DMDO package

Submodules

OMADS.POLL module

class DMDO.DMDO.BMMDO[source]

Bases: object

Simple testing problems

__init__() → None

class DMDO.DMDO.USER[source]

Bases: object

Custom class to introduce user data

__init__() → None

DMDO.DMDO.user: alias of DMDO.DMDO.USER

class DMDO.DMDO.double_precision(decimals: int)[source]

Bases: object

decimals: int: Decimal precision control

truncate(value: float) → float[source]

__init__(decimals: int) → None

class DMDO.DMDO.VAR_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

CONTINUOUS = 1

INTEGER = 2

BINARY = 3

CATEGORICAL = 4

ORDINAL = 5

class DMDO.DMDO.VALIDATOR(value)[source]

Bases: enum.Enum

An enumeration.

PRE = 1

RUNNING = 2

POST = 3

class DMDO.DMDO.BARRIER_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

EXTREME = 1

PROGRESSIVE = 2

FILTER = 3

class DMDO.DMDO.PSIZE_UPDATE(value)[source]

Bases: enum.Enum

An enumeration.

DEFAULT = 1

SUCCESS = 2

MAX = 3

LAST = 4

class DMDO.DMDO.w_scheme(value)[source]

Bases: enum.Enum

An enumeration.

MEDIAN = 1

MAX = 2

NORMAL = 3

RANK = 4

class DMDO.DMDO.MODEL_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

SURROGATE = 1

DATA = 2

SIMULATION = 3

NEUTRAL = 4

class DMDO.DMDO.COUPLING_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

SHARED = 1

FEEDBACK = 2

FEEDFORWARD = 3

UNCOUPLED = 4

DUMMY = 5

class DMDO.DMDO.COUPLING_STRENGTH(value)[source]

Bases: enum.Enum

An enumeration.

TIGHT = 1

LOOSE = 2

class DMDO.DMDO.MDO_ARCHITECTURE(value)[source]

Bases: enum.Enum

An enumeration.

MDF = 1

IDF = 2

class DMDO.DMDO.variableData(name: str, sp_index: int, coupling_type: int, link: str, dim: int, value: float, baseline: float, scaling: float, lb: float = None, ub: float = None, type: int = <VAR_TYPE.CONTINUOUS: 1>, index: int = None)[source]

Bases: object

name: str

sp_index: int

coupling_type: int

link: str

dim: int

value: float

baseline: float

scaling: float

lb: float = None

ub: float = None

type: int = 1

index: int = None

__init__(name: str, sp_index: int, coupling_type: int, link: str, dim: int, value: float, baseline: float, scaling: float, lb: Optional[float] = None, ub: Optional[float] = None, type: int = VAR_TYPE.CONTINUOUS, index: Optional[int] = None) → None

class DMDO.DMDO.Process_data(*args, **kwargs)[source]

Bases: Protocol

term_critteria: List[Callable]

term_type: List[int]

term_status: List[bool]

variables: List[DMDO.DMDO.variableData]

responses: List[DMDO.DMDO.variableData]

__init__(*args, **kwargs)

class DMDO.DMDO.coordinator(*args, **kwargs)[source]

Bases: Protocol

clone_point()[source]

create_linking_list()[source]

calc_inconsistency()[source]

calc_penalty()[source]

update_multipliers()[source]

__init__(*args, **kwargs)

class DMDO.DMDO.process(*args, **kwargs)[source]

Bases: DMDO.DMDO.Process_data, Protocol

run()[source]

validation()[source]

setInputs()[source]

getOutputs()[source]

setup()[source]

__init__(*args, **kwargs)

class DMDO.DMDO.search(*args, **kwargs)[source]

Bases: Protocol

evaluateSamples()[source]

run()[source]

__init__(*args, **kwargs)

class DMDO.DMDO.ModelInadequacyData(type: int, relative_inadequacies: numpy.ndarray, absolute_inadequacies: numpy.ndarray, approx_rel_inadeq: numpy.ndarray, approx_abs_inadeq: numpy.ndarray, reference_model_index: int, errorSurrogateType: int)[source]

