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# Backtracking

This library contains generic tools for constructing large sets whose elements can be enumerated by exploring a search space with a (lazy) tree or graph structure.

- `GenericBacktracker`: Depth first search through a tree described by a `children` function, with branch pruning, etc.

Deprecated classes (use `RecursivelyEnumeratedSet()` instead):

- `SearchForest`: Depth and breadth first search through a tree described by a `children` function.
- `TransitiveIdeal`: Depth first search through a graph described by a `neighbours` relation.
- `TransitiveIdealGraded`: Breadth first search through a graph described by a `neighbours` relation.

Deprecation details:

- `SearchForest(seeds, succ)` keeps the same behavior as before trac ticket #6637 and is now the same as `RecursivelyEnumeratedSet(seeds, succ, structure='forest', enumeration='depth')`.
- `TransitiveIdeal(succ, seeds)` keeps the same behavior as before trac ticket #6637 and is now the same as `RecursivelyEnumeratedSet(seeds, succ, structure=None, enumeration='naive')`.
- `TransitiveIdealGraded(succ, seeds, max_depth)` keeps the same behavior as before trac ticket #6637 and is now the same as `RecursivelyEnumeratedSet(seeds, succ, structure=None, enumeration='breadth', max_depth=max_depth)`.

TODO:

- For now the code of `SearchForest` is still in `sage/combinat/backtrack.py`. It should be moved in `sage/sets/recursively_enumerated_set.pyx` into a class named `RecursivelyEnumeratedSet_forest` in a later ticket.
- `TransitiveIdeal` and `TransitiveIealGraded` are used in the code of `sage/combinat`, `sage/categories` and `sage/groups` at least. These should be updated to use `RecursivelyEnumeratedSet` in a later ticket for speed improvements.
- Once the deprecation has been there for enough time: delete `TransitiveIdeal` and `TransitiveIealGraded`.

class `sage.combinat.backtrack.``GenericBacktracker`(initial_data, initial_state)

  Bases: `object`

  A generic backtrack tool for exploring a search space organized as a tree, with branch pruning, etc.

  See also `SearchForest` and `TransitiveIdeal` for handling simple special cases.

class `sage.combinat.backtrack.``PositiveIntegerSemigroup`

  Bases: `sage.structure.unique_representation.UniqueRepresentation`, `sage.combinat.backtrack.SearchForest`

  The commutative additive semigroup of positive integers.

  This class provides an example of algebraic structure which inherits from `SearchForest`. It builds the positive integers a la Peano, and endows it with its natural commutative additive semigroup structure.

  EXAMPLES:

      sage: from sage.combinat.backtrack import PositiveIntegerSemigroup
      sage: PP = PositiveIntegerSemigroup()
      sage: PP.category()
      Join of Category of monoids and Category of commutative additive semigroups and Category of infinite enumerated sets and Category of facade sets
      sage: PP.cardinality()
      +Infinity
      sage: PP.one()
      1
      sage: PP.an_element()
      1
      sage: some_elements = list(PP.some_elements()); some_elements
      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]

  TESTS:

      sage: from sage.combinat.backtrack import PositiveIntegerSemigroup
      sage: PP = PositiveIntegerSemigroup()

  We factor out the long test from the `TestSuite`:

      sage: TestSuite(PP).run(skip='_test_enumerated_set_contains')
      sage: PP._test_enumerated_set_contains()  # long time

  `children`(x)

    Return the single child `x+1` of the integer `x`

    EXAMPLES:

        sage: from sage.combinat.backtrack import PositiveIntegerSemigroup
        sage: PP = PositiveIntegerSemigroup()
        sage: list(PP.children(1))
        [2]
        sage: list(PP.children(42))
        [43]

  `one`()

    Return the unit of `self`.

    EXAMPLES:

        sage: from sage.combinat.backtrack import PositiveIntegerSemigroup
        sage: PP = PositiveIntegerSemigroup()
        sage: PP.one()
        1

  `roots`()

    Return the single root of `self`.

