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Solution Module

This section documents the solution components of the Nextmv Gurobi Python SDK.

solution

Defines gurobipy solution interoperability.

This module provides functionality for interacting with Gurobi solutions.

FUNCTION DESCRIPTION
ModelSolution

Creates a basic solution dictionary from a Gurobi model.

ModelSolution 

ModelSolution(model: Model) -> Optional[dict[str, Any]]

Creates a basic solution dictionary from a Gurobi model.

You can import the ModelSolution function directly from nextmv_gurobipy:

from nextmv_gurobipy import ModelSolution

The simple solution dictionary contains the variable name and the value of the variable for each variable in the model. If the model has not been solved, it will return None. Although this method is a good starting point to visualize the solution of a Gurobi model, we recommend that you implement your own logic to extract the information you need.

PARAMETER DESCRIPTION

model

The Gurobi model that has been solved.

TYPE: Model

RETURNS DESCRIPTION
dict[str, Any] or None

A dictionary with variable names as keys and their optimal values as values. Returns None if the model has not been solved.

Examples:

>>> import gurobipy as gp
>>> from nextmv_gurobipy import ModelSolution
>>>
>>> # Create and solve a simple model
>>> model = gp.Model("example")
>>> x = model.addVar(name="x")
>>> y = model.addVar(name="y")
>>> model.addConstr(x + y <= 1)
>>> model.setObjective(x + y, gp.GRB.MAXIMIZE)
>>> model.optimize()
>>>
>>> # Get the solution dictionary
>>> solution = ModelSolution(model)
>>> print(solution)
{'x': 0.5, 'y': 0.5}
Source code in nextmv-gurobipy/nextmv_gurobipy/solution.py 
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def ModelSolution(model: gp.Model) -> Optional[dict[str, Any]]:
    """
    Creates a basic solution dictionary from a Gurobi model.

    You can import the `ModelSolution` function directly from `nextmv_gurobipy`:

    ```python
    from nextmv_gurobipy import ModelSolution
    ```

    The simple solution dictionary contains the variable name and the value of the
    variable for each variable in the model. If the model has not been solved,
    it will return `None`. Although this method is a good starting point to
    visualize the solution of a Gurobi model, we recommend that you implement
    your own logic to extract the information you need.

    Parameters
    ----------
    model : gp.Model
        The Gurobi model that has been solved.

    Returns
    -------
    dict[str, Any] or None
        A dictionary with variable names as keys and their optimal values as values.
        Returns None if the model has not been solved.

    Examples
    --------
    >>> import gurobipy as gp
    >>> from nextmv_gurobipy import ModelSolution
    >>>
    >>> # Create and solve a simple model
    >>> model = gp.Model("example")
    >>> x = model.addVar(name="x")
    >>> y = model.addVar(name="y")
    >>> model.addConstr(x + y <= 1)
    >>> model.setObjective(x + y, gp.GRB.MAXIMIZE)
    >>> model.optimize()
    >>>
    >>> # Get the solution dictionary
    >>> solution = ModelSolution(model)
    >>> print(solution)
    {'x': 0.5, 'y': 0.5}
    """

    if model.SolCount < 1:
        return None

    return {x.VarName: x.X for x in model.getVars()}