Bases: object

type: int

relative_inadequacies: numpy.ndarray

absolute_inadequacies: numpy.ndarray

approx_rel_inadeq: numpy.ndarray

approx_abs_inadeq: numpy.ndarray

reference_model_index: int

errorSurrogateType: int

__init__(type: int, relative_inadequacies: numpy.ndarray, absolute_inadequacies: numpy.ndarray, approx_rel_inadeq: numpy.ndarray, approx_abs_inadeq: numpy.ndarray, reference_model_index: int, errorSurrogateType: int) → None

class DMDO.DMDO.DA_Data(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: int = None, timeout: int = 1000000)[source]

Bases: object

inputs: List[DMDO.DMDO.variableData]

outputs: List[DMDO.DMDO.variableData]

blackbox: Callable

links: List[int]

coupling_type: List[int]

preCondition: Optional[Callable]

runningCondition: Optional[Callable]

postCondition: Optional[Callable]

model: Optional[object]

modelType: Optional[int]

validation_list: Optional[List[Callable]]

validation_type: Optional[List[int]]

validation_status: Optional[List[bool]]

index: int = None

timeout: int = 1000000

__init__(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: Optional[int] = None, timeout: int = 1000000) → None

class DMDO.DMDO.optimizationData(objectives: List[Callable], constraints: List[Callable], objectiveWeights: List[Any], constraintsHandling: int, solver: Callable)[source]

Bases: object

objectives: List[Callable]

constraints: List[Callable]

objectiveWeights: List[Any]

constraintsHandling: int

solver: Callable

__init__(objectives: List[Callable], constraints: List[Callable], objectiveWeights: List[Any], constraintsHandling: int, solver: Callable) → None

class DMDO.DMDO.DA(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: int = None, timeout: int = 1000000)[source]

Bases: DMDO.DMDO.DA_Data

setup(input)[source]

run()[source]

validation(vType: int)[source]

setInputsValue(values: List[Any])[source]

setOutputsValue(values: Any)[source]

getInputsList()[source]

getOutputs()[source]

getOutputsList()[source]

__init__(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: Optional[int] = None, timeout: int = 1000000) → None

class DMDO.DMDO.MDA_data(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: int = None)[source]

Bases: DMDO.DMDO.Process_data

nAnalyses: int

analyses: List[DMDO.DMDO.DA]

index: int = None

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: Optional[int] = None) → None

class DMDO.DMDO.MDA(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: int = None)[source]

Bases: DMDO.DMDO.MDA_data

setup(input)[source]

run()[source]

validation(vType: int)[source]

setInputs(values: List[Any])[source]

getOutputs()[source]

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: Optional[int] = None) → None

class DMDO.DMDO.coordinationData(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: Any = 10.0, mode: str = 'serial', var_group: List[DMDO.DMDO.variableData] = <factory>, _linker: List[List[int]] = <factory>, n_dim: int = 0, index: int = None)[source]

Bases: object

nsp: int

budget: int = 20

index_of_master_SP: int = 1

display: bool = True

scaling: Any = 10.0

mode: str = 'serial'

var_group: List[DMDO.DMDO.variableData]

n_dim: int = 0

index: int = None

__init__(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: typing.Any = 10.0, mode: str = 'serial', var_group: typing.List[DMDO.DMDO.variableData] = <factory>, _linker: typing.List[typing.List[int]] = <factory>, n_dim: int = 0, index: typing.Optional[int] = None) → None

class DMDO.DMDO.ADMM_data(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: Any = 10.0, mode: str = 'serial', var_group: List[DMDO.DMDO.variableData] = <factory>, _linker: List[List[int]] = <factory>, n_dim: int = 0, index: int = None, beta: float = 1.3, gamma: float = 0.5, q: numpy.ndarray = array([], shape=(0, 0), dtype=float64), qold: numpy.ndarray = array([], shape=(0, 0), dtype=float64), phi: float = 1.0, v: numpy.ndarray = array([], shape=(0, 0), dtype=float64), w: numpy.ndarray = array([], shape=(0, 0), dtype=float64), update_w: bool = False, M_update_scheme: int = <w_scheme.MEDIAN: 1>, eps_qo: List = None, save_q_in: bool = False, save_q_in_out: bool = False, eps_fo: List = None)[source]