    EXAMPLES:

        sage: from sage.combinat.backtrack import PositiveIntegerSemigroup
        sage: PP = PositiveIntegerSemigroup()
        sage: list(PP.roots())
        [1]

class `sage.combinat.backtrack.``SearchForest`(roots=None, children=None, post_process=None, algorithm='depth', facade=None, category=None)

  Bases: `sage.structure.parent.Parent`

  The enumerated set of the nodes of the forest having the given `roots`, and where `children(x)` returns the children of the node `x` of the forest.

  See also `GenericBacktracker`, `TransitiveIdeal`, and `TransitiveIdealGraded`.

  INPUT:

  - `roots` – a list (or iterable)
  - `children` – a function returning a list (or iterable, or iterator)
  - `post_process` – a function defined over the nodes of the forest (default: no post processing)
  - `algorithm` – `'depth'` or `'breadth'` (default: `'depth'`)
  - `category` – a category (default: `EnumeratedSets`)

  The option `post_process` allows for customizing the nodes that are actually produced. Furthermore, if `f(x)` returns `None`, then `x` won’t be output at all.

  EXAMPLES:

  We construct the set of all binary sequences of length at most three, and list them:

      sage: S = SearchForest( [[]],
      ....:     lambda l: [l+[0], l+[1]] if len(l) < 3 else [],
      ....:     category=FiniteEnumeratedSets())
      doctest:...: DeprecationWarning: This class soon will not be
      available in that way anymore. Use RecursivelyEnumeratedSet
      instead.  See http://trac.sagemath.org/6637 for details.
      sage: S.list()
      [[],
       [0], [0, 0], [0, 0, 0], [0, 0, 1], [0, 1], [0, 1, 0], [0, 1, 1],
       [1], [1, 0], [1, 0, 0], [1, 0, 1], [1, 1], [1, 1, 0], [1, 1, 1]]

  `SearchForest` needs to be explicitly told that the set is finite for the following to work:

      sage: S.category()
      Category of finite enumerated sets
      sage: S.cardinality()
      15

  We proceed with the set of all lists of letters in `0,1,2` without repetitions, ordered by increasing length (i.e. using a breadth first search through the tree):

      sage: tb = SearchForest( [[]],
      ....:       lambda l: [l + [i] for i in range(3) if i not in l],
      ....:       algorithm = 'breadth',
      ....:       category=FiniteEnumeratedSets())
      doctest:...: DeprecationWarning: This class soon will not be
      available in that way anymore. Use RecursivelyEnumeratedSet
      instead.  See http://trac.sagemath.org/6637 for details.
      sage: tb[0]
      []
      sage: tb.cardinality()
      16
      sage: list(tb)
      [[],
       [0], [1], [2],
       [0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1],
       [0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0]]

  For infinite sets, this option should be set carefully to ensure that all elements are actually generated. The following example builds the set of all ordered pairs \((i,j)\) of nonnegative integers such that \(j\leq 1\):

      sage: I = SearchForest([(0,0)],
      ....:                  lambda l: [(l[0]+1, l[1]), (l[0], 1)]
      ....:                            if l[1] == 0 else [(l[0], l[1]+1)])
      doctest:...: DeprecationWarning: This class soon will not be
      available in that way anymore. Use RecursivelyEnumeratedSet
      instead.  See http://trac.sagemath.org/6637 for details.

  With a depth first search, only the elements of the form \((i,0)\) are generated:

      sage: depth_search = I.depth_first_search_iterator()
      sage: [depth_search.next() for i in range(7)]
      [(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0)]

  Using instead breadth first search gives the usual anti-diagonal iterator:

      sage: breadth_search = I.breadth_first_search_iterator()
      sage: [breadth_search.next() for i in range(15)]
      [(0, 0),
       (1, 0), (0, 1),
       (2, 0), (1, 1), (0, 2),
       (3, 0), (2, 1), (1, 2), (0, 3),
       (4, 0), (3, 1), (2, 2), (1, 3), (0, 4)]

  Deriving subclasses

  The class of a parent \(A\) may derive from `SearchForest` so that \(A\) can benefit from enumeration tools. As a running example, we consider the problem of enumerating integers whose binary expansion have at most three nonzero digits. For example, \(3 = 2^1 + 2^0\) has two nonzero digits. \(15 = 2^3 + 2^2 + 2^1 + 2^0\) has four nonzero digits. In fact, \(15\) is the smallest integer which is not in the enumerated set.

