nonlinear optimizer) [57] with the Python interface pyOpt [58].”
• Stress-constrained topology optimization with design-dependent loading (2012, unknown): “Optimization problems are solved using SNOPT (Gill et al. 2005), a sequential quadratic programming optimizer, which is used within pyOpt, an objectoriented framework for nonlinear optimization (Perez et al. 2011).”
• An evaluation of constraint aggregation strategies for wing box mass minimization (2017, methodology): “The optimizer used in the framework is SNOPT (Gill et al., 2002), which is accessed through the pyOpt interface (Perez et al., 2012).”
• Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing (2015, methodology): “We use the sparse nonlinear optimizer (SNOPT) [27] through the Python interface pyOpt [28] for all the results presented here.”
• Automatic Differentiation Adjoint of the Reynolds-Averaged Navier-Stokes Equations with a Turbulence Model (2013, methodology): “We use a Python-based optimization package, pyOpt [39], to interface with CFD and adjoint solvers.”
• Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013, unknown): “gradients [31,32].”
• RANS-based aerodynamic shape optimization investigations of the common research modelwing (2014, methodology): “The optimization algorithm we use is SNOPT (sparse nonlinear optimizer) [24] through the Python interface pyOpt [25] for all results presented here.”