修改约束体而不计算其值

Question

我使用pyomo5.3在python3.6中编程

我希望修改非索引约束的主体（如果它不是我需要的标准格式）。问题是，从实体中减去时，会计算特定点处约束的值。然而，我需要一个函数形式的体，因为我必须构造一个目标，它是导致最小-最大问题的所有非线性约束的最大值。你知道吗

我试图直接设置传递给函数的约束体，但得到的结果是无法设置属性。是否有一个函数来设置约束体？你知道吗

编辑：以下是我找到的解决方案：

约束。\u body=。。。你知道吗

我想用这个来改变一个优化问题的形式。你知道吗

（很抱歉非英语评论）

以下是本示例中使用的函数：

1. 第一种方法将所有变量的域放宽到实数。你知道吗
2. 第二种是用题词法建立一个新的模型。（这里我使用._body来修改约束。你知道吗

def cont_relax_model_same_bounds(model_vars):
    for var in model_vars:
        if str(var.domain) in int_type:
            var.domain = Reals

def epgraph_reformulation_without_bounds(model):

    #Erstelle Epigraph-Modell
    epi_model = model.clone()
    epi_model.alpha_epi = Var(within = Reals)

    #Speichere alle nichtlinearen Restriktionen des usprünglichen Modells in einer Liste
    nonlinear_constrs = []
    for constr in model.component_objects(Constraint):
        if not (constr.body.polynomial_degree() in [0, 1]):
            nonlinear_constrs.append(constr)

    #Speichere alle nichtlinearen Restriktionen des umformulierten Modells in einer Liste
    epi_nonlinear_constrs = []
    for constr in epi_model.component_objects(Constraint):
        if not (constr.body.polynomial_degree() in [0, 1]):
            epi_nonlinear_constrs.append(constr)

    #Kontrollausgabe, ob die Restriktionen richtig in der Liste gespeichert werden        
    for k, constr in enumerate(epi_nonlinear_constrs):
        print(epi_nonlinear_constrs[k].body)

    #Formuliere die nichtlinearen Restriktionen neu
    for k, constr in enumerate(nonlinear_constrs):
        epi_nonlinear_constrs[k]._body = (nonlinear_constrs[k].body - epi_model.alpha_epi)

    epi_model.obj = Objective(expr = epi_model.alpha_epi, sense = minimize)
    return epi_model

以下是原始模型：

model_ESH = ConcreteModel(name = "Example 1")

model_ESH.x1 = Var(bounds=(1,20), domain=Reals)
model_ESH.x2 = Var(bounds=(1,20), domain=Integers)

model_ESH.obj = Objective(expr=(-1)*model_ESH.x1-model_ESH.x2)

model_ESH.g1 = Constraint(expr=0.15*((model_ESH.x1 - 8)**2)+0.1*((model_ESH.x2 - 6)**2)+0.025*exp(model_ESH.x1)*((model_ESH.x2)**(-2))-5<=0)
model_ESH.g2 = Constraint(expr=(model_ESH.x1)**(-1) + (model_ESH.x2)**(-1) - ((model_ESH.x1)**(0.5)) * ((model_ESH.x2) ** (0.5))+4<=0)
model_ESH.l1 = Constraint(expr=2 * (model_ESH.x1) - 3 * (model_ESH.x2) -2<=0)
model_ESH.pprint()

然后我克隆模型并放松整数变量

NLP_model = model_ESH.clone()

#Relaxiere das Problem und deaktiviere die nichtlinearen Restriktionen
#Das funktioniert schonmal

cont_relax_model_same_bounds(get_model_vars(NLP_model))
NLP_model.pprint()

2 Var Declarations
    x1 : Size=1, Index=None
        Key  : Lower : Value : Upper : Fixed : Stale : Domain
        None :     1 :  None :    20 : False :  True :  Reals
    x2 : Size=1, Index=None
        Key  : Lower : Value : Upper : Fixed : Stale : Domain
        None :     1 :  None :    20 : False :  True :  Reals