Bases: DMDO.DMDO.coordinationData

beta: float = 1.3

gamma: float = 0.5

q: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

qold: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

phi: float = 1.0

v: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

w: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

update_w: bool = False

M_update_scheme: int = 1

eps_qo: List = None

save_q_in: bool = False

save_q_in_out: bool = False

eps_fo: List = None

__init__(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: typing.Any = 10.0, mode: str = 'serial', var_group: typing.List[DMDO.DMDO.variableData] = <factory>, _linker: typing.List[typing.List[int]] = <factory>, n_dim: int = 0, index: typing.Optional[int] = None, beta: float = 1.3, gamma: float = 0.5, q: numpy.ndarray = array([], shape=(0, 0), dtype=float64), qold: numpy.ndarray = array([], shape=(0, 0), dtype=float64), phi: float = 1.0, v: numpy.ndarray = array([], shape=(0, 0), dtype=float64), w: numpy.ndarray = array([], shape=(0, 0), dtype=float64), update_w: bool = False, M_update_scheme: int = w_scheme.MEDIAN, eps_qo: typing.Optional[typing.List] = None, save_q_in: bool = False, save_q_in_out: bool = False, eps_fo: typing.Optional[typing.List] = None) → None

class DMDO.DMDO.ADMM(nsp, beta, budget, index_of_master_SP, display, scaling, mode, M_update_scheme, store_q_o=False, store_q_io=False, index=None)[source]

Bases: DMDO.DMDO.ADMM_data

Alternating directions method of multipliers

__init__(nsp, beta, budget, index_of_master_SP, display, scaling, mode, M_update_scheme, store_q_o=False, store_q_io=False, index=None)[source]

clone_point(p: DMDO.DMDO.variableData)[source]

create_linking_list()[source]

calc_inconsistency()[source]

calc_inconsistency_old()[source]

update_master_vector(vars: List[DMDO.DMDO.variableData], resps: List[DMDO.DMDO.variableData])[source]

calc_penalty(q_indices)[source]

update_multipliers()[source]

class DMDO.DMDO.partitionedProblemData[source]

Bases: object

nv: int

nr: int

sp_index: int

vars: List[DMDO.DMDO.variableData]

resps: List[DMDO.DMDO.variableData]

is_main: bool

MDA_process: DMDO.DMDO.process

coupling: List[float]

solution: List[Any]

solver: Any

realistic_objective: bool = False

optFunctions: List[Callable] = None

obj: float = inf

constraints: List[float] = [inf]

frealistic: float = 0.0

scaling: float = 10.0

coord: DMDO.DMDO.ADMM

opt: Callable

fmin_nop: float

budget: int

display: bool

psize: float

psize_init: int

tol: float

scipy: Dict

class DMDO.DMDO.SubProblem(nv, index, vars, resps, is_main, analysis, coordination, opt, fmin_nop, budget, display, psize, pupdate, scipy=None, freal=None, tol=1e-12, solver='OMADS')[source]

Bases: DMDO.DMDO.partitionedProblemData

__init__(nv, index, vars, resps, is_main, analysis, coordination, opt, fmin_nop, budget, display, psize, pupdate, scipy=None, freal=None, tol=1e-12, solver='OMADS')[source]

get_minimizer()[source]

get_coupling_vars()[source]

get_design_vars()[source]

set_variables_value(vlist: List)[source]

set_pair()[source]

getLocalIndices()[source]

evaluate(vlist: List[float])[source]

solve(v, w)[source]

get_coupling_vars_diff(con)[source]

set_dependent_vars(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars_ub(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars_lb(vars: List[DMDO.DMDO.variableData])[source]

get_design_vars_scaling(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars_names(vars: List[DMDO.DMDO.variableData])[source]

class DMDO.DMDO.MDO_data(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: List[float] = None, eps_fio: List[float] = None)[source]

