"""Implements a fast dual formulation."""
from sympy.core.singleton import S
from micom.logger import logger
[docs]def fast_dual(model, prefix="dual_"):
"""Add dual formulation to the problem.
A mathematical optimization problem can be viewed as a primal and a dual
problem. If the primal problem is a minimization problem the dual is a
maximization problem, and the optimal value of the dual is a lower bound of
the optimal value of the primal. For linear problems, strong duality holds,
which means that the optimal values of the primal and dual are equal
(duality gap = 0). This functions takes an optlang Model representing a
primal linear problem
and returns a new Model representing the dual optimization problem. The
provided model must have a linear objective, linear constraints and only
continuous variables. Furthermore, the problem must be in standard form,
i.e. all variables should be non-negative. Both minimization and
maximization problems are allowed.
Attributes
----------
model : cobra.Model
The model to be dualized.
prefix : str
The string that will be prepended to all variable and constraint names
in the returned dual problem.
Returns
-------
dict
The coefficients for the new dual objective.
"""
logger.info("adding dual variables")
if len(model.variables) > 1e5:
logger.warning(
"the model has a lot of variables,"
"dual optimization will be extremely slow :O"
)
prob = model.problem
maximization = model.objective.direction == "max"
if maximization:
sign = 1
else:
sign = -1
coefficients = {}
dual_objective = {}
to_add = []
# Add dual variables from primal constraints:
for constraint in model.constraints:
if constraint.expression == 0:
continue # Skip empty constraint
if not constraint.is_Linear:
raise ValueError(
"Non-linear problems are not supported: " + str(constraint)
)
if constraint.lb is None and constraint.ub is None:
logger.warning("skipped free constraint %s" % constraint.name)
continue # Skip free constraint
if constraint.lb == constraint.ub:
const_var = prob.Variable(
prefix + constraint.name + "_constraint", lb=None, ub=None
)
to_add.append(const_var)
if constraint.lb != 0:
dual_objective[const_var.name] = sign * constraint.lb
coefs = constraint.get_linear_coefficients(constraint.variables)
for variable, coef in coefs.items():
coefficients.setdefault(variable.name, {})[const_var.name] = (
sign * coef
)
else:
if constraint.lb is not None:
lb_var = prob.Variable(
prefix + constraint.name + "_constraint_lb", lb=0, ub=None
)
to_add.append(lb_var)
if constraint.lb != 0:
dual_objective[lb_var.name] = -sign * constraint.lb
if constraint.ub is not None:
ub_var = prob.Variable(
prefix + constraint.name + "_constraint_ub", lb=0, ub=None
)
to_add.append(ub_var)
if constraint.ub != 0:
dual_objective[ub_var.name] = sign * constraint.ub
if not (
constraint.expression.is_Add or constraint.expression.is_Mul
):
raise ValueError(
"Invalid expression type: "
+ str(type(constraint.expression))
)
if constraint.expression.is_Add:
coefficients_dict = constraint.get_linear_coefficients(
constraint.variables
)
else: # constraint.expression.is_Mul:
args = constraint.expression.args
coefficients_dict = {args[1]: args[0]}
for variable, coef in coefficients_dict.items():
if constraint.lb is not None:
coefficients.setdefault(variable.name, {})[lb_var.name] = (
-sign * coef
)
if constraint.ub is not None:
coefficients.setdefault(variable.name, {})[ub_var.name] = (
sign * coef
)
# Add dual variables from primal bounds
for variable in model.variables:
if not variable.type == "continuous":
raise ValueError(
"Integer variables are not supported: " + str(variable)
)
if variable.lb is not None and variable.lb < 0:
raise ValueError(
"Problem is not in standard form ("
+ variable.name
+ " can be negative)"
)
if variable.lb > 0:
bound_var = prob.Variable(
prefix + variable.name + "_lb", lb=0, ub=None
)
to_add.append(bound_var)
coefficients.setdefault(variable.name, {})[bound_var.name] = -sign
dual_objective[bound_var.name] = -sign * variable.lb
if variable.ub is not None:
bound_var = prob.Variable(
prefix + variable.name + "_ub", lb=0, ub=None
)
to_add.append(bound_var)
coefficients.setdefault(variable.name, {})[bound_var.name] = sign
if variable.ub != 0:
dual_objective[bound_var.name] = sign * variable.ub
model.add_cons_vars(to_add)
# Add dual constraints from primal objective
primal_objective_dict = model.objective.get_linear_coefficients(
model.objective.variables
)
for variable in model.objective.variables:
obj_coef = primal_objective_dict[variable]
if maximization:
const = prob.Constraint(
S.Zero, lb=obj_coef, name=prefix + variable.name
)
else:
const = prob.Constraint(
S.Zero, ub=obj_coef, name=prefix + variable.name
)
model.add_cons_vars([const])
model.solver.update()
coefs = {
model.variables[vid]: coef
for vid, coef in coefficients[variable.name].items()
}
const.set_linear_coefficients(coefs)
# Make dual objective
coefs = {
model.variables[vid]: coef
for vid, coef in dual_objective.items()
if coef != 0
}
logger.info("dual model has {} terms in objective".format(len(coefs)))
return coefs