1 Objective Declarations
    obj : Size=1, Index=None, Active=True
        Key  : Active : Sense    : Expression
        None :   True : minimize :  - x1 - x2

3 Constraint Declarations
    g1 : Size=1, Index=None, Active=True
        Key  : Lower : Body                                                                             : Upper : Active
        None :  -Inf : -5 + 0.15*( -8 + x1 )**2.0 + 0.1*( -6 + x2 )**2.0 + 0.025 * exp( x1 ) * x2**-2.0 :   0.0 :   True
    g2 : Size=1, Index=None, Active=True
        Key  : Lower : Body                                        : Upper : Active
        None :  -Inf : 4 + x1**-1.0 + x2**-1.0 - x1**0.5 * x2**0.5 :   0.0 :   True
    l1 : Size=1, Index=None, Active=True
        Key  : Lower : Body             : Upper : Active
        None :  -Inf : -2 + 2*x1 - 3*x2 :   0.0 :   True

6 Declarations: x1 x2 obj g1 g2 l1

现在我使用我的函数来更改/修改模型：

epi_model_ESH = epgraph_reformulation_without_bounds(NLP_model)
epi_model_ESH.pprint()

WARNING: Implicitly replacing the Component attribute obj (type=<class
    'pyomo.core.base.objective.SimpleObjective'>) on block Example 1 with a
    new Component (type=<class 'pyomo.core.base.objective.SimpleObjective'>).
    This is usually indicative of a modelling error. To avoid this warning,
    use block.del_component() and block.add_component().
3 Var Declarations
    alpha_epi : Size=1, Index=None
        Key  : Lower : Value : Upper : Fixed : Stale : Domain
        None :  None :  None :  None : False :  True :  Reals
    x1 : Size=1, Index=None
        Key  : Lower : Value             : Upper : Fixed : Stale : Domain
        None :     1 : 8.636750397018059 :    20 : False : False :  Reals
    x2 : Size=1, Index=None
        Key  : Lower : Value              : Upper : Fixed : Stale : Domain
        None :     1 : 12.335071455814422 :    20 : False : False :  Reals

1 Objective Declarations
    obj : Size=1, Index=None, Active=True
        Key  : Active : Sense    : Expression
        None :   True : minimize :  alpha_epi

3 Constraint Declarations
    g1 : Size=1, Index=None, Active=True
        Key  : Lower : Body                                                                                         : Upper : Active
        None :  -Inf : -5 + 0.15*( -8 + x1 )**2.0 + 0.1*( -6 + x2 )**2.0 + 0.025 * exp( x1 ) * x2**-2.0 - alpha_epi :   0.0 :   True
    g2 : Size=1, Index=None, Active=True
        Key  : Lower : Body                                                    : Upper : Active
        None :  -Inf : 4 + x1**-1.0 + x2**-1.0 - x1**0.5 * x2**0.5 - alpha_epi :   0.0 :   True
    l1 : Size=1, Index=None, Active=True
        Key  : Lower : Body             : Upper : Active
        None :  -Inf : -2 + 2*x1 - 3*x2 :   0.0 :   True

7 Declarations: x1 x2 g1 g2 l1 alpha_epi obj

但是，如果尝试使用IPOPT来解决新创建的模型，则会出现以下错误：

opt = SolverFactory('ipopt')
#opt.options['bonmin.algorithm'] = 'Bonmin'
print('using IPOPT')
# Set Options for solver.
#opt.options['bonmin.solution_limit'] = '1'
#opt.options['bonmin.time_limit'] = 1800
results = opt.solve(epi_model_ESH, tee = True)
results.write()

using IPOPT
ERROR: Variable 'x1' is not part of the model being written out, but appears
    in an expression used on this model.
ERROR: Variable 'x2' is not part of the model being written out, but appears
    in an expression used on this model.