  To achieve this, we use `SearchForest` to enumerate binary tuples with at most three nonzero digits, apply a post processing to recover the corresponding integers, and discard tuples finishing by zero.

  A first approach is to pass the `roots` and `children` functions as arguments to `SearchForest.__init__()`:

      sage: from sage.combinat.backtrack import SearchForest
      sage: class A(UniqueRepresentation, SearchForest):
      ....:     def __init__(self):
      ....:         SearchForest.__init__(self, [()],
      ....:             lambda x : [x+(0,), x+(1,)] if sum(x) < 3 else [],
      ....:             lambda x : sum(x[i]*2^i for i in range(len(x))) if sum(x) != 0 and x[-1] != 0 else None,
      ....:             algorithm = 'breadth',
      ....:             category=InfiniteEnumeratedSets())
      sage: MyForest = A(); MyForest
      An enumerated set with a forest structure
      sage: MyForest.category()
      Category of infinite enumerated sets
      sage: p = iter(MyForest)
      sage: [p.next() for i in range(30)]
      [1, 2, 3, 4, 6, 5, 7, 8, 12, 10, 14, 9, 13, 11, 16, 24, 20, 28, 18, 26, 22, 17, 25, 21, 19, 32, 48, 40, 56, 36]

  An alternative approach is to implement `roots` and `children` as methods of the subclass (in fact they could also be attributes of \(A\)). Namely, `A.roots()` must return an iterable containing the enumeration generators, and `A.children(x)` must return an iterable over the children of \(x\). Optionally, \(A\) can have a method or attribute such that `A.post_process(x)` returns the desired output for the node `x` of the tree:

      sage: from sage.combinat.backtrack import SearchForest
      sage: class A(UniqueRepresentation, SearchForest):
      ....:     def __init__(self):
      ....:         SearchForest.__init__(self, algorithm = 'breadth',
      ....:                               category=InfiniteEnumeratedSets())
      ....:
      ....:     def roots(self):
      ....:         return [()]
      ....:
      ....:     def children(self, x):
      ....:         if sum(x) < 3:
      ....:             return [x+(0,), x+(1,)]
      ....:         else:
      ....:             return []
      ....:
      ....:     def post_process(self, x):
      ....:         if sum(x) == 0 or x[-1] == 0:
      ....:             return None
      ....:         else:
      ....:             return sum(x[i]*2^i for i in range(len(x)))
      sage: MyForest = A(); MyForest
      An enumerated set with a forest structure
      sage: MyForest.category()
      Category of infinite enumerated sets
      sage: p = iter(MyForest)
      sage: [p.next() for i in range(30)]
      [1, 2, 3, 4, 6, 5, 7, 8, 12, 10, 14, 9, 13, 11, 16, 24, 20, 28, 18, 26, 22, 17, 25, 21, 19, 32, 48, 40, 56, 36]

  Warning

  A `SearchForest` instance is picklable if and only if the input functions are themselves picklable. This excludes anonymous or interactively defined functions:

      sage: def children(x):
      ....:     return [x+1]
      sage: S = SearchForest( [1], children, category=InfiniteEnumeratedSets())
      sage: dumps(S)
      Traceback (most recent call last):
      ....:
      PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed

  Let us now fake `children` being defined in a Python module:

      sage: import __main__
      sage: __main__.children = children
      sage: S = SearchForest( [1], children, category=InfiniteEnumeratedSets())
      sage: loads(dumps(S))
      An enumerated set with a forest structure

  `breadth_first_search_iterator`()

    Return a breadth first search iterator over the elements of `self`

    EXAMPLES:

        sage: f = SearchForest([[]],
        ....:                  lambda l: [l+[0], l+[1]] if len(l) < 3 else [])
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: list(f.breadth_first_search_iterator())
        [[], [0], [1], [0, 0], [0, 1], [1, 0], [1, 1], [0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]]
        sage: S = SearchForest([(0,0)],
        ....: lambda x : [(x[0], x[1]+1)] if x[1] != 0 else [(x[0]+1,0), (x[0],1)],
        ....: post_process = lambda x: x if ((is_prime(x[0]) and is_prime(x[1])) and ((x[0] - x[1]) == 2)) else None)
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: p = S.breadth_first_search_iterator()
        sage: [p.next(), p.next(), p.next(), p.next(), p.next(), p.next(), p.next()]
        [(5, 3), (7, 5), (13, 11), (19, 17), (31, 29), (43, 41), (61, 59)]

  `children`(x)

    Return the children of the element `x`

    The result can be a list, an iterable, an iterator, or even a generator.

