ParOpt is a nonlinear interior point optimizer that is designed for large parallel design optimization problems with structured sparse constraints. ParOpt is open source and can be downloaded at Documentation and examples for ParOpt can be found at The version of ParOpt supported is v2.0.2.


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. This typically requires installing it in a location which is already present under $PYTHONPATH environment variable, or you can modify the .bashrc file and manually append the path.


Please see the ParOpt documentation for all available options.


class pyoptsparse.pyParOpt.ParOpt.ParOpt(*args, **kwargs)[source]

ParOpt optimizer class

ParOpt has the capability to handle distributed design vectors. This is not replicated here since pyOptSparse does not have the capability to handle this type of design problem.

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


Optimizer name


Typically local or global


A dictionary containing the default options


Dictionary of the inform codes

__call__(optProb, sens=None, sensStep=None, sensMode=None, storeHistory=None, hotStart=None, storeSens=True)[source]

This is the main routine used to solve the optimization problem.

optProbOptimization or Solution class instance

This is the complete description of the optimization problem to be solved by the optimizer

sensstr or python Function.

Specifiy method to compute sensitivities. To explictly use pyOptSparse gradient class to do the derivatives with finite differenes use ‘FD’. ‘sens’ may also be ‘CS’ which will cause pyOptSpare to compute the derivatives using the complex step method. Finally, ‘sens’ may be a python function handle which is expected to compute the sensitivities directly. For expensive function evaluations and/or problems with large numbers of design variables this is the preferred method.


Set the step size to use for design variables. Defaults to 1e-6 when sens is ‘FD’ and 1e-40j when sens is ‘CS’.


Use ‘pgc’ for parallel gradient computations. Only available with mpi4py and each objective evaluation is otherwise serial


File name of the history file into which the history of this optimization will be stored


File name of the history file to “replay” for the optimziation. The optimization problem used to generate the history file specified in ‘hotStart’ must be IDENTICAL to the currently supplied ‘optProb’. By identical we mean, EVERY SINGLE PARAMETER MUST BE IDENTICAL. As soon as he requested evaluation point from ParOpt does not match the history, function and gradient evaluations revert back to normal evaluations.


Flag sepcifying if sensitivities are to be stored in hist. This is necessay for hot-starting only.