给定点的非线性pyomo约束的梯度

Question

I (repeatedly) need numeric gradient information of a nonlinear pyomo constraint con at a given point (ie the variables of the corresponding pyomo model are all set to a specific value). I have read this post and decided that the (slightly modified) lines

from pyomo.core.base.symbolic import differentiate

var_list = list(model.component_objects(Var, active=True))
grad_num = [value(partial) for partial in differentiate(g_nu.body, wrt_list=vars)] 

should serve my purpose.

However, the example below already fails, presumably due to the appearance of the exponential function:

from pyomo.environ import *

model = ConcreteModel()
model.x_1 = Var()
model.x_2 = Var()
model.constr = Constraint(expr = 2*(model.x_1)**4+exp(model.x_2)<=3)
model.x_1.set_value(1)
model.x_2.set_value(1)
varList = list(model.component_objects(Var, active=True))
grad = [value(partial) for partial in differentiate(model.constr.body, wrt_list=varList)]

DeveloperError: Internal Pyomo implementation error: "sympy expression type 'exp' not found in the operator map for expression >exp(x1)" Please report this to the Pyomo Developers.

So, my question is: Can pyomo generally differentiate expressions like the exponential function/ square root etc. and is my example just an unfortunate coincidence which can be easily fixed? I will deal with various models from the MINLPLIB and some tool for differentiating the appearing expressions is crucial.

Answer 1

This error existed through Pyomo 5.2 and was resolved in Pyomo 5.3. Upgrading to 5.3 fixes the problem, and your example works fine (after adding from pyomo.core.base.symbolic import differentiate ).