ks_comp.py

ks_comp.py#

KS Function Component.

class openmdao.components.ks_comp.KSComp(**kwargs)[source]

Bases: ExplicitComponent

KS function component.

Component that aggregates a number of functions to a single value via the Kreisselmeier-Steinhauser Function. This new constraint is satisfied when it is less than or equal to zero.

Parameters:

**kwargsdict of keyword arguments
Keyword arguments that will be mapped into the Component options.

Attributes:

citestr
Listing of relevant citations that should be referenced when publishing work that uses this class.

Methods

abs_meta_iter(iotype[, local, cont, discrete])

Iterate over absolute variable names and their metadata for this System.

add_constraint(name[, lower, upper, equals, ...])

Add a constraint variable to this system.

add_design_var(name[, lower, upper, ref, ...])

Add a design variable to this system.

add_discrete_input(name, val[, desc, tags, ...])

Add a discrete input variable to the component.

add_discrete_output(name, val[, desc, tags, ...])

Add an output variable to the component.

add_input(name[, val, shape, units, desc, ...])

Add an input variable to the component.

add_objective(name[, ref, ref0, index, ...])

Add a response variable to this system.

add_output(name[, val, shape, units, ...])

Add an output variable to the component.

add_recorder(recorder[, recurse])

Add a recorder to the system.

add_response(name, type_[, lower, upper, ...])

Add a response variable to this system.

best_partial_deriv_direction()

Return the best direction for partial deriv calculations based on input and output sizes.

check_config(logger)

Perform optional error checks.

check_partials([out_stream, compact_print, ...])

Check partial derivatives comprehensively for this component.

check_sparsity([method, max_nz, out_stream])

Check the sparsity of the computed jacobian against the declared sparsity.

cleanup()

Clean up resources prior to exit.

comm_info_iter()

Yield comm size for this system and all subsystems.

compute(inputs, outputs)

Compute the output of the KS function.

compute_fd_jac(jac[, method])

Force the use of finite difference to compute a jacobian.

compute_fd_sparsity([method, num_full_jacs, ...])

Use finite difference to compute a sparsity matrix.

compute_jacvec_product(inputs, d_inputs, ...)

Compute jac-vector product.

compute_partials(inputs, partials)

Compute sub-jacobian parts.

compute_sparsity([direction, num_iters, ...])

Compute the sparsity of the partial jacobian.

convert2units(name, val, units)

Convert the given value to the specified units.

convert_from_units(name, val, units)

Convert the given value from the specified units to those of the named variable.

convert_units(name, val, units_from, units_to)

Wrap the utility convert_units and give a good error message.

declare_coloring([wrt, method, form, step, ...])

Set options for deriv coloring of a set of wrt vars matching the given pattern(s).

declare_partials(of, wrt[, dependent, rows, ...])

Declare information about this component's subjacobians.

dist_size_iter(io, top_comm)

Yield names and distributed ranges of all local and remote variables in this system.

get_coloring_fname(mode)

Return the full pathname to a coloring file.

get_conn_graph()

Return the model connection graph.

get_constraints([recurse, get_sizes, ...])

Get the Constraint settings from this system.

get_declare_partials_calls([sparsity])

Return a string containing declare_partials() calls based on the subjac sparsity.

get_design_vars([recurse, get_sizes, ...])

Get the DesignVariable settings from this system.

get_io_metadata([iotypes, metadata_keys, ...])

Retrieve metadata for a filtered list of variables.

get_linear_vectors()

Return the linear inputs, outputs, and residuals vectors.

get_nonlinear_vectors()

Return the inputs, outputs, and residuals vectors.

get_objectives([recurse, get_sizes, ...])

