C++ Reference: class DisjunctivePropagator

Note: This documentation is automatically generated.

This class acts like a CP propagator: it takes a set of tasks given by their start/duration/end features, and reduces the range of possible values.

Method
`ChainSpanMin`	Return type: `bool` Arguments: `Tasks* tasks` Propagates a lower bound of the chain span, end[num_chain_tasks] - start[0], to span_min.
`ChainSpanMinDynamic`	Return type: `bool` Arguments: `Tasks* tasks` Computes a lower bound of the span of the chain, taking into account only the first nonchain task. For more accurate results, this should be called after Precedences(), otherwise the lower bound might be lower than feasible.
`Clear`	Return type: `void`
`DetectablePrecedencesWithChain`	Return type: `bool` Arguments: `Tasks* tasks` Does detectable precedences deductions on tasks in the chain precedence, taking the time windows of nonchain tasks into account.
`DistanceDuration`	Return type: `bool` Arguments: `Tasks* tasks` Propagates distance_duration constraints, if any.
`EdgeFinding`	Return type: `bool` Arguments: `Tasks* tasks` Does edge-finding deductions on all tasks.
`ForbiddenIntervals`	Return type: `bool` Arguments: `Tasks* tasks` Tasks might have holes in their domain, this enforces such holes.
`MirrorTasks`	Return type: `bool` Arguments: `Tasks* tasks` Transforms the problem with a time symmetry centered in 0. Returns true for convenience.
`Precedences`	Return type: `bool` Arguments: `Tasks* tasks` Propagates the deductions from the chain of precedences, if there is one.
`Propagate`	Return type: `bool` Arguments: `Tasks* tasks` Computes new bounds for all tasks, returns false if infeasible. This does not compute a fixed point, so recalling it may filter more.