MSS (cpmpy.tools.explain.mss)

cpmpy.tools.explain.mss.mss(soft, hard=[], solver='ortools')

Compute Maximal Satisfiable Subset of unsatisfiable model. Computes a subset of constraints which maximises the total number of constraints

cpmpy.tools.explain.mss.mss_grow(soft, hard=[], solver='ortools')

Compute Maximal Satisfiable Subset of unsatsifiable model. Computes a subset-maximal set of constraints by greedily adding contraints. Relies on solving under assumptions, so using an incremental solver is adviced No guarantees on optimality, but can be faster in some cases

cpmpy.tools.explain.mss.mss_grow_naive(soft, hard=[], solver='ortools')

Compute Maximal Satisfiable Subset of unsatsifiable model. Computes a subset-maximal set of constraints by greedily adding contraints. Can be used when solver does not support assumptions No guarantees on optimality, but can be faster in some cases

cpmpy.tools.explain.mss.mss_opt(soft, hard=[], weights=1, solver='ortools')

Compute Maximal Satisfiable Subset of unsatisfiable model. Constraints can be weighted using the weights parameter. Computes a subset of constraints which maximizes the sum of all weights on constraints