# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2021, 2023.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Translator between a docplex.mp model and a quadratic program"""
from math import isclose
from typing import Any, Dict, Optional, Tuple, cast
from warnings import warn
from docplex.mp.basic import Expr
from docplex.mp.constants import ComparisonType
from docplex.mp.constr import (
IndicatorConstraint,
LinearConstraint,
NotEqualConstraint,
QuadraticConstraint,
)
from docplex.mp.dvar import Var
from docplex.mp.linear import AbstractLinearExpr
from docplex.mp.model import Model
from docplex.mp.quad import QuadExpr
from docplex.mp.vartype import BinaryVarType, ContinuousVarType, IntegerVarType
from qiskit_optimization.exceptions import QiskitOptimizationError
from qiskit_optimization.problems.constraint import Constraint
from qiskit_optimization.problems.quadratic_objective import QuadraticObjective
from qiskit_optimization.problems.quadratic_program import QuadraticProgram
from qiskit_optimization.problems.variable import Variable
[documentos]def to_docplex_mp(quadratic_program: QuadraticProgram) -> Model:
"""Returns a docplex.mp model corresponding to a quadratic program.
Args:
quadratic_program: The quadratic program to be translated.
Returns:
The docplex.mp model corresponding to a quadratic program.
Raises:
QiskitOptimizationError: if the model contains non-supported elements (should never happen).
"""
# initialize model
mdl = Model(quadratic_program.name)
# add variables
var = {}
for idx, x in enumerate(quadratic_program.variables):
if x.vartype == Variable.Type.CONTINUOUS:
var[idx] = mdl.continuous_var(lb=x.lowerbound, ub=x.upperbound, name=x.name)
elif x.vartype == Variable.Type.BINARY:
var[idx] = mdl.binary_var(name=x.name)
elif x.vartype == Variable.Type.INTEGER:
var[idx] = mdl.integer_var(lb=x.lowerbound, ub=x.upperbound, name=x.name)
else:
# should never happen
raise QiskitOptimizationError(f"Internal error: unsupported variable type: {x.vartype}")
# add objective
objective = (
quadratic_program.objective.constant
+ mdl.sum(
v * var[cast(int, i)] for i, v in quadratic_program.objective.linear.to_dict().items()
)
+ mdl.sum(
v * var[cast(int, i)] * var[cast(int, j)]
for (i, j), v in quadratic_program.objective.quadratic.to_dict().items()
)
)
if quadratic_program.objective.sense == QuadraticObjective.Sense.MINIMIZE:
mdl.minimize(objective)
else:
mdl.maximize(objective)
# add linear constraints
for l_constraint in quadratic_program.linear_constraints:
name = l_constraint.name
rhs = l_constraint.rhs
if rhs == 0 and l_constraint.linear.coefficients.nnz == 0:
continue
linear_expr = mdl.sum(
v * var[cast(int, j)] for j, v in l_constraint.linear.to_dict().items()
)
sense = l_constraint.sense
if sense == Constraint.Sense.EQ:
mdl.add_constraint(linear_expr == rhs, ctname=name)
elif sense == Constraint.Sense.GE:
mdl.add_constraint(linear_expr >= rhs, ctname=name)
elif sense == Constraint.Sense.LE:
mdl.add_constraint(linear_expr <= rhs, ctname=name)
else:
# should never happen
raise QiskitOptimizationError(f"Internal error: unsupported constraint sense: {sense}")
# add quadratic constraints
for q_constraint in quadratic_program.quadratic_constraints:
name = q_constraint.name
rhs = q_constraint.rhs
if (
rhs == 0
and q_constraint.linear.coefficients.nnz == 0
and q_constraint.quadratic.coefficients.nnz == 0
):
continue
quadratic_expr = mdl.sum(
v * var[cast(int, j)] for j, v in q_constraint.linear.to_dict().items()
) + mdl.sum(
v * var[cast(int, j)] * var[cast(int, k)]
for (j, k), v in q_constraint.quadratic.to_dict().items()
)
sense = q_constraint.sense
if sense == Constraint.Sense.EQ:
mdl.add_constraint(quadratic_expr == rhs, ctname=name)
elif sense == Constraint.Sense.GE:
mdl.add_constraint(quadratic_expr >= rhs, ctname=name)
elif sense == Constraint.Sense.LE:
mdl.add_constraint(quadratic_expr <= rhs, ctname=name)
else:
# should never happen
raise QiskitOptimizationError(f"Internal error: unsupported constraint sense: {sense}")
return mdl
# from_docplex_mp
class _FromDocplexMp:
_sense_dict = {ComparisonType.EQ: "==", ComparisonType.LE: "<=", ComparisonType.GE: ">="}
def __init__(self, model: Model):
"""
Args:
model: Docplex model
"""
self._model: Model = model
self._quadratic_program: QuadraticProgram = QuadraticProgram()
self._var_names: Dict[Var, str] = {}
self._var_bounds: Dict[str, Tuple[float, float]] = {}
def _variables(self):
