When running this code
import numpy as np
import xpress as xp
z = np.array([xp.var () for i in range (200)]).reshape (4,5,10)
t = np.array([xp.var (vartype = xp.binary) for i in range (200)]).reshape (4,5,10)
p = xp.problem()
p.addVariable(z,t)
p.addConstraint(z <= 1 + t)
I get the following error
Invalid constraint
---------------------------------------------------------------------------
ModelError Traceback (most recent call last)
3 p = xp.problem()
4 p.addVariable(z,t)
----> 5 p.addConstraint(z <= 1 + t)
6 p.addConstraint(xp.Sum(z[i][j][k] for i in range (4) for j in range (5)) <= 4 for k in range (10))
ModelError: Invalid constraint
Any help would be greatly appreciated, since I'm not sure how to fix it!
The dtype of the np arrays must be explicitly set to xp.npvar. This is stated here:
The NumPy arrays must have the attribute dtype equal to xpress.npvar (abbreviated to xp.npvar here) in order to use the matricial/vectorial form of the comparison (<=, =, >=), arithmetic (+, -, *, /, **), and logic (&, |) operators.
If you don't set the type to npvar, the wrong overloads for these operators will be used and z <= 1 - t will just be an array of booleans.
This is the correct way to create your arrays:
z = np.array([xp.var () for i in range (200)], dtype=xp.npvar).reshape (4,5,10)
t = np.array([xp.var (vartype = xp.binary) for i in range (200)], dtype=xp.npvar).reshape (4,5,10)