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-22-dcc4023897c3> in <module>
      5 #opt.options['bonmin.solution_limit'] = '1'
      6 #opt.options['bonmin.time_limit'] = 1800
----> 7 results = opt.solve(epi_model_ESH, tee = True)
      8 results.write()

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\opt\base\solvers.py in solve(self, *args, **kwds)
    594             initial_time = time.time()
    595 
--> 596             self._presolve(*args, **kwds)
    597 
    598             presolve_completion_time = time.time()

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\opt\solver\shellcmd.py in _presolve(self, *args, **kwds)
    194         self._keepfiles = kwds.pop("keepfiles", False)
    195 
--> 196         OptSolver._presolve(self, *args, **kwds)
    197 
    198         #

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\opt\base\solvers.py in _presolve(self, *args, **kwds)
    691                                       self._problem_format,
    692                                       self._valid_problem_formats,
--> 693                                       **kwds)
    694             total_time = time.time() - write_start_time
    695             if self._report_timing:

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\opt\base\solvers.py in _convert_problem(self, args, problem_format, valid_problem_formats, **kwds)
    762                                valid_problem_formats,
    763                                self.has_capability,
--> 764                                **kwds)
    765 
    766     def _default_results_format(self, prob_format):

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\opt\base\convert.py in convert_problem(args, target_problem_type, valid_problem_types, has_capability, **kwds)
    108                     tmpkw = kwds
    109                     tmpkw['capabilities'] = has_capability
--> 110                     problem_files, symbol_map = converter.apply(*tmp, **tmpkw)
    111                     return problem_files, ptype, symbol_map
    112 

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\solvers\plugins\converter\model.py in apply(self, *args, **kwds)
    190                             format=args[1],
    191                             solver_capability=capabilities,
--> 192                             io_options=io_options)
    193                 return (problem_filename,), symbol_map_id
    194             else:

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\core\base\block.py in write(self, filename, format, solver_capability, io_options)
   1645                                           filename,
   1646                                           solver_capability,
-> 1647                                           io_options)
   1648         smap_id = id(smap)
   1649         if not hasattr(self, 'solutions'):

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\repn\plugins\ampl\ampl_.py in __call__(self, model, filename, solver_capability, io_options)
    390                     skip_trivial_constraints=skip_trivial_constraints,
    391                     file_determinism=file_determinism,
--> 392                     include_all_variable_bounds=include_all_variable_bounds)
    393 
    394         self._symbolic_solver_labels = False

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\repn\plugins\ampl\ampl_.py in _print_model_NL(self, model, solver_capability, show_section_timing, skip_trivial_constraints, file_determinism, include_all_variable_bounds)
    959                         ampl_repn,
    960                         list(self_varID_map[id(var)] for var in ampl_repn._linear_vars),
--> 961                         list(self_varID_map[id(var)] for var in ampl_repn._nonlinear_vars))
    962                 except KeyError as err:
    963                     self._symbolMapKeyError(err, model, self_varID_map,

~\Anaconda3\envs\seminarorsteinss2019\lib\site-packages\pyomo\repn\plugins\ampl\ampl_.py in <genexpr>(.0)
    959                         ampl_repn,
    960                         list(self_varID_map[id(var)] for var in ampl_repn._linear_vars),
--> 961                         list(self_varID_map[id(var)] for var in ampl_repn._nonlinear_vars))
    962                 except KeyError as err:
    963                     self._symbolMapKeyError(err, model, self_varID_map,

KeyError: (200822968, "Variable 'x1' is not part of the model being written out, but appears in an expression used on this model.", "Variable 'x2' is not part of the model being written out, but appears in an expression used on this model.")

新模型的pprint（）仍然将x1和x2列为变量。你知道吗

是我用constraint._body = ...造成的吗？你知道吗

Answer 1

我发现了错误。你知道吗

在

epi_nonlinear_constrs[k]._body = (epi_nonlinear_constrs[k].body - epi_model.alpha_epi)   

我使用了另一个模型的约束体nonlinear_constrs[k].body，而不是同一个模型的约束体。因此，约束有模型中未引用的变量。因此，来自解算器的错误消息。你知道吗