Bases: DMDO.DMDO.Process_data

Architecture: int

Coordinator: DMDO.DMDO.ADMM

subProblems: List[DMDO.DMDO.SubProblem]

fmin: float

hmin: float

display: bool

inc_stop: float

stop: str

tab_inc: List

noprogress_stop: int

eps_qio: List[float] = None

eps_fio: List[float] = None

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: Optional[List[float]] = None, eps_fio: Optional[List[float]] = None) → None

class DMDO.DMDO.MDO(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: List[float] = None, eps_fio: List[float] = None)[source]

Bases: DMDO.DMDO.MDO_data

setup(input)[source]

get_list_of_var_values()[source]

get_master_vars_difference()[source]

check_termination_critt(iter)[source]

run()[source]

validation(vType: int)[source]

setInputs(values: List[Any])[source]

getOutputs()[source]

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: Optional[List[float]] = None, eps_fio: Optional[List[float]] = None) → None

class DMDO.DMDO.problemSetup(data: Dict = None, Vs: List[DMDO.DMDO.variableData] = None, DAs: List[DMDO.DMDO.process] = None, MDAs: List[DMDO.DMDO.process] = None, Coords: List[DMDO.DMDO.coordinator] = None, SPs: List[DMDO.DMDO.SubProblem] = None, MDAO: DMDO.DMDO.MDO = None, Qscaling: List = None, userData: DMDO.DMDO.USER = None)[source]

Bases: object

data: Dict = None

Vs: List[DMDO.DMDO.variableData] = None

DAs: List[DMDO.DMDO.process] = None

MDAs: List[DMDO.DMDO.process] = None

Coords: List[DMDO.DMDO.coordinator] = None

SPs: List[DMDO.DMDO.SubProblem] = None

MDAO: DMDO.DMDO.MDO = None

Qscaling: List = None

userData: DMDO.DMDO.USER = None

setup_couplingList() → List[source]: Build the list of the coupling types

getWupdateScheme(inp: str) → int[source]

getCouplingType(inp: str) → int[source]

getPollUpdate(inp: str) → int[source]

getMDOArch(inp: str) → int[source]

variablesSetup()[source]: Setup the list of global variables struct

getVariables(v: List[str]) → List[DMDO.DMDO.variableData][source]: Find the variables by the input key assigned to them

getDA(d: List[int]) → List[DMDO.DMDO.DA][source]: Get the list of corresponding disciplinary analyses

getSPs(sp: List[int]) → List[DMDO.DMDO.SubProblem][source]: Get the list of corresponding disciplinary analyses

getMDA(M: int) → DMDO.DMDO.MDA[source]: Get the MDA process from the provided index

getCoord(c: int) → DMDO.DMDO.ADMM[source]: Get the coordinator from the provided index

setBlackboxes(bb: str, bb_type: str, copts: Optional[str] = None)[source]

DASetup()[source]: Setup discipliary Analyses

MDASetup()[source]: Setup the MDA process

COORDSetup()[source]: Setup coordinators definition

SPSetup()[source]: Setup subproblems definition

MDOSetup()[source]: Setup MDO process

UserData()[source]: Set user data attr

__init__(data: Optional[Dict] = None, Vs: Optional[List[DMDO.DMDO.variableData]] = None, DAs: Optional[List[DMDO.DMDO.process]] = None, MDAs: Optional[List[DMDO.DMDO.process]] = None, Coords: Optional[List[DMDO.DMDO.coordinator]] = None, SPs: Optional[List[DMDO.DMDO.SubProblem]] = None, MDAO: Optional[DMDO.DMDO.MDO] = None, Qscaling: Optional[List] = None, userData: Optional[DMDO.DMDO.USER] = None) → None

autoProbSetup() → DMDO.DMDO.MDO[source]: Setup the MDO problem

DMDO.DMDO.main(*args) → Dict[str, Any][source]: The DMDO main routine

Module contents

class DMDO.BMMDO[source]

Bases: object

Simple testing problems

__init__() → None

class DMDO.USER[source]

Bases: object

Custom class to introduce user data

__init__() → None

class DMDO.double_precision(decimals: int)[source]

Bases: object

decimals: int: Decimal precision control

truncate(value: float) → float[source]

__init__(decimals: int) → None

class DMDO.VAR_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

CONTINUOUS = 1

INTEGER = 2

BINARY = 3

CATEGORICAL = 4

ORDINAL = 5

class DMDO.VALIDATOR(value)[source]

Bases: enum.Enum

An enumeration.

