Added support for an indicator constraint to QuadraticProgram #151
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a-matsuo
added
the
Changelog: New feature
a-matsuo
requested review from
manoelmarques,
pbark,
stefan-woerner and
woodsp-ibm
as code owners
a-matsuo
changed the title
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I think the PR is ready for review. Could you please review this? |
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Thanks. Could you write a reno? |
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Sure (I had written reno but I had forgotten to add it). |
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Should we be adding a new tutorial, or updating an existing one, to show this new capability. For significant new features I think it would be good, since we have the tutorials now in this same repo, that they are updated/improved at the same time if the new/updated feature warrants it. |
…tion into indicator_const
…tion into indicator_const
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@woodsp-ibm Thank you for your helpful comments. I fixed the code. I haven't fixed all the f string and typehints, but I fixed them at least in this code. |
| from ..problems.variable import Variable | ||
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| class IndicatorToInequality(QuadraticProgramConverter): |
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I suggest IndicatorToLinear because this converts not only inequality constraints but also equality constraints. Another reason is that there is another way to convert indicator constraints into quadratic constraints.
| new_name = indicator_const.name + self._delimiter + "indicator" | ||
| if sense == Constraint.Sense.LE: | ||
| _, lhs_ub = self._calc_linear_bounds(problem, new_linear) | ||
| big_m = lhs_ub - new_rhs |
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make sure that big_m is a positive value.
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I think it does work even when big_m is a negative value in this case. When it's inactive, it becomes lhs <= the upperbounds of lhs. (When rhs is larger than the upper bound of lhs, the constraint doesn't make sense though).
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The indicator constraints will be treated as part of a docplex translator #122 |
Summary
resolve #13
The PR adds support for an indicator constraint to
QuadraticProgram.Details and comments
An indicator constraint control the satisfaction of a linear constraint by the value of a binary variable. If the binary variable equals the active value, then the constraint must be satisfied, but otherwise the constraint might or might not be satisfied.
e.g.
x = 1 -> y + z = 1