为什么这种限制提出一个DCP错误？

我有定义的一个问题，这将最大限度地降低成本的运行一个水泵。 这被定义为目的的问题。

cost_cp = cp.sum(cp.multiply(cost_,selection))
objective = cp.Minimize(cost_cp)

该问题定义为：

problem = cp.Problem(objective, constraints)

我跑的计算使用的 cp.multiply 和 cp.vec 为计算差值在储卷，它提供了我的答案，我希望与正确的差异。

flow_in = cp.vec(cp.multiply(input_flow_, flow_in_minutes))
flow_out = cp.vec(flow_out_)
flow_diff = flow_in - flow_out

问题出现时我的计算累积求和使用 cp.cumsum. 它的工作和计算正确，但是当我想添加的限制围绕这是我提供的 DCPError我不确定我要去的错误在这样计算，因为它曾在以前没有问题，对我来说。

约束我希望定义是：

volume_constraint = volume_cp >= 300000
min_level_constraint = res_level >= min_level
max_level_constraint = res_level <= max_level

constraints = [assignment_constraint, volume_constraint, min_level_constraint, max_level_constraint]

的 volume_constraint 完美的作品。 问题是有的 min_level_constraint 和 max_level_constraint.

我尝试解决方案的使用

problem.solve(solver=cp.CPLEX, verbose=False)  

一种回溯在我提供的是：

DCPError                                  Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_14560/1602474026.py in <module>
     33 
     34 # Problem solve
---> 35 problem.solve(solver=cp.CPLEX, verbose=False)

~\AppData\Local\Programs\Python\Python38\lib\site-packages\cvxpy\problems\problem.py in solve(self, *args, **kwargs)
    457         else:
    458             solve_func = Problem._solve
--> 459         return solve_func(self, *args, **kwargs)
    460 
    461     @classmethod

~\AppData\Local\Programs\Python\Python38\lib\site-packages\cvxpy\problems\problem.py in _solve(self, solver, warm_start, verbose, gp, qcp, requires_grad, enforce_dpp, **kwargs)
    936                 return self.value
    937 
--> 938         data, solving_chain, inverse_data = self.get_problem_data(
    939             solver, gp, enforce_dpp, verbose)
    940 

~\AppData\Local\Programs\Python\Python38\lib\site-packages\cvxpy\problems\problem.py in get_problem_data(self, solver, gp, enforce_dpp, verbose)
    563         if key != self._cache.key:
    564             self._cache.invalidate()
--> 565             solving_chain = self._construct_chain(
    566                 solver=solver, gp=gp, enforce_dpp=enforce_dpp)
    567             self._cache.key = key

~\AppData\Local\Programs\Python\Python38\lib\site-packages\cvxpy\problems\problem.py in _construct_chain(self, solver, gp, enforce_dpp)
    789         candidate_solvers = self._find_candidate_solvers(solver=solver, gp=gp)
    790         self._sort_candidate_solvers(candidate_solvers)
--> 791         return construct_solving_chain(self, candidate_solvers, gp=gp,
    792                                        enforce_dpp=enforce_dpp)
    793 

~\AppData\Local\Programs\Python\Python38\lib\site-packages\cvxpy\reductions\solvers\solving_chain.py in construct_solving_chain(problem, candidates, gp, enforce_dpp)
    153     if len(problem.variables()) == 0:
    154         return SolvingChain(reductions=[ConstantSolver()])
--> 155     reductions = _reductions_for_problem_class(problem, candidates, gp)
    156 
    157     dpp_context = 'dcp' if not gp else 'dgp'

~\AppData\Local\Programs\Python\Python38\lib\site-packages\cvxpy\reductions\solvers\solving_chain.py in _reductions_for_problem_class(problem, candidates, gp)
     89             append += ("\nHowever, the problem does follow DQCP rules. "
     90                        "Consider calling solve() with `qcp=True`.")
---> 91         raise DCPError(
     92             "Problem does not follow DCP rules. Specifically:\n" + append)
     93     elif gp and not problem.is_dgp():

我已经看了看四周的文件CVXPY和堆溢出，但我什么也没有发现在这工作我的问题。 我感到困惑，因为它已经为我的过去。