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 only basic functions of docplex as follows: - quadratic objective function - linear / quadratic / indicator constraints - binary / integer / continuous variables 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") # get name quadratic_program = QuadraticProgram(model.name) # get variables # keep track of names separately, since docplex allows to have None names. var_names = {} var_bounds = {} for x in model.iter_variables(): if isinstance(x.vartype, ContinuousVarType): x_new = quadratic_program.continuous_var(x.lb, x.ub, x.name) elif isinstance(x.vartype, BinaryVarType): x_new = quadratic_program.binary_var(x.name) elif isinstance(x.vartype, IntegerVarType): x_new = quadratic_program.integer_var(x.lb, x.ub, x.name) else: raise QiskitOptimizationError( f"Unsupported variable type: {x.name} {x.vartype}") var_names[x] = x_new.name var_bounds[x.name] = (x_new.lowerbound, x_new.upperbound) # objective sense minimize = model.objective_sense.is_minimize() # make sure objective expression is linear or quadratic and not a variable if isinstance(model.objective_expr, Var): model.objective_expr = model.objective_expr + 0 # get objective offset constant = model.objective_expr.constant # get linear part of objective linear = {} linear_part = model.objective_expr.get_linear_part() for x in linear_part.iter_variables(): linear[var_names[x]] = linear_part.get_coef(x) # get quadratic part of objective quadratic = {} if isinstance(model.objective_expr, QuadExpr): for quad_triplet in model.objective_expr.iter_quad_triplets(): i = var_names[quad_triplet[0]] j = var_names[quad_triplet[1]] v = quad_triplet[2] quadratic[i, j] = v # set objective if minimize: quadratic_program.minimize(constant, linear, quadratic) else: quadratic_program.maximize(constant, linear, quadratic) # 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}") # get linear constraints for constraint in model.iter_linear_constraints(): lhs, sense, rhs = _FromDocplexMp._linear_constraint( var_names, constraint) quadratic_program.linear_constraint(lhs, sense, rhs, constraint.name) # get quadratic constraints for constraint in model.iter_quadratic_constraints(): linear, quadratic, sense, rhs = _FromDocplexMp._quadratic_constraint( var_names, constraint) quadratic_program.quadratic_constraint(linear, quadratic, sense, rhs, constraint.name) # get indicator constraints for constraint in model.iter_indicator_constraints(): linear_constraints = _FromDocplexMp._indicator_constraints( var_names, var_bounds, constraint, indicator_big_m) for linear, sense, rhs, name in linear_constraints: quadratic_program.linear_constraint(linear, sense, rhs, name) return quadratic_program
def from_gurobipy(model: Model) -> QuadraticProgram: """Translate a gurobipy model into a quadratic program. Note that this supports only basic functions of gurobipy as follows: - quadratic objective function - linear / quadratic constraints - binary / integer / continuous variables Args: model: The gurobipy model to be loaded. Returns: The quadratic program corresponding to the model. Raises: QiskitOptimizationError: if the model contains unsupported elements. MissingOptionalLibraryError: if gurobipy is not installed. """ _check_gurobipy_is_installed("from_gurobipy") if not isinstance(model, Model): raise QiskitOptimizationError(f"The model is not compatible: {model}") quadratic_program = QuadraticProgram() # Update the model to make sure everything works as expected model.update() # get name quadratic_program.name = model.ModelName # get variables # keep track of names separately, since gurobipy allows to have None names. var_names = {} for x in model.getVars(): if x.vtype == gp.GRB.CONTINUOUS: x_new = quadratic_program.continuous_var(x.lb, x.ub, x.VarName) elif x.vtype == gp.GRB.BINARY: x_new = quadratic_program.binary_var(x.VarName) elif x.vtype == gp.GRB.INTEGER: x_new = quadratic_program.integer_var(x.lb, x.ub, x.VarName) else: raise QiskitOptimizationError( f"Unsupported variable type: {x.VarName} {x.vtype}") var_names[x] = x_new.name # objective sense minimize = model.ModelSense == gp.GRB.MINIMIZE # Retrieve the objective objective = model.getObjective() has_quadratic_objective = False # Retrieve the linear part in case it is a quadratic objective if isinstance(objective, gp.QuadExpr): linear_part = objective.getLinExpr() has_quadratic_objective = True else: linear_part = objective # Get the constant constant = linear_part.getConstant() # get linear part of objective linear = {} for i in range(linear_part.size()): linear[var_names[linear_part.getVar(i)]] = linear_part.getCoeff(i) # get quadratic part of objective quadratic = {} if has_quadratic_objective: for i in range(objective.size()): x = var_names[objective.getVar1(i)] y = var_names[objective.getVar2(i)] v = objective.getCoeff(i) quadratic[x, y] = v # set objective if minimize: quadratic_program.minimize(constant, linear, quadratic) else: quadratic_program.maximize(constant, linear, quadratic) # check whether there are any general constraints if model.NumSOS > 0 or model.NumGenConstrs > 0: raise QiskitOptimizationError( "Unsupported constraint: SOS or General Constraint") # get linear constraints for constraint in model.getConstrs(): name = constraint.ConstrName sense = constraint.Sense left_expr = model.getRow(constraint) rhs = constraint.RHS lhs = {} for i in range(left_expr.size()): lhs[var_names[left_expr.getVar(i)]] = left_expr.getCoeff(i) if sense == gp.GRB.EQUAL: quadratic_program.linear_constraint(lhs, "==", rhs, name) elif sense == gp.GRB.GREATER_EQUAL: quadratic_program.linear_constraint(lhs, ">=", rhs, name) elif sense == gp.GRB.LESS_EQUAL: quadratic_program.linear_constraint(lhs, "<=", rhs, name) else: raise QiskitOptimizationError( f"Unsupported constraint sense: {constraint}") # get quadratic constraints for constraint in model.getQConstrs(): name = constraint.QCName sense = constraint.QCSense left_expr = model.getQCRow(constraint) rhs = constraint.QCRHS linear = {} quadratic = {} linear_part = left_expr.getLinExpr() for i in range(linear_part.size()): linear[var_names[linear_part.getVar(i)]] = linear_part.getCoeff(i) for i in range(left_expr.size()): x = var_names[left_expr.getVar1(i)] y = var_names[left_expr.getVar2(i)] v = left_expr.getCoeff(i) quadratic[x, y] = v if sense == gp.GRB.EQUAL: quadratic_program.quadratic_constraint(linear, quadratic, "==", rhs, name) elif sense == gp.GRB.GREATER_EQUAL: quadratic_program.quadratic_constraint(linear, quadratic, ">=", rhs, name) elif sense == gp.GRB.LESS_EQUAL: quadratic_program.quadratic_constraint(linear, quadratic, "<=", rhs, name) else: raise QiskitOptimizationError( f"Unsupported constraint sense: {constraint}") return quadratic_program
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