Introduction

pyOptSparse is fairly extensive modification to the original pyOpt framework originally written by Dr. Ruben E. Perez and Peter W. Jansen. It is not backwards compatible with pyOpt and thus optimization scripts will need to be modified to use pyOptSparse.

The original goal of pyOpt was to create an object-oriented framework for formulating and solving nonlinear constrained optimization problems. This goal remains the same with pyOptSparse.

From the pyOpt README some of the main pyOpt features are:

• Object-oriented development maintains independence between the optimization problem formulation and its solution by different optimizers
• Allows for easy integration of gradient-based, gradient-free, and population-based optimization algorithms
• Interfaces both open source as well as industrial optimizers
• Ease the work required to do nested optimization and provides automated solution refinement
• On parallel systems it enables the use of optimizers when running in a mpi parallel environment, allows for evaluation of gradients in parallel, and can distribute function evaluations for gradient-free optimizers
• Optimization solution histories can be stored during the optimization process. A partial history can also be used to warm-restart the optimization