    EXAMPLES:

        sage: I = SearchForest([(0,0)], lambda l: [(l[0]+1, l[1]), (l[0], 1)] if l[1] == 0 else [(l[0], l[1]+1)])
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: [i for i in I.children((0,0))]
        [(1, 0), (0, 1)]
        sage: [i for i in I.children((1,0))]
        [(2, 0), (1, 1)]
        sage: [i for i in I.children((1,1))]
        [(1, 2)]
        sage: [i for i in I.children((4,1))]
        [(4, 2)]
        sage: [i for i in I.children((4,0))]
        [(5, 0), (4, 1)]

  `depth_first_search_iterator`()

    Return a depth first search iterator over the elements of `self`

    EXAMPLES:

        sage: f = SearchForest([[]],
        ....:                  lambda l: [l+[0], l+[1]] if len(l) < 3 else [])
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: list(f.depth_first_search_iterator())
        [[], [0], [0, 0], [0, 0, 0], [0, 0, 1], [0, 1], [0, 1, 0], [0, 1, 1], [1], [1, 0], [1, 0, 0], [1, 0, 1], [1, 1], [1, 1, 0], [1, 1, 1]]

  `elements_of_depth_iterator`(depth=0)

    Return an iterator over the elements of `self` of given depth. An element of depth \(n\) can be obtained applying \(n\) times the children function from a root.

    EXAMPLES:

        sage: S = SearchForest([(0,0)] ,
        ....:        lambda x : [(x[0], x[1]+1)] if x[1] != 0 else [(x[0]+1,0), (x[0],1)],
        ....:        post_process = lambda x: x if ((is_prime(x[0]) and is_prime(x[1]))
        ....:                                        and ((x[0] - x[1]) == 2)) else None)
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: p = S.elements_of_depth_iterator(8)
        sage: p.next()
        (5, 3)
        sage: S = SearchForest(NN, lambda x : [],
        ....:                      lambda x: x^2 if x.is_prime() else None)
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: p = S.elements_of_depth_iterator(0)
        sage: [p.next(), p.next(), p.next(), p.next(), p.next()]
        [4, 9, 25, 49, 121]

  `roots`()

    Return an iterable over the roots of `self`.

    EXAMPLES:

        sage: I = SearchForest([(0,0)], lambda l: [(l[0]+1, l[1]), (l[0], 1)] if l[1] == 0 else [(l[0], l[1]+1)])
        doctest:...: DeprecationWarning: This class soon will not be
        available in that way anymore. Use RecursivelyEnumeratedSet
        instead.  See http://trac.sagemath.org/6637 for details.
        sage: [i for i in I.roots()]
        [(0, 0)]
        sage: I = SearchForest([(0,0),(1,1)], lambda l: [(l[0]+1, l[1]), (l[0], 1)] if l[1] == 0 else [(l[0], l[1]+1)])
        sage: [i for i in I.roots()]
        [(0, 0), (1, 1)]

class `sage.combinat.backtrack.``TransitiveIdeal`(succ, generators)

  Bases: `sage.sets.recursively_enumerated_set.RecursivelyEnumeratedSet_generic`

  Generic tool for constructing ideals of a relation.

  INPUT:

  - `relation` – a function (or callable) returning a list (or iterable)
  - `generators` – a list (or iterable)

  Returns the set \(S\) of elements that can be obtained by repeated application of `relation` on the elements of `generators`.

  Consider `relation` as modeling a directed graph (possibly with loops, cycles, or circuits). Then \(S\) is the ideal generated by `generators` under this relation.

  Enumerating the elements of \(S\) is achieved by depth first search through the graph. The time complexity is \(O(n+m)\) where \(n\) is the size of the ideal, and \(m\) the number of edges in the relation. The memory complexity is the depth, that is the maximal distance between a generator and an element of \(S\).