Get the Objective settings from this system.

get_outputs_dir(*subdirs[, mkdir])

Get the path under which all output files of this system are to be placed.

get_promotions([inprom, outprom])

Return all promotions for the given promoted variable(s).

get_reports_dir()

Get the path to the directory where the report files should go.

get_responses([recurse, get_sizes, use_prom_ivc])

Get the response variable settings from this system.

get_self_statics()

Override this in derived classes if compute_primal references static values.

get_source(name)

Return the source variable connected to the given named variable.

get_val(name[, units, indices, get_remote, ...])

Get an output/input/residual variable.

get_var_dup_info(name, io)

Return information about how the given variable is duplicated across MPI processes.

get_var_sizes(name, io)

Return the sizes of the given variable on all procs.

has_vectors()

Check if the system vectors have been initialized.

initialize()

Declare options.

is_explicit([is_comp])

Return True if this is an explicit component.

list_inputs([val, prom_name, units, shape, ...])

Write a list of input names and other optional information to a specified stream.

list_options([include_default, ...])

Write a list of output names and other optional information to a specified stream.

list_outputs([explicit, implicit, val, ...])

Write a list of output names and other optional information to a specified stream.

list_vars([val, prom_name, residuals, ...])

Write a list of inputs and outputs sorted by component in execution order.

load_case(case)

Pull all input and output variables from a Case into this System.

load_model_options()

Load the relevant model options from Problem._metadata['model_options'].

override_method(name, method)

Dynamically add a method to this component instance.

record_iteration()

Record an iteration of the current System.

run_apply_linear(mode[, scope_out, scope_in])

Compute jac-vec product.

run_apply_nonlinear()

Compute residuals.

run_linearize([sub_do_ln])

Compute jacobian / factorization.

run_solve_linear(mode)

Apply inverse jac product.

run_solve_nonlinear()

Compute outputs.

run_validation()

Run validate method on all systems below this system.

set_check_partial_options(wrt[, method, ...])

Set options that will be used for checking partial derivatives.

set_constraint_options(name[, ref, ref0, ...])

Set options for constraints in the model.

set_design_var_options(name[, lower, upper, ...])

Set options for design vars in the model.

set_objective_options(name[, ref, ref0, ...])

Set options for objectives in the model.

set_output_solver_options(name[, lower, ...])

Set solver output options.

set_solver_print([level, depth, type_, ...])

Control printing for solvers and subsolvers in the model.

set_val(name, val[, units, indices])

Set an input or output variable.

setup()

Declare inputs, outputs, and derivatives for the KS component.

setup_partials()

Declare partials.

sparsity_matches_fd([direction, outstream])

Compare the sparsity computed by this system vs.

subjac_sparsity_iter(sparsity[, wrt_matches])

Iterate over sparsity for each subjac in the jacobian.

system_iter([include_self, recurse, typ, ...])

Yield a generator of local subsystems of this system.

total_local_size(io)

Return the total local size of the given variable.

use_fixed_coloring([coloring, recurse])

Use a precomputed coloring for this System.

uses_approx()

Return True if the system uses approximations to compute derivatives.

validate(inputs, outputs[, discrete_inputs, ...])

Check any final input / output values after a run.

__init__(**kwargs)[source]

Initialize the KS component.

compute(inputs, outputs)[source]

Compute the output of the KS function.

Parameters:

inputsVector
Vector containing inputs.

outputsVector
Vector containing outputs.

compute_partials(inputs, partials)[source]

Compute sub-jacobian parts. The model is assumed to be in an unscaled state.

Parameters:

inputsVector
Unscaled, dimensional input variables read via inputs[key].

partialsJacobian
Sub-jac components written to partials[output_name, input_name].

initialize()[source]

Declare options.

setup()[source]

Declare inputs, outputs, and derivatives for the KS component.

class openmdao.components.ks_comp.KSfunction[source]

Bases: object

Helper class for KSComp.

Helper class that can be used to aggregate constraint vectors with a Kreisselmeier-Steinhauser Function.

