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to_mip

class CompiledInstance​

CompiledInstance: object return compile_model method.

Attributes​

  • sense(jijmodeling.ProblemSense) : problem sense minimize or maximize.
  • objective(SubstitutedExpression) : objective expression.
  • constraint(dict[str, dict[tuple[int, ...], SubstitutedExpression]]) : constraints. str key represents name of constraint. tuple[int,...] is values of forall index.
  • penalty : dict[str, dict[tuple[int, ...], SubstitutedExpression]]
  • var_map : VariableMap
  • data : InstanceData
  • problem : jijmodeling.InstanceData

Examples​

import jijmodeling as jm
import jijmodeling_transpiler as jmt
n = jm.Placeholder("n")
x = jm.Binary("x", (n, n))
i = jm.Element("i", n)
problem = jm.Problem("sample")
problem += x[:, :]
problem += jm.Constraint("onehot", x[:, i], forall=i)
compiled_instance = jmt.core.compile_model(problem, {"n": 2}, {})
compiled_instance
CompiledInstance(
    objective=SubstitutedExpression(
        linear=LinearSubstitutedExpr(coeff={0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}, constant=0.0),
        nonlinear=None),
    constraint={
        'onehot': {
            (0,): SubstitutedExpression(
                    linear=LinearSubstitutedExpr(coeff={0: 1.0, 1: 1.0}, constant=0.0),
                    nonlinear=None),
            (1,): SubstitutedExpression(
                    linear=LinearSubstitutedExpr(coeff={2: 1.0, 3: 1.0}, constant=0.0),
                    nonlinear=None)}
            },
    penalty={},
    var_map=VariableMap(var_map={'x': {(0, 0): 0, (0, 1): 2, (1, 0): 1, (1, 1): 3}},
                        var_num=4,
                        integer_bound={}),
    ...
)

add (self, source) -> -​

class MIPBuilder​

MIP Builder

Attributes​

  • compiled_model(CompiledInstance) : compiled model.
  • model(mip.Model) : mip model.

decode_from_mip (self, status, model) -> jm.SampleSet​

Decode from mip model.

Parameters​

  • status(mip.OptimizationStatus) : status of optimization.
  • model(mip.Model) : mip model.

Returns​

  • jm.SampleSet : sample set.

get_model (self) -> mip.Model​

Get mip model.

Returns​

  • mip.Model : mip model.

class ProblemSense​

An enumeration.

class SubstitutedExpression​

SubstitutedExpression(coeff: 'dict[tuple[int, ...], float]', constant: 'float', order: 'int')

add (self, other) -> -​

from_serializable (data) -> -​

is_constant (self) -> -​

mul (self, other) -> -​

power (self, exponent) -> -​

to_serializable (self) -> -​

dict_to_record (results, compiled_model) -> jm.Record​

convert to jm.Record from dict result

Parameters​

  • results(typ.Iterable[dict[int, int | float]]) : dict result
  • compiled_model(CompiledInstance) : compiled_model to get var_map and fixed_variables.

Returns​

  • jm.Record : converted record.

evaluate (samples, compiled_model) -> -​

transpile_to_mip (compiled_model, relax_list, solver_name, mip_model) -> MIPBuilder​

Transpile a compiled model into a MIP model.

Parameters​

  • compiled_model(CompiledInstance) : a compiled model
  • relax_list(list[str]) : a list of labels to be relaxed. Defaults to None.
  • solver_name(str) : the name of the solver. Defaults to "CBC".
  • mip_model(mip.Model) : a MIP model to be used. Defaults to None.

Returns​

  • MIPBuilder : a MIP model builder