penaltymodel
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dwavesystems
Utilities and interfaces for using penalty models.
There was an install error for OSX 10.10.5, python2.7 ``` Collecting ortools=6.6.4659 (from penaltymodel-mip=0.1.2; platform_machine != "x86" and python_version != "3.4"->dwavebinarycsp=0.0.2->dwave-ocean-sdk) Could not find a version that satisfies the requirement...
**Description** If you `pip install dwavebinarycsp` and then build `penaltymodel-maxgap`, running any `dwavebinarycsp.stich()` fails for `scipy==1.4.1`: ``` decorator==4.4.2 dimod==0.9.11 dwave-networkx==0.8.4 dwavebinarycsp==0.1.2 homebase==1.0.1 networkx==2.5.1 numpy==1.20.2 penaltymodel==0.16.4 penaltymodel-cache==0.4.3 penaltymodel-lp==0.1.4 penaltymodel-maxgap==0.5.4 PySMT==0.8.0 scipy==1.4.1...
This is branched from feature/69-balanced-penaltymodels - ignore `utilities.py` as those changes are from feature/69-blanaced-penaltymodels
**Current Problem** When no quadratic biases are present in a penaltymodel, return an error that is more comprehensible to the end user. (Note: end user is likely to be calling...
e.g. ``` feasible_configurations, decision_variables = pm.AND('a', 'b', 'c') ```
**Current Problem** Currently it returns a gap of 0.
**Current Problem** Currently, the Specification class is a super class of PenaltyModel. This is a bit confusing as Specification objects get passed to penaltymodel-mip, -maxgap, and -lp in order to...
**Current Problem** Currently, MaxGap may apply auxiliary variables to the Ising equation. This could potentially lead to double counting a feasible state. (ex. One feasible state, S1, may have only...
**Current Problem** It would be good if the default energy ranges for linear/quadratic biases were stored in one central location rather than hardcoded in the different packages **Proposed Solution** Add...
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Utilities and interfaces for using penalty models.