IPOPT

IPOPT (Interior Point OPTimizer) is an open source interior point optimizer, designed for large-scale nonlinear optimization. The source code can be found here. The latest version we support is 3.14.17.

Installation

IPOPT and its Python interface cyipopt <https://github.com/mechmotum/cyipopt> must be installed separately. Follow the instructions here. OpenMDAO also has a very helpful script which can be used to install IPOPT with other linear solvers, but it does not install cyipopt for you.

Options

Please refer to the IPOPT website for complete listing of options. The following are the options which are set by default within pyOptSparse. All other options take the default value with IPOPT unless specified by the user.

IPOPT Default Options

Name	Type	Default value	Description
print_level	int	0	Printing level
file_print_level	int	5	Printing level for the output file
sb	str	yes	This is an undocumented option which suppresses the IPOPT header from being printed to screen every time.
print_user_options	str	yes	Whether to print the user-modified options
output_file	str	IPOPT.out	The name of the output file from IPOPT
linear_solver	str	mumps	The linear solver used.

Informs

IPOPT Informs

Code	Description
0	Solve Succeeded
1	Solved To Acceptable Level
2	Infeasible Problem Detected
3	Search Direction Becomes Too Small
4	Diverging Iterates
5	User Requested Stop
6	Feasible Point Found
-1	Maximum Iterations Exceeded
-2	Restoration Failed
-3	Error In Step Computation
-4	Maximum CpuTime Exceeded
-10	Not Enough Degrees Of Freedom
-11	Invalid Problem Definition
-12	Invalid Option
-13	Invalid Number Detected
-100	Unrecoverable Exception
-101	NonIpopt Exception Thrown
-102	Insufficient Memory
-199	Internal Error

API

class pyoptsparse.pyIPOPT.pyIPOPT.IPOPT(*args, **kwargs)

IPOPT Optimizer Class - Inherited from Optimizer Abstract Class

IPOPT Optimizer Class Initialization

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

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

Parameters:

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.

sensStepfloat

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

sensModestr

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

storeHistorystr

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

hotStartstr

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 does not match the history, function and gradient evaluations revert back to normal evaluations.

storeSensbool

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