Methods

compute(g[, rho])

Compute the value of the KS function for the given array of constraints.

derivatives(g[, rho])

Compute elements of [dKS_gd, dKS_drho] for the given array of constraints.

static compute(g, rho=50.0)[source]

Compute the value of the KS function for the given array of constraints.

Parameters:

gndarray
Array of constraint values, where negative means satisfied and positive means violated.

rhofloat
Constraint Aggregation Factor.

Returns:

float
Value of KS function.

static derivatives(g, rho=50.0)[source]

Compute elements of [dKS_gd, dKS_drho] for the given array of constraints.

Parameters:

gndarray
Array of constraint values, where negative means satisfied and positive means violated.

rhofloat
Constraint Aggregation Factor.

Returns:

ndarray
Derivative of KS function with respect to parameter values.

openmdao.components.ks_comp.check_option(option, value)[source]

Check option for validity.

Parameters:

optionstr
The name of the option.

valueany
The value of the option.

Raises:

ValueError

`abs_meta_iter`(iotype[, local, cont, discrete])	Iterate over absolute variable names and their metadata for this System.
`add_constraint`(name[, lower, upper, equals, ...])	Add a constraint variable to this system.
`add_design_var`(name[, lower, upper, ref, ...])	Add a design variable to this system.
`add_discrete_input`(name, val[, desc, tags, ...])	Add a discrete input variable to the component.
`add_discrete_output`(name, val[, desc, tags, ...])	Add an output variable to the component.
`add_input`(name[, val, shape, units, desc, ...])	Add an input variable to the component.
`add_objective`(name[, ref, ref0, index, ...])	Add a response variable to this system.
`add_output`(name[, val, shape, units, ...])	Add an output variable to the component.
`add_recorder`(recorder[, recurse])	Add a recorder to the system.
`add_response`(name, type_[, lower, upper, ...])	Add a response variable to this system.
`best_partial_deriv_direction`()	Return the best direction for partial deriv calculations based on input and output sizes.
`check_config`(logger)	Perform optional error checks.
`check_partials`([out_stream, compact_print, ...])	Check partial derivatives comprehensively for this component.
`check_sparsity`([method, max_nz, out_stream])	Check the sparsity of the computed jacobian against the declared sparsity.
`cleanup`()	Clean up resources prior to exit.
`comm_info_iter`()	Yield comm size for this system and all subsystems.
`compute`(inputs, outputs)	Compute the output of the KS function.
`compute_fd_jac`(jac[, method])	Force the use of finite difference to compute a jacobian.
`compute_fd_sparsity`([method, num_full_jacs, ...])	Use finite difference to compute a sparsity matrix.
`compute_jacvec_product`(inputs, d_inputs, ...)	Compute jac-vector product.
`compute_partials`(inputs, partials)	Compute sub-jacobian parts.
`compute_sparsity`([direction, num_iters, ...])	Compute the sparsity of the partial jacobian.
`convert2units`(name, val, units)	Convert the given value to the specified units.
`convert_from_units`(name, val, units)	Convert the given value from the specified units to those of the named variable.
`convert_units`(name, val, units_from, units_to)	Wrap the utility convert_units and give a good error message.
`declare_coloring`([wrt, method, form, step, ...])	Set options for deriv coloring of a set of wrt vars matching the given pattern(s).
`declare_partials`(of, wrt[, dependent, rows, ...])	Declare information about this component's subjacobians.
`dist_size_iter`(io, top_comm)	Yield names and distributed ranges of all local and remote variables in this system.
`get_coloring_fname`(mode)	Return the full pathname to a coloring file.
`get_conn_graph`()	Return the model connection graph.
`get_constraints`([recurse, get_sizes, ...])	Get the Constraint settings from this system.
`get_declare_partials_calls`([sparsity])	Return a string containing declare_partials() calls based on the subjac sparsity.
`get_design_vars`([recurse, get_sizes, ...])	Get the DesignVariable settings from this system.
