API Reference

This page is auto-generated from the source code and is intended to be used as an API reference. See Quickstart Guide to get started the easy way.

MetaOptimizationProblem

class rtc_tools_meta.meta_optimization_problem.MetaOptimizationProblem(**kwargs)

Bases: rtctools.optimization.optimization_problem.OptimizationProblem

An optimization problem of aggregated submodels and a global objective

Use this class to define a meta optimization problem. A meta problem consists of submodels, which interact via variable_mappings.

To optimize the meta model, call the optimize() method. This will:

1. Optimize aggregated model
2. Impose interface constraints on submodels
3. Optimize submodels

Results will be located in their respective submodel output directories.

interface_variables

Tuple of interface variables

Note: This property is extracted from self.variable_mappings

Return type:Tuple[str]

meta_objective()

Symbolic expression to be minimized

For example, here is a quadratic minimization objective:

def meta_objective(self):
    return ca.sum1(self.states_in("ModelA::var_name")) ** 2.0

Return type:MX

optimize(preprocessing=True, postprocessing=True, log_solver_failure_as_error=True)

Perform one initialize-transcribe-solve-finalize cycle.

Parameters:

• preprocessing – True to enable a call to pre preceding the optimization.
• postprocessing – True to enable a call to post following the optimization.

Returns:

True on success.

pop_submodel(variable)

Function to split submodel name from variable

The submodel a variable is stored in is indicated by preceding that variable’s name with the model name, seperated by ::. For example,

>>> self.pop_submodel("submodel_name::variable_name")
("submodel_name", "variable_name")
>>> self.pop_submodel("submodelA_name::submodelB_name::variable_name")
("submodelA_name", "submodelB_name::variable_name")  # Nested models

Parameters:variable (str) – Submodel name and variable name in a single string Return type:Tuple[str, str] Returns:Submodel name and variable name as separate strings Raises:AssertionError, ValueError

post()

Execute post() method of all submodels

pre()

Execute pre() method of all submodels

submodels

Dictionary of submodels

This variable is where we store the submodel instances. The models are indexed by a unique short name. For example:

# Meta Problem Optimizing ModelA and ModelB
submodels = {
    "ModelA": collect_model_instance('ModelA', path_to_a),
    "ModelB": collect_model_instance('ModelB', path_to_b),
}

Return type:Dict[str, OptimizationProblem]

transcribe()

Transcribe all submodels and aggregate them into a single nlp problem

variable_mappings

Mappings that constrain variables in separate submodels to be equal

To define how submodels interact with each other, we set variable_mappings equal to a list of 3-tuples. The 3-tuples have the structure (var1, var2, sign). For example:

# Meta Problem Optimizing ModelA and ModelB
variable_mappings = [
    (
        "ModelA::some_variable",
        "ModelB::another_variable",
        "-",  # Either "-" (if vars are equal) or "+" (if they sum to 0)
    )
]

Return type:List[Tuple[str, str, str]]

MetaMixin

class rtc_tools_meta.meta_mixin.MetaMixin(**kwargs)

Bases: rtctools.optimization.optimization_problem.OptimizationProblem

Adds Compatibility with MetaOptimizationProblem models

This mixin allows MetaOptimizationProblem models to assimilate your RTC-Tools model into its meta problem and impose constraints back onto your model. Resistance is futile.

collect_model_instance()

rtc_tools_meta.util.collect_model_instance(model_name, module_path, base_folder='..')

Collect an object from a python file anywhere in your file system

Parameters:

• model_name (str) – Name of the model that you want to collect and instantiate
• module_path (str) – Path to the file where the model is defined
• base_folder (str) – Path to the base folder of the model. Default is ".."

Raises:

AssertionError