PRE = 1

RUNNING = 2

POST = 3

class DMDO.BARRIER_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

EXTREME = 1

PROGRESSIVE = 2

FILTER = 3

class DMDO.PSIZE_UPDATE(value)[source]

Bases: enum.Enum

An enumeration.

DEFAULT = 1

SUCCESS = 2

MAX = 3

LAST = 4

class DMDO.w_scheme(value)[source]

Bases: enum.Enum

An enumeration.

MEDIAN = 1

MAX = 2

NORMAL = 3

RANK = 4

class DMDO.MODEL_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

SURROGATE = 1

DATA = 2

SIMULATION = 3

NEUTRAL = 4

class DMDO.COUPLING_STRENGTH(value)[source]

Bases: enum.Enum

An enumeration.

TIGHT = 1

LOOSE = 2

class DMDO.COUPLING_TYPE(value)[source]

Bases: enum.Enum

An enumeration.

SHARED = 1

FEEDBACK = 2

FEEDFORWARD = 3

UNCOUPLED = 4

DUMMY = 5

class DMDO.MDO_ARCHITECTURE(value)[source]

Bases: enum.Enum

An enumeration.

MDF = 1

IDF = 2

class DMDO.variableData(name: str, sp_index: int, coupling_type: int, link: str, dim: int, value: float, baseline: float, scaling: float, lb: float = None, ub: float = None, type: int = <VAR_TYPE.CONTINUOUS: 1>, index: int = None)[source]

Bases: object

name: str

sp_index: int

coupling_type: int

link: str

dim: int

value: float

baseline: float

scaling: float

lb: float = None

ub: float = None

type: int = 1

index: int = None

__init__(name: str, sp_index: int, coupling_type: int, link: str, dim: int, value: float, baseline: float, scaling: float, lb: Optional[float] = None, ub: Optional[float] = None, type: int = VAR_TYPE.CONTINUOUS, index: Optional[int] = None) → None

class DMDO.Process_data(*args, **kwargs)[source]

Bases: Protocol

term_critteria: List[Callable]

term_type: List[int]

term_status: List[bool]

variables: List[DMDO.DMDO.variableData]

responses: List[DMDO.DMDO.variableData]

__init__(*args, **kwargs)

class DMDO.coordinationData(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: Any = 10.0, mode: str = 'serial', var_group: List[DMDO.DMDO.variableData] = <factory>, _linker: List[List[int]] = <factory>, n_dim: int = 0, index: int = None)[source]

Bases: object

nsp: int

budget: int = 20

index_of_master_SP: int = 1

display: bool = True

scaling: Any = 10.0

mode: str = 'serial'

var_group: List[DMDO.DMDO.variableData]

n_dim: int = 0

index: int = None

__init__(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: typing.Any = 10.0, mode: str = 'serial', var_group: typing.List[DMDO.DMDO.variableData] = <factory>, _linker: typing.List[typing.List[int]] = <factory>, n_dim: int = 0, index: typing.Optional[int] = None) → None

class DMDO.coordinator(*args, **kwargs)[source]

Bases: Protocol

clone_point()[source]

create_linking_list()[source]

calc_inconsistency()[source]

calc_penalty()[source]

update_multipliers()[source]

__init__(*args, **kwargs)

class DMDO.process(*args, **kwargs)[source]

Bases: DMDO.DMDO.Process_data, Protocol

run()[source]

validation()[source]

setInputs()[source]

getOutputs()[source]

setup()[source]

__init__(*args, **kwargs)

class DMDO.search(*args, **kwargs)[source]

Bases: Protocol

evaluateSamples()[source]

run()[source]

__init__(*args, **kwargs)

class DMDO.ModelInadequacyData(type: int, relative_inadequacies: numpy.ndarray, absolute_inadequacies: numpy.ndarray, approx_rel_inadeq: numpy.ndarray, approx_abs_inadeq: numpy.ndarray, reference_model_index: int, errorSurrogateType: int)[source]

