didppy.BreadthFirstSearch

class didppy.BreadthFirstSearch

Breadth-first search solver.

This performs breadth-first search using the dual bound as the heuristic function.

To apply this solver, the cost must be computed in the form of x + state_cost, x * state_cost, didppy.max(x, state_cost), or didppy.min(x, state_cost) where, state_cost is either of IntExpr.state_cost() and FloatExpr.state_cost(), and x is a value independent of state_cost. Otherwise, it may not produce the optimal solution.

Breadth-first searches layer by layer, where the i th layer contains states that can be reached with i transitions. By default, this solver only keeps states in the current layer to check for duplicates. If keep_all_layers is True, this solver keeps states in all layers to check for duplicates.

Parameters:
  • model (Model) – DyPDL model to solve.

  • f_operator (FOperator, default: FOperator.Plus) – If the cost is computed by +, this should be Plus. If the cost is computed by *, this should be Product. If the cost is computed by max, this should be Max. If the cost is computed by min, this should be Min.

  • primal_bound (int, float, or None, default: None) – Primal bound.

  • time_limit (int, float, or None, default: None) – Time limit.

  • get_all_solutions (bool, default: False) – Return a solution if it is not improving when search_next() is called.

  • quiet (bool, default: False) – Suppress the log output or not.

  • initial_registry_capacity (int, default: 1000000) – Initial size of the data structure storing all generated states.

  • keep_all_layers (bool, default: False) – Keep all layers of the search graph for duplicate detection in memory.

Raises:
  • TypeError – If primal_bound is float and model is int cost.

  • PanicException – If time_limit is negative.

Examples

Example with + operator.

>>> import didppy as dp
>>> model = dp.Model()
>>> x = model.add_int_var(target=1)
>>> model.add_base_case([x == 0])
>>> t = dp.Transition(
...     name="decrement",
...     cost=1 + dp.IntExpr.state_cost(),
...     effects=[(x, x - 1)]
... )
>>> model.add_transition(t)
>>> model.add_dual_bound(x)
>>> solver = dp.BreadthFirstSearch(model, quiet=True)
>>> solution = solver.search()
>>> print(solution.cost)
1

Example with max operator.

>>> import didppy as dp
>>> model = dp.Model()
>>> x = model.add_int_var(target=2)
>>> model.add_base_case([x == 0])
>>> t = dp.Transition(
...     name="decrement",
...     cost=dp.max(x, dp.IntExpr.state_cost()),
...     effects=[(x, x - 1)]
... )
>>> model.add_transition(t)
>>> model.add_dual_bound(x)
>>> solver = dp.BreadthFirstSearch(model, f_operator=dp.FOperator.Max, quiet=True)
>>> solution = solver.search()
>>> print(solution.cost)
2

Methods

search()

Search for the optimal solution of a DyPDL model.

search_next()

Search for the next solution of a DyPDL model.

search()

Search for the optimal solution of a DyPDL model.

Returns:

Solution.

Return type:

Solution

Raises:

PanicException – If the model is invalid.

Examples

>>> import didppy as dp
>>> model = dp.Model()
>>> x = model.add_int_var(target=1)
>>> model.add_base_case([x == 0])
>>> t = dp.Transition(
...     name="decrement",
...     cost=1 + dp.IntExpr.state_cost(),
...     effects=[(x, x - 1)]
... )
>>> model.add_transition(t)
>>> model.add_dual_bound(x)
>>> solver = dp.BreadthFirstSearch(model, quiet=True)
>>> solution = solver.search()
>>> solution.cost
1
search_next()

Search for the next solution of a DyPDL model.

Returns:

  • solution (Solution) – Solution.

  • terminated (bool) – Whether the search is terminated.

Raises:

PanicException – If the model is invalid.

Examples

>>> import didppy as dp
>>> model = dp.Model()
>>> x = model.add_int_var(target=1)
>>> model.add_base_case([x == 0])
>>> t = dp.Transition(
...     name="decrement",
...     cost=1 + dp.IntExpr.state_cost(),
...     effects=[(x, x - 1)]
... )
>>> model.add_transition(t)
>>> model.add_dual_bound(x)
>>> solver = dp.BreadthFirstSearch(model, quiet=True)
>>> solution, terminated = solver.search_next()
>>> solution.cost
1
>>> terminated
True