`get_io_metadata`([iotypes, metadata_keys, ...])	Retrieve metadata for a filtered list of variables.
`get_linear_vectors`()	Return the linear inputs, outputs, and residuals vectors.
`get_nonlinear_vectors`()	Return the inputs, outputs, and residuals vectors.
`get_objectives`([recurse, get_sizes, ...])	Get the Objective settings from this system.
`get_outputs_dir`(*subdirs[, mkdir])	Get the path under which all output files of this system are to be placed.
`get_promotions`([inprom, outprom])	Return all promotions for the given promoted variable(s).
`get_reports_dir`()	Get the path to the directory where the report files should go.
`get_responses`([recurse, get_sizes, use_prom_ivc])	Get the response variable settings from this system.
`get_self_statics`()	Override this in derived classes if compute_primal references static values.
`get_source`(name)	Return the source variable connected to the given named variable.
`get_val`(name[, units, indices, get_remote, ...])	Get an output/input/residual variable.
`get_var_dup_info`(name, io)	Return information about how the given variable is duplicated across MPI processes.
`get_var_sizes`(name, io)	Return the sizes of the given variable on all procs.
`has_vectors`()	Check if the system vectors have been initialized.
`initialize`()	Declare options.
`is_explicit`([is_comp])	Return True if this is an explicit component.
`list_inputs`([val, prom_name, units, shape, ...])	Write a list of input names and other optional information to a specified stream.
`list_options`([include_default, ...])	Write a list of output names and other optional information to a specified stream.
`list_outputs`([explicit, implicit, val, ...])	Write a list of output names and other optional information to a specified stream.
`list_vars`([val, prom_name, residuals, ...])	Write a list of inputs and outputs sorted by component in execution order.
`load_case`(case)	Pull all input and output variables from a Case into this System.
`load_model_options`()	Load the relevant model options from Problem._metadata['model_options'].
`override_method`(name, method)	Dynamically add a method to this component instance.
`record_iteration`()	Record an iteration of the current System.
`run_apply_linear`(mode[, scope_out, scope_in])	Compute jac-vec product.
`run_apply_nonlinear`()	Compute residuals.
`run_linearize`([sub_do_ln])	Compute jacobian / factorization.
`run_solve_linear`(mode)	Apply inverse jac product.
`run_solve_nonlinear`()	Compute outputs.
`run_validation`()	Run validate method on all systems below this system.
`set_check_partial_options`(wrt[, method, ...])	Set options that will be used for checking partial derivatives.
`set_constraint_options`(name[, ref, ref0, ...])	Set options for constraints in the model.
`set_design_var_options`(name[, lower, upper, ...])	Set options for design vars in the model.
`set_objective_options`(name[, ref, ref0, ...])	Set options for objectives in the model.
`set_output_solver_options`(name[, lower, ...])	Set solver output options.
`set_solver_print`([level, depth, type_, ...])	Control printing for solvers and subsolvers in the model.
`set_val`(name, val[, units, indices])	Set an input or output variable.
`setup`()	Declare inputs, outputs, and derivatives for the KS component.
`setup_partials`()	Declare partials.
`sparsity_matches_fd`([direction, outstream])	Compare the sparsity computed by this system vs.
`subjac_sparsity_iter`(sparsity[, wrt_matches])	Iterate over sparsity for each subjac in the jacobian.
`system_iter`([include_self, recurse, typ, ...])	Yield a generator of local subsystems of this system.
`total_local_size`(io)	Return the total local size of the given variable.
`use_fixed_coloring`([coloring, recurse])	Use a precomputed coloring for this System.
`uses_approx`()	Return True if the system uses approximations to compute derivatives.
`validate`(inputs, outputs[, discrete_inputs, ...])	Check any final input / output values after a run.

`compute`(g[, rho])	Compute the value of the KS function for the given array of constraints.
`derivatives`(g[, rho])	Compute elements of [dKS_gd, dKS_drho] for the given array of constraints.