Bases: object

type: int

relative_inadequacies: numpy.ndarray

absolute_inadequacies: numpy.ndarray

approx_rel_inadeq: numpy.ndarray

approx_abs_inadeq: numpy.ndarray

reference_model_index: int

errorSurrogateType: int

__init__(type: int, relative_inadequacies: numpy.ndarray, absolute_inadequacies: numpy.ndarray, approx_rel_inadeq: numpy.ndarray, approx_abs_inadeq: numpy.ndarray, reference_model_index: int, errorSurrogateType: int) → None

class DMDO.DA_Data(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: int = None, timeout: int = 1000000)[source]

Bases: object

inputs: List[DMDO.DMDO.variableData]

outputs: List[DMDO.DMDO.variableData]

blackbox: Callable

links: List[int]

coupling_type: List[int]

preCondition: Optional[Callable]

runningCondition: Optional[Callable]

postCondition: Optional[Callable]

model: Optional[object]

modelType: Optional[int]

validation_list: Optional[List[Callable]]

validation_type: Optional[List[int]]

validation_status: Optional[List[bool]]

index: int = None

timeout: int = 1000000

__init__(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: Optional[int] = None, timeout: int = 1000000) → None

class DMDO.optimizationData(objectives: List[Callable], constraints: List[Callable], objectiveWeights: List[Any], constraintsHandling: int, solver: Callable)[source]

Bases: object

objectives: List[Callable]

constraints: List[Callable]

objectiveWeights: List[Any]

constraintsHandling: int

solver: Callable

__init__(objectives: List[Callable], constraints: List[Callable], objectiveWeights: List[Any], constraintsHandling: int, solver: Callable) → None

class DMDO.DA(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: int = None, timeout: int = 1000000)[source]

Bases: DMDO.DMDO.DA_Data

setup(input)[source]

run()[source]

validation(vType: int)[source]

setInputsValue(values: List[Any])[source]

setOutputsValue(values: Any)[source]

getInputsList()[source]

getOutputs()[source]

getOutputsList()[source]

__init__(inputs: List[DMDO.DMDO.variableData], outputs: List[DMDO.DMDO.variableData], blackbox: Callable, links: List[int], coupling_type: List[int], index: Optional[int] = None, timeout: int = 1000000) → None

class DMDO.MDA_data(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: int = None)[source]

Bases: DMDO.DMDO.Process_data

nAnalyses: int

analyses: List[DMDO.DMDO.DA]

index: int = None

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: Optional[int] = None) → None

term_critteria: List[Callable]

term_type: List[int]

term_status: List[bool]

variables: List[DMDO.DMDO.variableData]

responses: List[DMDO.DMDO.variableData]

class DMDO.MDA(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: int = None)[source]

Bases: DMDO.DMDO.MDA_data

setup(input)[source]

run()[source]

validation(vType: int)[source]

setInputs(values: List[Any])[source]

getOutputs()[source]

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], nAnalyses: int, analyses: List[DMDO.DMDO.DA], index: Optional[int] = None) → None

class DMDO.ADMM(nsp, beta, budget, index_of_master_SP, display, scaling, mode, M_update_scheme, store_q_o=False, store_q_io=False, index=None)[source]

Bases: DMDO.DMDO.ADMM_data

Alternating directions method of multipliers

__init__(nsp, beta, budget, index_of_master_SP, display, scaling, mode, M_update_scheme, store_q_o=False, store_q_io=False, index=None)[source]

clone_point(p: DMDO.DMDO.variableData)[source]

create_linking_list()[source]

calc_inconsistency()[source]

calc_inconsistency_old()[source]

update_master_vector(vars: List[DMDO.DMDO.variableData], resps: List[DMDO.DMDO.variableData])[source]

calc_penalty(q_indices)[source]

update_multipliers()[source]

class DMDO.ADMM_data(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: Any = 10.0, mode: str = 'serial', var_group: List[DMDO.DMDO.variableData] = <factory>, _linker: List[List[int]] = <factory>, n_dim: int = 0, index: int = None, beta: float = 1.3, gamma: float = 0.5, q: numpy.ndarray = array([], shape=(0, 0), dtype=float64), qold: numpy.ndarray = array([], shape=(0, 0), dtype=float64), phi: float = 1.0, v: numpy.ndarray = array([], shape=(0, 0), dtype=float64), w: numpy.ndarray = array([], shape=(0, 0), dtype=float64), update_w: bool = False, M_update_scheme: int = <w_scheme.MEDIAN: 1>, eps_qo: List = None, save_q_in: bool = False, save_q_in_out: bool = False, eps_fo: List = None)[source]

