ommx.v1.Instance

ommx.v1.Instance#

ommx.v1.Instance is a data structure for describing the optimization problem itself (mathematical model). It consists of the following components:

For example, let’s consider a simple optimization problem:

\[ \begin{aligned} \max \quad & x + y \\ \text{subject to} \quad & x y = 0 \\ & x, y \in \{0, 1\} \end{aligned} \]

The corresponding ommx.v1.Instance is as follows.

from ommx.v1 import Instance, DecisionVariable

x = DecisionVariable.binary(1, name='x')
y = DecisionVariable.binary(2, name='y')

instance = Instance.from_components(
    decision_variables=[x, y],
    objective=x + y,
    constraints={0: x * y == 0},
    sense=Instance.MAXIMIZE
)

Each of these components has a corresponding property. The objective function is converted into the form of ommx.v1.Function, as explained in the previous section.

instance.objective

sense is set to Instance.MAXIMIZE for maximization problems or Instance.MINIMIZE for minimization problems.

instance.sense == Instance.MAXIMIZE

Decision Variables#

Decision variables and constraints can be obtained in the form of pandas.DataFrame.

instance.decision_variables_df

First, kind, lower, and upper are essential information for the mathematical model.

  • kind specifies the type of decision variable, which can be Binary, Integer, Continuous, SemiInteger, or SemiContinuous.

  • lower and upper are the lower and upper bounds of the decision variable. For Binary variables, this range is \([0, 1]\).

Additionally, OMMX is designed to handle metadata that may be needed when integrating mathematical optimization into practical data analysis. While this metadata does not affect the mathematical model itself, it is useful for data analysis and visualization.

  • name is a human-readable name for the decision variable. In OMMX, decision variables are always identified by ID, so this name may be duplicated. It is intended to be used in combination with subscripts, which is described later.

  • description is a more detailed explanation of the decision variable.

  • When dealing with many mathematical optimization problems, decision variables are often handled as multidimensional arrays. For example, it is common to consider constraints with subscripts like \(x_i + y_i \leq 1, \forall i \in [1, N]\). In this case, x and y are the names of the decision variables, so they are stored in name, and the part corresponding to \(i\) is stored in subscripts. subscripts is a list of integers, but if the subscript cannot be represented as an integer, there is a parameters property that allows storage in the form of dict[str, str].

If you need a list of ommx.v1.DecisionVariable directly, you can use the decision_variables property.

for v in instance.decision_variables:
    print(f"{v.id=}, {v.name=}")

To obtain ommx.v1.DecisionVariable from the ID of the decision variable, you can use the get_decision_variable_by_id method.

x1 = instance.get_decision_variable_by_id(1)
print(f"{x1.id=}, {x1.name=}")

Constraints#

Next, let’s look at the constraints.

instance.constraints_df

In OMMX, constraints are also managed by ID, and this ID is independent of the decision variable ID. The ID is assigned when a constraint is attached to an Instance: the key you use in the constraints dictionary passed to Instance.from_components becomes the constraint ID.

The essential information for constraints is equality. equality indicates whether the constraint is an equality constraint (Constraint.EQUAL_TO_ZERO) or an inequality constraint (Constraint.LESS_THAN_OR_EQUAL_TO_ZERO). Note that constraints of the type \(f(x) \geq 0\) are treated as \(-f(x) \leq 0\).

Constraints can also store metadata similar to decision variables. You can use name, description, subscripts, and parameters. These can be set using the add_name, add_description, add_subscripts, and add_parameters methods.

c = (x * y == 0).add_name("prod-zero")
print(f"{c.name=}")

You can also use the constraints property to directly obtain a dict[int, ommx.v1.Constraint] keyed by constraint ID. To obtain an ommx.v1.Constraint by its ID, use the get_constraint_by_id method.

for cid, c in instance.constraints.items():
    print(f"id={cid}: {c}")
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