# keep track of names separately, since docplex allows to have None names.
for x in self._model.iter_variables():
if isinstance(x.vartype, ContinuousVarType):
x_new = self._quadratic_program.continuous_var(x.lb, x.ub, x.name)
elif isinstance(x.vartype, BinaryVarType):
x_new = self._quadratic_program.binary_var(x.name)
elif isinstance(x.vartype, IntegerVarType):
x_new = self._quadratic_program.integer_var(x.lb, x.ub, x.name)
else:
raise QiskitOptimizationError(f"Unsupported variable type: {x.name} {x.vartype}")
self._var_names[x] = x_new.name
self._var_bounds[x.name] = (x_new.lowerbound, x_new.upperbound)
def _linear_expr(self, expr: AbstractLinearExpr) -> Dict[str, float]:
# AbstractLinearExpr is a parent of LinearExpr, ConstantExpr, and ZeroExpr
linear = {}
for x, coeff in expr.iter_terms():
linear[self._var_names[x]] = coeff
return linear
def _quadratic_expr(
self, expr: QuadExpr
) -> Tuple[Dict[str, float], Dict[Tuple[str, str], float]]:
linear = self._linear_expr(expr.get_linear_part())
quad = {}
for x, y, coeff in expr.iter_quad_triplets():
i = self._var_names[x]
j = self._var_names[y]
quad[i, j] = coeff
return linear, quad
def quadratic_program(self, indicator_big_m: Optional[float]) -> QuadraticProgram:
"""Generate a quadratic program corresponding to the input Docplex model.
Args:
indicator_big_m: The big-M value used for the big-M formulation to convert
indicator constraints into linear constraints.
If ``None``, it is automatically derived from the model.
Returns:
a quadratic program corresponding to the input Docplex model.