Bases: DMDO.DMDO.coordinationData

beta: float = 1.3

gamma: float = 0.5

q: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

qold: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

phi: float = 1.0

v: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

w: numpy.ndarray = array([], shape=(0, 0), dtype=float64)

update_w: bool = False

M_update_scheme: int = 1

eps_qo: List = None

save_q_in: bool = False

save_q_in_out: bool = False

eps_fo: List = None

__init__(nsp: int, budget: int = 20, index_of_master_SP: int = 1, display: bool = True, scaling: typing.Any = 10.0, mode: str = 'serial', var_group: typing.List[DMDO.DMDO.variableData] = <factory>, _linker: typing.List[typing.List[int]] = <factory>, n_dim: int = 0, index: typing.Optional[int] = None, beta: float = 1.3, gamma: float = 0.5, q: numpy.ndarray = array([], shape=(0, 0), dtype=float64), qold: numpy.ndarray = array([], shape=(0, 0), dtype=float64), phi: float = 1.0, v: numpy.ndarray = array([], shape=(0, 0), dtype=float64), w: numpy.ndarray = array([], shape=(0, 0), dtype=float64), update_w: bool = False, M_update_scheme: int = w_scheme.MEDIAN, eps_qo: typing.Optional[typing.List] = None, save_q_in: bool = False, save_q_in_out: bool = False, eps_fo: typing.Optional[typing.List] = None) → None

nsp: int

var_group: List[DMDO.DMDO.variableData]

class DMDO.partitionedProblemData[source]

Bases: object

nv: int

nr: int

sp_index: int

vars: List[DMDO.DMDO.variableData]

resps: List[DMDO.DMDO.variableData]

is_main: bool

MDA_process: DMDO.DMDO.process

coupling: List[float]

solution: List[Any]

solver: Any

realistic_objective: bool = False

optFunctions: List[Callable] = None

obj: float = inf

constraints: List[float] = [inf]

frealistic: float = 0.0

scaling: float = 10.0

coord: DMDO.DMDO.ADMM

opt: Callable

fmin_nop: float

budget: int

display: bool

psize: float

psize_init: int

tol: float

scipy: Dict

class DMDO.SubProblem(nv, index, vars, resps, is_main, analysis, coordination, opt, fmin_nop, budget, display, psize, pupdate, scipy=None, freal=None, tol=1e-12, solver='OMADS')[source]

Bases: DMDO.DMDO.partitionedProblemData

__init__(nv, index, vars, resps, is_main, analysis, coordination, opt, fmin_nop, budget, display, psize, pupdate, scipy=None, freal=None, tol=1e-12, solver='OMADS')[source]

get_minimizer()[source]

get_coupling_vars()[source]

get_design_vars()[source]

set_variables_value(vlist: List)[source]

set_pair()[source]

getLocalIndices()[source]

evaluate(vlist: List[float])[source]

solve(v, w)[source]

get_coupling_vars_diff(con)[source]

set_dependent_vars(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars_ub(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars_lb(vars: List[DMDO.DMDO.variableData])[source]

get_design_vars_scaling(vars: List[DMDO.DMDO.variableData])[source]

get_list_vars_names(vars: List[DMDO.DMDO.variableData])[source]

class DMDO.MDO_data(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: List[float] = None, eps_fio: List[float] = None)[source]

