DanieleGioia
commented
2 years ago Hi, I'm dealing with the same issue.
I'm working on a two-stage DRO model with a bound on the variances. Once I noticed the issue I tried the given DRO example on portfolio optimization in the DRO guide. That one doesn't work as well due to the same error ( unsupported operand type(s) for @: 'Var' and 'Var'" ) on the squared constraints on the support.
(Nb: that example has some other errors easy to solve like bounds defined as z then used as d and pandas .std() called as .sigma() )
Looking into the code, the problem could also be a Gurobi related one. It is generated when the conic constraints are added to the model. The critic function (and constraint) is rsome.square() and the squared norm too. No issues with 1-norm (that does not involve SOC).
I was not able to fix it so far. Hopefully, the authors will give us a solution.
COMMENT UPDATE: Actually, it turns out to be a Gurobi communication problem. WIth MOSEK everything is fine.
XiongPengNUS
when I compile the file, it gives a message that" Traceback (most recent call last): File "C:\Users\zhang\Documents\Pycharm\rsome\test_mv.py", line 44, in
model.solve(grb) # solve the model by Gurobi
File "E:\Python\lib\site-packages\rsome\dro.py", line 656, in solve
solution = solver.solve(self.do_math(), display, export, params)
File "E:\Python\lib\site-packages\rsome\grb_solver.py", line 39, in solve
x[index_right] @ x[index_right])
TypeError: unsupported operand type(s) for @: 'Var' and 'Var'"
My code is a demo from the DRO
"from rsome import dro from rsome import norm from rsome import E from rsome import grb_solver as grb import numpy as np import numpy.random as rd
model and ambiguity set parameters
I = 2 S = 50 c = np.ones(I) d = 50 I p = 1 + 4rd.rand(I) zbar = 100 rd.rand(I) zhat = zbar rd.rand(S, I) theta = 0.01 * zbar.min()
modeling with RSOME
model = dro.Model(S) # create a DRO model with S scenarios z = model.rvar(I) # random demand z u = model.rvar() # auxiliary random variable
fset = model.ambiguity() # create an ambiguity set for s in range(S): fset[s].suppset(0 <= z, z <= zbar, norm(z - zhat[s]) <= u) # define the support for each scenario fset.exptset(E(u) <= theta) # the Wasserstein metric constraint pr = model.p # an array of scenario probabilities fset.probset(pr == 1/S) # support of scenario probabilities
x = model.dvar(I) # define first-stage decisions y = model.dvar(I) # define decision rule variables y.adapt(z) # y affinely adapts to z y.adapt(u) # y affinely adapts to u for s in range(S): y.adapt(s) # y adapts to each scenario s
model.minsup(-p@x + E(p@y), fset) # worst-case expectation over fset model.st(y >= 0) # constraints model.st(y >= x - z) # constraints model.st(x >= 0) # constraints model.st(c@x == d) # constraints
model.solve(grb) # solve the model by Gurobi "