I have an optimization problem with a continuous matrix variable (C), but:
My objective function is not about min/max the entries of C, but to maximize the amounts of 0 in C. My approach to model this, is to translate C to heaviside(C), which would make all non-zero entries equal to 1 and all zero entries stay 0. Then i could minimize the sum of these matrix entries, resulting a maximum of zero entries.
model.set_objective('min', sum(np.heaviside(C[k,j], 0) for k, j in itertools.product(range(M), range(P))))
Here I get errors of cplex, because C does not have defined values at this point. Any kind of casting the values of C to int or float fails as well.
TypeError: ufunc 'heaviside' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
I do not understand, why this is a problem, while e.g.
model.set_objective('min', sum(C[k,j] for k, j in itertools.product(range(M), range(P))))
works fine.
numpy functions are not allowed you should turn them into functions that are in docplex.
For instance you could rely on logical constraints
from docplex.mp.model import Model
mdl = Model(name='buses')
nbbus40 = mdl.integer_var(name='nbBus40')
nbbus30 = mdl.integer_var(name='nbBus30')
mdl.add_constraint(nbbus40*40 + nbbus30*30 >= 300, 'kids')
mdl.minimize(nbbus40*460 + nbbus30*360)
mdl.solve()
for v in mdl.iter_integer_vars():
print(v," = ",v.solution_value)
print()
print("with the logical constraint")
nbKindOfBuses = mdl.integer_var(name='nbKindOfBuses')
mdl.add(nbKindOfBuses==(nbbus40>=1)+(nbbus30>=1))
mdl.minimize(nbbus40*460 + nbbus30*360+(nbKindOfBuses-1)*(500))
mdl.solve()
for v in mdl.iter_integer_vars():
print(v," = ",v.solution_value)