  See also `SearchForest` and `TransitiveIdealGraded`.

  EXAMPLES:

      sage: [i for i in TransitiveIdeal(lambda i: [i+1] if i<10 else [], [0])]
      doctest:...: DeprecationWarning: This class soon will not be
      available in that way anymore. Use RecursivelyEnumeratedSet
      instead.  See http://trac.sagemath.org/6637 for details.
      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

      sage: [i for i in TransitiveIdeal(lambda i: [mod(i+1,3)], [0])]
      [0, 1, 2]
      sage: [i for i in TransitiveIdeal(lambda i: [mod(i+2,3)], [0])]
      [0, 2, 1]
      sage: [i for i in TransitiveIdeal(lambda i: [mod(i+2,10)], [0])]
      [0, 2, 4, 6, 8]
      sage: [i for i in TransitiveIdeal(lambda i: [mod(i+3,10),mod(i+5,10)], [0])]
      [0, 3, 8, 1, 4, 5, 6, 7, 9, 2]
      sage: [i for i in TransitiveIdeal(lambda i: [mod(i+4,10),mod(i+6,10)], [0])]
      [0, 4, 8, 2, 6]
      sage: [i for i in TransitiveIdeal(lambda i: [mod(i+3,9)], [0,1])]
      [0, 1, 3, 4, 6, 7]

      sage: [p for p in TransitiveIdeal(lambda x:[x],[Permutation([3,1,2,4]), Permutation([2,1,3,4])])]
      [[2, 1, 3, 4], [3, 1, 2, 4]]

  We now illustrate that the enumeration is done lazily, by depth first search:

      sage: C = TransitiveIdeal(lambda x: [x-1, x+1], (-10, 0, 10))
      sage: f = C.__iter__()
      sage: [ f.next() for i in range(6) ]
      [0, 1, 2, 3, 4, 5]

  We compute all the permutations of 3:

      sage: [p for p in TransitiveIdeal(attrcall("permutohedron_succ"), [Permutation([1,2,3])])]
      [[1, 2, 3], [2, 1, 3], [1, 3, 2], [2, 3, 1], [3, 2, 1], [3, 1, 2]]

  We compute all the permutations which are larger than [3,1,2,4], [2,1,3,4] in the right permutohedron:

      sage: [p for p in TransitiveIdeal(attrcall("permutohedron_succ"), [Permutation([3,1,2,4]), Permutation([2,1,3,4])])]
      [[2, 1, 3, 4], [2, 1, 4, 3], [2, 4, 1, 3], [4, 2, 1, 3], [4, 2, 3, 1], [4, 3, 2, 1], [3, 1, 2, 4], [2, 4, 3, 1], [3, 2, 1, 4], [2, 3, 1, 4], [2, 3, 4, 1], [3, 2, 4, 1], [3, 1, 4, 2], [3, 4, 2, 1], [3, 4, 1, 2], [4, 3, 1, 2]]

  Using TransitiveIdeal people have been using the `__contains__` method provided from the `__iter__` method. We need to make sure that this continues to work:

      sage: T = TransitiveIdeal(lambda a:[a+7,a+5], [0])
      sage: 12 in T
      True

class `sage.combinat.backtrack.``TransitiveIdealGraded`(succ, generators, max_depth=inf)

  Bases: `sage.sets.recursively_enumerated_set.RecursivelyEnumeratedSet_generic`

  Generic tool for constructing ideals of a relation.

  INPUT:

  - `relation` – a function (or callable) returning a list (or iterable)
  - `generators` – a list (or iterable)
  - `max_depth` – (Default: infinity) Specifies the maximal depth to which elements are computed

  Return the set \(S\) of elements that can be obtained by repeated application of `relation` on the elements of `generators`.

  Consider `relation` as modeling a directed graph (possibly with loops, cycles, or circuits). Then \(S\) is the ideal generated by `generators` under this relation.

  Enumerating the elements of \(S\) is achieved by breadth first search through the graph; hence elements are enumerated by increasing distance from the generators. The time complexity is \(O(n+m)\) where \(n\) is the size of the ideal, and \(m\) the number of edges in the relation. The memory complexity is the depth, that is the maximal distance between a generator and an element of \(S\).

