ommx_python_mip_adapter.adapter

Classes

OMMXPythonMIPAdapter

An abstract interface for Adapters, defining how solvers should be used with OMMX.

Module Contents

class ommx_python_mip_adapter.adapter.OMMXPythonMIPAdapter(ommx_instance: ommx.v1.Instance, *, relax: bool = False, solver_name: str = mip.CBC, solver: mip.Solver | None = None, verbose: bool = False)

Bases: ommx.adapter.SolverAdapter

An abstract interface for Adapters, defining how solvers should be used with OMMX.

See the implementation guide for more details. .. _implementation guide: https://jij-inc.github.io/ommx/ommx_ecosystem/solver_adapter_guide.html

_as_lin_expr(f: ommx.v1.function_pb2.Function) → mip.LinExpr: Translate ommx.v1.Function to mip.LinExpr or float.

_set_constraints()

_set_decision_variables()

_set_objective()

decode(data: mip.Model) → ommx.v1.Solution

Convert optimized Python-MIP model and ommx.v1.Instance to ommx.v1.Solution.

This method is intended to be used if the model has been acquired with solver_input for futher adjustment of the solver parameters, and separately optimizing the model.

Note that alterations to the model may make the decoding process incompatible – decoding will only work if the model still describes effectively the same problem as the OMMX instance used to create the adapter.

When creating the solution, this method reflects the relax flag used in this adapter’s constructor. The solution’s relaxation metadata will be set _only_ if relax=True was passed to the constructor. There is no way for this adapter to get relaxation information from Python-MIP directly. If relaxing the model separately after obtaining it with solver_input, you must set solution.raw.relaxation yourself if you care about this value.

Examples

>>> from ommx.v1 import Instance, DecisionVariable
>>> from ommx_python_mip_adapter import OMMXPythonMIPAdapter

>>> p = [10, 13, 18, 32, 7, 15]
>>> w = [11, 15, 20, 35, 10, 33]
>>> x = [DecisionVariable.binary(i) for i in range(6)]
>>> instance = Instance.from_components(
...     decision_variables=x,
...     objective=sum(p[i] * x[i] for i in range(6)),
...     constraints=[sum(w[i] * x[i] for i in range(6)) <= 47],
...     sense=Instance.MAXIMIZE,
... )

>>> adapter = OMMXPythonMIPAdapter(instance)
>>> model = adapter.solver_input
>>> # ... some modification of model's parameters
>>> model.optimize()
<OptimizationStatus.OPTIMAL: 0>

>>> solution = adapter.decode(model)
>>> solution.raw.objective
42.0

decode_to_state(data: mip.Model) → ommx.v1.State

Create an ommx.v1.State from an optimized Python-MIP Model.

Examples

The following example of solving an unconstrained linear optimization problem with x1 as the objective function.

>>> from ommx_python_mip_adapter import OMMXPythonMIPAdapter
>>> from ommx.v1 import Instance, DecisionVariable

>>> x1 = DecisionVariable.integer(1, lower=0, upper=5)
>>> ommx_instance = Instance.from_components(
...     decision_variables=[x1],
...     objective=x1,
...     constraints=[],
...     sense=Instance.MINIMIZE,
... )
>>> adapter = OMMXPythonMIPAdapter(ommx_instance)
>>> model = adapter.solver_input
>>> model.optimize()
<OptimizationStatus.OPTIMAL: 0>

>>> ommx_state = adapter.decode_to_state(model)
>>> ommx_state.entries
{1: 0.0}

static solve(ommx_instance: ommx.v1.Instance, relax: bool = False, verbose: bool = False) → ommx.v1.Solution

Solve the given ommx.v1.Instance using Python-MIP, returning an ommx.v1.Solution.

Parameters:

ommx_instance – The ommx.v1.Instance to solve.
relax – If True, relax all integer variables to continuous variables by using the relax parameter in Python-MIP’s Model.optimize() <https://docs.python-mip.com/en/latest/classes.html#mip.Model.optimize>.
verbose – If True, enable Python-MIP’s verbose mode

Examples

KnapSack Problem

>>> from ommx.v1 import Instance, DecisionVariable
>>> from ommx.v1.solution_pb2 import Optimality
>>> from ommx_python_mip_adapter import OMMXPythonMIPAdapter

>>> p = [10, 13, 18, 32, 7, 15]
>>> w = [11, 15, 20, 35, 10, 33]
>>> x = [DecisionVariable.binary(i) for i in range(6)]
>>> instance = Instance.from_components(
...     decision_variables=x,
...     objective=sum(p[i] * x[i] for i in range(6)),
...     constraints=[sum(w[i] * x[i] for i in range(6)) <= 47],
...     sense=Instance.MAXIMIZE,
... )

Solve it

>>> solution = OMMXPythonMIPAdapter.solve(instance)

Check output

>>> sorted([(id, value) for id, value in solution.state.entries.items()])
[(0, 1.0), (1, 0.0), (2, 0.0), (3, 1.0), (4, 0.0), (5, 0.0)]
>>> solution.feasible
True
>>> assert solution.optimality == Optimality.OPTIMALITY_OPTIMAL

p[0] + p[3] = 42
w[0] + w[3] = 46 <= 47

>>> solution.objective
42.0
>>> solution.raw.evaluated_constraints[0].evaluated_value
-1.0

Infeasible Problem

>>> from ommx.v1 import Instance, DecisionVariable
>>> from ommx_python_mip_adapter import OMMXPythonMIPAdapter

>>> x = DecisionVariable.integer(0, upper=3, lower=0)
>>> instance = Instance.from_components(
...     decision_variables=[x],
...     objective=x,
...     constraints=[x >= 4],
...     sense=Instance.MAXIMIZE,
... )

>>> OMMXPythonMIPAdapter.solve(instance)
Traceback (most recent call last):
    ...
ommx.adapter.InfeasibleDetected: Model was infeasible

Unbounded Problem

>>> from ommx.v1 import Instance, DecisionVariable
>>> from ommx_python_mip_adapter import OMMXPythonMIPAdapter

>>> x = DecisionVariable.integer(0, lower=0)
>>> instance = Instance.from_components(
...     decision_variables=[x],
...     objective=x,
...     constraints=[],
...     sense=Instance.MAXIMIZE,
... )

>>> OMMXPythonMIPAdapter.solve(instance)
Traceback (most recent call last):
    ...
ommx.adapter.UnboundedDetected: Model was unbounded

Dual variable

>>> from ommx.v1 import Instance, DecisionVariable
>>> from ommx_python_mip_adapter import OMMXPythonMIPAdapter

>>> x = DecisionVariable.continuous(0, lower=0, upper=1)
>>> y = DecisionVariable.continuous(1, lower=0, upper=1)
>>> instance = Instance.from_components(
...     decision_variables=[x, y],
...     objective=x + y,
...     constraints=[x + y <= 1],
...     sense=Instance.MAXIMIZE,
... )

>>> solution = OMMXPythonMIPAdapter.solve(instance)
>>> solution.raw.evaluated_constraints[0].dual_variable
1.0

instance

model

property solver_input: mip.Model: The Python-MIP model generated from this OMMX instance