"""
self._quadratic_program = QuadraticProgram(self._model.name)
# prepare variables
self._variables()
# objective sense
minimize = self._model.objective_sense.is_minimize()
# make sure objective expression is linear or quadratic and not a variable
if isinstance(self._model.objective_expr, Var):
self._model.objective_expr = self._model.objective_expr + 0 # Var + 0 -> LinearExpr
constant = self._model.objective_expr.constant
if isinstance(self._model.objective_expr, QuadExpr):
linear, quadratic = self._quadratic_expr(self._model.objective_expr)
else:
linear = self._linear_expr(self._model.objective_expr.get_linear_part())
quadratic = {}
# set objective
if minimize:
self._quadratic_program.minimize(constant, linear, quadratic)
else:
self._quadratic_program.maximize(constant, linear, quadratic)
# set linear constraints
for constraint in self._model.iter_linear_constraints():
linear, sense, rhs = self._linear_constraint(constraint)
if not linear: # lhs == 0
warn(f"Trivial constraint: {constraint}", stacklevel=3)
self._quadratic_program.linear_constraint(linear, sense, rhs, constraint.name)
# set quadratic constraints
for constraint in self._model.iter_quadratic_constraints():
linear, quadratic, sense, rhs = self._quadratic_constraint(constraint)
if not linear and not quadratic: # lhs == 0
warn(f"Trivial constraint: {constraint}", stacklevel=3)
self._quadratic_program.quadratic_constraint(
linear, quadratic, sense, rhs, constraint.name
)
# set indicator constraints
for index, constraint in enumerate(self._model.iter_indicator_constraints()):
linear, _, _ = self._linear_constraint(constraint.linear_constraint)
if not linear: # lhs == 0
warn(f"Trivial constraint: {constraint}", stacklevel=3)
prefix = constraint.name or f"ind{index}"
linear_constraints = self._indicator_constraints(constraint, prefix, indicator_big_m)
for linear, sense, rhs, name in linear_constraints:
self._quadratic_program.linear_constraint(linear, sense, rhs, name)
return self._quadratic_program
@staticmethod
def _subtract(dict1: Dict[Any, float], dict2: Dict[Any, float]) -> Dict[Any, float]:
"""Calculate dict1 - dict2"""
ret = dict1.copy()
for key, val2 in dict2.items():
if key in dict1:
val1 = ret[key]
if isclose(val1, val2):
del ret[key]
else:
ret[key] -= val2
else:
ret[key] = -val2
return ret
def _linear_constraint(
self, constraint: LinearConstraint
) -> Tuple[Dict[str, float], str, float]:
left_expr = constraint.get_left_expr()
right_expr = constraint.get_right_expr()
# for linear constraints we may get an instance of Var instead of expression,
# e.g. x + y = z
if not isinstance(left_expr, (Expr, Var)):
raise QiskitOptimizationError(f"Unsupported expression: {left_expr} {type(left_expr)}")
if not isinstance(right_expr, (Expr, Var)):
raise QiskitOptimizationError(
f"Unsupported expression: {right_expr} {type(right_expr)}"
)
if constraint.sense not in self._sense_dict:
raise QiskitOptimizationError(f"Unsupported constraint sense: {constraint}")
if isinstance(left_expr, Var):
left_expr = left_expr + 0 # Var + 0 -> LinearExpr
left_linear = self._linear_expr(left_expr)
if isinstance(right_expr, Var):
right_expr = right_expr + 0
right_linear = self._linear_expr(right_expr)
linear = self._subtract(left_linear, right_linear)
rhs = right_expr.constant - left_expr.constant
return linear, self._sense_dict[constraint.sense], rhs
def _quadratic_constraint(
self, constraint: QuadraticConstraint
) -> Tuple[Dict[str, float], Dict[Tuple[str, str], float], str, float]:
left_expr = constraint.get_left_expr()
right_expr = constraint.get_right_expr()
if not isinstance(left_expr, (Expr, Var)):
raise QiskitOptimizationError(f"Unsupported expression: {left_expr} {type(left_expr)}")
if not isinstance(right_expr, (Expr, Var)):
raise QiskitOptimizationError(
f"Unsupported expression: {right_expr} {type(right_expr)}"
)
if constraint.sense not in self._sense_dict:
raise QiskitOptimizationError(f"Unsupported constraint sense: {constraint}")
if isinstance(left_expr, Var):
left_expr = left_expr + 0 # Var + 0 -> LinearExpr
if left_expr.is_quad_expr():
