ParOpt

ParOpt is an open source package that implements trust-region, interior points, and MMA optimization algorithms. The ParOpt optimizers are themselves MPI parallel, which allows them to scale to large problems. Unlike other optimizers supported by pyOptSparse, as of ParOpt version 2.1.5 and later, the pyOptSparse interface to ParOpt is a part of ParOpt itself. Maintaining the wrapper, and control of which versions of pyOptSparse are compatible with which versions of ParOpt, is therefore the responsibility of the ParOpt developers. ParOpt can be downloaded at https://github.com/smdogroup/paropt. Documentation and examples can be found at https://smdogroup.github.io/paropt/. The wrapper code in pyOptSparse is minimal, simply allowing the ParOpt wrapper to be used in the same way as other optimizers in pyOptSparse, through the OPT method.

The ParOpt wrapper takes a sparse argument, which controls whether ParOpt uses sparse or dense storage for the constraint Jacobian. The default is True, which uses sparse storage, but is incompatible with ParOpt’s trust-region algorithm. If you want to use the trust-region algorithm, you must set sparse=False, e.g.:

from pyoptsparse import OPT
opt = OPT("ParOpt", sparse=False)

Installation

Please follow the instructions here to install ParOpt as a separate Python package. Make sure that the package is named paropt and the installation location can be found by Python, so that from paropt import ParOpt works within the pyOptSparse folder.

Options

Please see the ParOpt documentation for all available options.

API

class pyoptsparse.pyParOpt.ParOpt.ParOpt(*args, **kwargs)

This is the base optimizer class that all optimizers inherit from. We define common methods here to avoid code duplication.

Parameters:

namestr

Optimizer name

categorystr

Typically local or global

defaultOptionsdictionary

A dictionary containing the default options

informsdict

Dictionary of the inform codes

__call__(*args, **kwargs)

Call self as a function.