This code implements an SQP approach that is modified to generate feasible iterates. In addition to handling general single objective constrained nonlinear optimization problems, the code is also capable of handling multiple competing linear and nonlinear objective functions (minimax), linear and nonlinear inequality constraints, as well as linear and nonlinear equality constraints


FSQP build fails, and is therefore deprecated.


class pyoptsparse.pyFSQP.pyFSQP.FSQP(*args, **kwargs)[source]

FSQP Optimizer Class - Inherited from Optimizer Abstract Class

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

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

optProb : Optimization or Solution class instance

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

sens : str 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.

sensStep : float

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

sensMode : str

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

storeHistory : str

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

hotStart : str

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

storeSens : bool

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