left_lin, left_quad = self._quadratic_expr(left_expr)
else:
left_lin = self._linear_expr(left_expr)
left_quad = {}
if isinstance(right_expr, Var):
right_expr = right_expr + 0
if right_expr.is_quad_expr():
right_lin, right_quad = self._quadratic_expr(right_expr)
else:
right_lin = self._linear_expr(right_expr)
right_quad = {}
linear = self._subtract(left_lin, right_lin)
quadratic = self._subtract(left_quad, right_quad)
rhs = right_expr.constant - left_expr.constant
return linear, quadratic, self._sense_dict[constraint.sense], rhs
def _linear_bounds(self, linear: Dict[str, float]):
linear_lb = 0.0
linear_ub = 0.0
for var_name, val in linear.items():
x_lb, x_ub = self._var_bounds[var_name]
x_lb *= val
x_ub *= val
linear_lb += min(x_lb, x_ub)
linear_ub += max(x_lb, x_ub)
return linear_lb, linear_ub
def _indicator_constraints(
self,
constraint: IndicatorConstraint,
name: str,
indicator_big_m: Optional[float] = None,
):
binary_var = constraint.binary_var
active_value = constraint.active_value
linear_constraint = constraint.linear_constraint
linear, sense, rhs = self._linear_constraint(linear_constraint)
linear_lb, linear_ub = self._linear_bounds(linear)
ret = []
if sense in ["<=", "=="]:
big_m = max(0.0, linear_ub - rhs) if indicator_big_m is None else indicator_big_m
if active_value:
# rhs += big_m * (1 - binary_var)
linear2 = self._subtract(linear, {binary_var.name: -big_m})
rhs2 = rhs + big_m
else:
# rhs += big_m * binary_var
linear2 = self._subtract(linear, {binary_var.name: big_m})
rhs2 = rhs
name2 = name + "_LE" if sense == "==" else name
ret.append((linear2, "<=", rhs2, name2))
if sense in [">=", "=="]:
big_m = max(0.0, rhs - linear_lb) if indicator_big_m is None else indicator_big_m
if active_value:
# rhs += -big_m * (1 - binary_var)
linear2 = self._subtract(linear, {binary_var.name: big_m})
rhs2 = rhs - big_m
else:
# rhs += -big_m * binary_var
linear2 = self._subtract(linear, {binary_var.name: -big_m})
rhs2 = rhs
name2 = name + "_GE" if sense == "==" else name
ret.append((linear2, ">=", rhs2, name2))
if sense not in ["<=", ">=", "=="]:
raise QiskitOptimizationError(
f"Internal error: invalid sense of indicator constraint: {sense}"
)
return ret
[documentos]def from_docplex_mp(model: Model, indicator_big_m: Optional[float] = None) -> QuadraticProgram:
"""Translate a docplex.mp model into a quadratic program.
Note that this supports the following features of docplex:
- linear / quadratic objective function
- linear / quadratic / indicator constraints
- binary / integer / continuous variables
- logical expressions (``logical_not``, ``logical_and``, and ``logical_or``)
Args:
model: The docplex.mp model to be loaded.
indicator_big_m: The big-M value used for the big-M formulation to convert
indicator constraints into linear constraints.
If ``None``, it is automatically derived from the model.
Returns:
The quadratic program corresponding to the model.
Raises:
QiskitOptimizationError: if the model contains unsupported elements.
"""
if not isinstance(model, Model):
raise QiskitOptimizationError(f"The model is not compatible: {model}")
if model.number_of_user_cut_constraints > 0:
raise QiskitOptimizationError("User cut constraints are not supported")
if model.number_of_lazy_constraints > 0:
raise QiskitOptimizationError("Lazy constraints are not supported")
if model.number_of_sos > 0:
raise QiskitOptimizationError("SOS sets are not supported")
# check constraint type
for constraint in model.iter_constraints():
# If any constraint is not linear/quadratic/indicator constraints, it raises an error.
if isinstance(constraint, LinearConstraint):
if isinstance(constraint, NotEqualConstraint):
# Notice that NotEqualConstraint is a subclass of Docplex's LinearConstraint,
# but it cannot be handled by optimization.
raise QiskitOptimizationError(f"Unsupported constraint: {constraint}")
elif not isinstance(constraint, (QuadraticConstraint, IndicatorConstraint)):
raise QiskitOptimizationError(f"Unsupported constraint: {constraint}")
return _FromDocplexMp(model).quadratic_program(indicator_big_m)