Bases: DMDO.DMDO.Process_data

Architecture: int

Coordinator: DMDO.DMDO.ADMM

subProblems: List[DMDO.DMDO.SubProblem]

fmin: float

hmin: float

display: bool

inc_stop: float

stop: str

tab_inc: List

noprogress_stop: int

eps_qio: List[float] = None

eps_fio: List[float] = None

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: Optional[List[float]] = None, eps_fio: Optional[List[float]] = None) → None

term_critteria: List[Callable]

term_type: List[int]

term_status: List[bool]

variables: List[DMDO.DMDO.variableData]

responses: List[DMDO.DMDO.variableData]

class DMDO.MDO(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: List[float] = None, eps_fio: List[float] = None)[source]

Bases: DMDO.DMDO.MDO_data

setup(input)[source]

get_list_of_var_values()[source]

get_master_vars_difference()[source]

check_termination_critt(iter)[source]

run()[source]

validation(vType: int)[source]

setInputs(values: List[Any])[source]

getOutputs()[source]

__init__(variables: List[DMDO.DMDO.variableData], responses: List[DMDO.DMDO.variableData], Architecture: int, Coordinator: DMDO.DMDO.ADMM, subProblems: List[DMDO.DMDO.SubProblem], fmin: float, hmin: float, display: bool, inc_stop: float, stop: str, tab_inc: List, noprogress_stop: int, eps_qio: Optional[List[float]] = None, eps_fio: Optional[List[float]] = None) → None

class DMDO.problemSetup(data: Dict = None, Vs: List[DMDO.DMDO.variableData] = None, DAs: List[DMDO.DMDO.process] = None, MDAs: List[DMDO.DMDO.process] = None, Coords: List[DMDO.DMDO.coordinator] = None, SPs: List[DMDO.DMDO.SubProblem] = None, MDAO: DMDO.DMDO.MDO = None, Qscaling: List = None, userData: DMDO.DMDO.USER = None)[source]

Bases: object

data: Dict = None

Vs: List[DMDO.DMDO.variableData] = None

DAs: List[DMDO.DMDO.process] = None

MDAs: List[DMDO.DMDO.process] = None

Coords: List[DMDO.DMDO.coordinator] = None

SPs: List[DMDO.DMDO.SubProblem] = None

MDAO: DMDO.DMDO.MDO = None

Qscaling: List = None

userData: DMDO.DMDO.USER = None

setup_couplingList() → List[source]: Build the list of the coupling types

getWupdateScheme(inp: str) → int[source]

getCouplingType(inp: str) → int[source]

getPollUpdate(inp: str) → int[source]

getMDOArch(inp: str) → int[source]

variablesSetup()[source]: Setup the list of global variables struct

getVariables(v: List[str]) → List[DMDO.DMDO.variableData][source]: Find the variables by the input key assigned to them

getDA(d: List[int]) → List[DMDO.DMDO.DA][source]: Get the list of corresponding disciplinary analyses

getSPs(sp: List[int]) → List[DMDO.DMDO.SubProblem][source]: Get the list of corresponding disciplinary analyses

getMDA(M: int) → DMDO.DMDO.MDA[source]: Get the MDA process from the provided index

getCoord(c: int) → DMDO.DMDO.ADMM[source]: Get the coordinator from the provided index

setBlackboxes(bb: str, bb_type: str, copts: Optional[str] = None)[source]

DASetup()[source]: Setup discipliary Analyses

MDASetup()[source]: Setup the MDA process

COORDSetup()[source]: Setup coordinators definition

SPSetup()[source]: Setup subproblems definition

MDOSetup()[source]: Setup MDO process

UserData()[source]: Set user data attr

__init__(data: Optional[Dict] = None, Vs: Optional[List[DMDO.DMDO.variableData]] = None, DAs: Optional[List[DMDO.DMDO.process]] = None, MDAs: Optional[List[DMDO.DMDO.process]] = None, Coords: Optional[List[DMDO.DMDO.coordinator]] = None, SPs: Optional[List[DMDO.DMDO.SubProblem]] = None, MDAO: Optional[DMDO.DMDO.MDO] = None, Qscaling: Optional[List] = None, userData: Optional[DMDO.DMDO.USER] = None) → None

autoProbSetup() → DMDO.DMDO.MDO[source]: Setup the MDO problem

DMDO.main(*args) → Dict[str, Any][source]: The DMDO main routine