  See also `SearchForest` and `TransitiveIdeal`.

  EXAMPLES:

      sage: [i for i in TransitiveIdealGraded(lambda i: [i+1] if i<10 else [], [0])]
      doctest:...: DeprecationWarning: This class soon will not be
      available in that way anymore. Use RecursivelyEnumeratedSet
      instead.  See http://trac.sagemath.org/6637 for details.
      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

  We now illustrate that the enumeration is done lazily, by breadth first search:

      sage: C = TransitiveIdealGraded(lambda x: [x-1, x+1], (-10, 0, 10))
      sage: f = C.__iter__()

  The elements at distance 0 from the generators:

      sage: sorted([ f.next() for i in range(3) ])
      [-10, 0, 10]

  The elements at distance 1 from the generators:

      sage: sorted([ f.next() for i in range(6) ])
      [-11, -9, -1, 1, 9, 11]

  The elements at distance 2 from the generators:

      sage: sorted([ f.next() for i in range(6) ])
      [-12, -8, -2, 2, 8, 12]

  The enumeration order between elements at the same distance is not specified.

  We compute all the permutations which are larger than [3,1,2,4] or [2,1,3,4] in the permutohedron:

      sage: [p for p in TransitiveIdealGraded(attrcall("permutohedron_succ"), [Permutation([3,1,2,4]), Permutation([2,1,3,4])])]
      [[3, 1, 2, 4], [2, 1, 3, 4], [2, 1, 4, 3], [3, 2, 1, 4], [2, 3, 1, 4], [3, 1, 4, 2], [2, 3, 4, 1], [3, 4, 1, 2], [3, 2, 4, 1], [2, 4, 1, 3], [2, 4, 3, 1], [4, 3, 1, 2], [4, 2, 1, 3], [3, 4, 2, 1], [4, 2, 3, 1], [4, 3, 2, 1]]

`sage.combinat.backtrack.``search_forest_iterator`(roots, children, algorithm='depth')

  Return an iterator on the nodes of the forest having the given roots, and where `children(x)` returns the children of the node `x` of the forest. Note that every node of the tree is returned, not simply the leaves.

  INPUT:

  - `roots` – a list (or iterable)
  - `children` – a function returning a list (or iterable)
  - `algorithm` – `'depth'` or `'breadth'` (default: `'depth'`)

  EXAMPLES:

  We construct the prefix tree of binary sequences of length at most three, and enumerate its nodes:

      sage: from sage.combinat.backtrack import search_forest_iterator
      sage: list(search_forest_iterator([[]], lambda l: [l+[0], l+[1]]
      ....:                                   if len(l) < 3 else []))
      [[], [0], [0, 0], [0, 0, 0], [0, 0, 1], [0, 1], [0, 1, 0],
       [0, 1, 1], [1], [1, 0], [1, 0, 0], [1, 0, 1], [1, 1], [1, 1, 0], [1, 1, 1]]

  By default, the nodes are iterated through by depth first search. We can instead use a breadth first search (increasing depth):

      sage: list(search_forest_iterator([[]], lambda l: [l+[0], l+[1]]
      ....:                                   if len(l) < 3 else [],
      ....:                             algorithm='breadth'))
      [[],
       [0], [1],
       [0, 0], [0, 1], [1, 0], [1, 1],
       [0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1],
       [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]]

  This allows for iterating trough trees of infinite depth:

      sage: it = search_forest_iterator([[]], lambda l: [l+[0], l+[1]], algorithm='breadth')
      sage: [ it.next() for i in range(16) ]
      [[],
       [0], [1], [0, 0], [0, 1], [1, 0], [1, 1],
       [0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1],
       [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1],
       [0, 0, 0, 0]]

  Here is an interator through the prefix tree of sequences of letters in \(0,1,2\) without repetitions, sorted by length; the leaves are therefore permutations:

      sage: list(search_forest_iterator([[]], lambda l: [l + [i] for i in range(3) if i not in l],
      ....:                             algorithm='breadth'))
      [[],
       [0], [1], [2],
       [0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1],
       [0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0]]

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