Incompatible LpConstraint with LHS as numpy value

[fledee]

Details for the issue

What did you do?

What I think is a bug, consists in writing a LpConstraint from LpVariables the following way: LHS == RHS

Case 1: using Python int/float alone on RHS >> LpVariable(name='X1') == 1

Case 2: using Numpy int/float alone on RHS >> LpVariable(name='X1') == numpy.float_(1)

Case 3: using Python int/float alone on LHS >> 1 == LpVariable(name='X1')

Case 4 (Does not work): using Numpy int/float alone on LHS >> numpy.float_(1) == LpVariable(name='X1')

Note: the issue is the same with any operator (==, >=, <=) and any constant (as numpy float or integer)

Note: original use is to automatically perform lots of optimization problems, using values extracted from numpy matrices. Some equations of the problem are so that, for some values, we end up with a numpy float on the LHS of equations. Above is the most simplified version of the problem.

What did you expect to see?

In cases 1, 2 and 3, the result is a LpConstraint object for the equation 1*X1 + -1 = 0 So it is expected for case 4 to also return a LpConstraint object for the equation 1*X1 + -1 = 0.

What did you see instead?

Instead, in case 4, the result is True of type numpy.bool_, which makes the constraint vanish from an actual problem.

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• [ ] 2.7
• [ ] 3.4
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• [X] 3.6
• [X] Other: 3.8.10

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Did you also

• [X] Tried out the latest github version: https://github.com/coin-or/pulp
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