# scipy_optimizer.py¶

OpenMDAO Wrapper for the scipy.optimize.minimize family of local optimizers.

class openmdao.drivers.scipy_optimizer.ScipyOptimizeDriver(**kwargs)[source]

Driver wrapper for the scipy.optimize.minimize family of local optimizers.

Inequality constraints are supported by COBYLA and SLSQP, but equality constraints are only supported by SLSQP. None of the other optimizers support constraints.

ScipyOptimizeDriver supports the following:

equality_constraints inequality_constraints

Attributes

 fail (bool) Flag that indicates failure of most recent optimization. iter_count (int) Counter for function evaluations. result (OptimizeResult) Result returned from scipy.optimize call. opt_settings (dict) Dictionary of solver-specific options. See the scipy.optimize.minimize documentation.
__init__(self, **kwargs)[source]

Initialize the ScipyOptimizeDriver.

Parameters
**kwargsdict of keyword arguments

Keyword arguments that will be mapped into the Driver options.

add_recorder(self, recorder)

Add a recorder to the driver.

Parameters
recorderCaseRecorder

A recorder instance.

cleanup(self)

Clean up resources prior to exit.

declare_coloring(self, num_full_jacs=3, tol=1e-25, orders=None, perturb_size=1e-09, min_improve_pct=5.0, show_summary=True, show_sparsity=False)

Set options for total deriv coloring.

Parameters
num_full_jacsint

Number of times to repeat partial jacobian computation when computing sparsity.

tolfloat

Tolerance used to determine if an array entry is nonzero during sparsity determination.

ordersint

Number of orders above and below the tolerance to check during the tolerance sweep.

perturb_sizefloat

Size of input/output perturbation during generation of sparsity.

min_improve_pctfloat

If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used.

show_summarybool

If True, display summary information after generating coloring.

show_sparsitybool

If True, display sparsity with coloring info after generating coloring.

get_constraint_values(self, ctype='all', lintype='all', driver_scaling=True)

Return constraint values.

Parameters
ctypestring

Default is ‘all’. Optionally return just the inequality constraints with ‘ineq’ or the equality constraints with ‘eq’.

lintypestring

Default is ‘all’. Optionally return just the linear constraints with ‘linear’ or the nonlinear constraints with ‘nonlinear’.

driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each constraint.

get_design_var_values(self)

Return the design variable values.

Returns
dict

Dictionary containing values of each design variable.

get_objective_values(self, driver_scaling=True)

Return objective values.

Parameters
driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each objective.

property msginfo

Return info to prepend to messages.

Returns
str

Info to prepend to messages.

record_iteration(self)

Record an iteration of the current Driver.

run(self)[source]

Optimize the problem using selected Scipy optimizer.

Returns
boolean

Failure flag; True if failed to converge, False is successful.

set_design_var(self, name, value)

Set the value of a design variable.

Parameters
namestr

Global pathname of the design variable.

valuefloat or ndarray

Value for the design variable.

use_fixed_coloring(self, coloring=<object object at 0x7fdd46258cf0>)

Tell the driver to use a precomputed coloring.

Parameters
coloringstr

A coloring filename. If no arg is passed, filename will be determined automatically.

openmdao.drivers.scipy_optimizer.signature_extender(fcn, extra_args)[source]

Closure function, which appends extra arguments to the original function call.

The first argument is the design vector. The possible extra arguments from the callback of scipy.optimize.minimize() are not passed to the function.

Some algorithms take a sequence of NonlinearConstraint as input for the constraints. For this class it is not possible to pass additional arguments. With this function the signature will be correct for both scipy and the driver.

Parameters
fcncallable

Function, which takes the design vector as the first argument.

extra_argstuple or list

Extra arguments for the function

Returns
callable

The function with the signature expected by the driver.