有没有一种方法可以在求解器运行时更改约束的值？

基本上，我有一个依赖于变量值的约束。问题是约束是基于变量的初始值计算的，但是不会随着变量的变化而更新。

这里有一个简单的例子：

from pyomo.environ import *
from pyomo.opt import SolverFactory
import numpy as np

# Setup
model = ConcreteModel()
model.A = Set(initialize = [0,1,2])
model.B = Set(initialize = [0,1,2])
model.x = Var(model.A, model.B, initialize=0)

# A constraint that I'd like to keep updating, based on the value of x
def changing_constraint_rule(model, a):
    x_values = list((model.x[a, b].value for b in model.B))
    if np.max(x_values) == 0:
        return Constraint.Skip
    else:
        # Not really important what goes here, just as long as it updates the constraint list
        if a == 1 : return sum(model.x[a,b] for b in model.B) == 0
        else:       return sum(model.x[a,b] for b in model.B) == 1
model.changing_constraint = Constraint(model.A, rule = changing_constraint_rule)

# Another constraint that changes the value of x
def bounding_constraint_rule(model, a):
    return sum(model.x[a, b] for b in model.B) == 1
model.bounding_constraint = Constraint(
    model.A,
    rule = bounding_constraint_rule)

# Some objective function
def obj_rule(model):
    return(sum(model.x[a,b] for a in model.A for b in model.B))
model.objective = Objective(rule=obj_rule)

# Results
opt = SolverFactory("glpk")
results = opt.solve(model)
results.write()
model.x.display()

如果我运行模型。更改_约束。pprint（）我可以看到，由于变量模型的初始值不正确，因此没有进行任何约束。x已设置为0。

如果在求解时无法更改约束值，我如何以不同的方式表述此问题以实现我所寻找的目标？我读过另一篇文章，但从说明书上看不出来。