def test_process_branch_rtn(self):
bb = BranchAndBound(small_branch)
node = BaseNode(small_branch.lp, small_branch.integerIndices, idx=0)
node.bound()
rtn = node.branch(next_node_idx=1)
left_node = rtn['left']
right_node = rtn['right']
bb._process_branch_rtn(node.idx, rtn)
# check attributes
self.assertTrue(isinstance(bb._node_queue.get(), BaseNode))
self.assertTrue(isinstance(bb._node_queue.get(), BaseNode))
self.assertTrue(bb._node_queue.empty())
children = bb.tree.get_children(node.idx)
self.assertTrue(len(children) == 2, 'there should be two kids created')
for child in children:
self.assertFalse(bb.tree.get_children(child),
'children shouldnt have kids')
self.assertTrue(bb.tree.get_node(1).attr['node'] is left_node)
self.assertTrue(bb.tree.get_node(2).attr['node'] is right_node)
# check function calls
bb = BranchAndBound(small_branch)
node = BaseNode(small_branch.lp, small_branch.integerIndices, 0)
node.bound()
rtn = node.branch()
with patch.object(bb, '_process_rtn') as pr, \
patch.object(bb.tree, 'add_left_child') as alc, \
patch.object(bb.tree, 'add_right_child') as arc:
bb._process_branch_rtn(0, rtn)
self.assertTrue(pr.call_count == 1, 'should call process rtn')
self.assertTrue(alc.call_count == 1, 'should call add left child')
self.assertTrue(arc.call_count == 1, 'should call add right child')
def test_branch(self):
node = BaseNode(small_branch.lp, small_branch.integerIndices)
node.bound()
# check function calls
mock_pth = 'simple_mip_solver.nodes.base_node.BaseNode._most_fractional_index'
with patch(mock_pth, new_callable=PropertyMock) as mfi, \
patch.object(node, '_base_branch') as bb:
node.branch(junk='stuff') # should work with extra args
self.assertTrue(mfi.call_count == 1, 'should call most frac idx')
self.assertTrue(bb.call_count == 1, 'should call base branch')
# check returns
nodes = node.branch()
self.assertTrue(all(isinstance(n, BaseNode) for n in nodes.values()))
def test_process_branch_rtn(self):
bb = BranchAndBound(small_branch)
node = BaseNode(small_branch.lp, small_branch.integerIndices)
node.bound()
rtn = node.branch()
bb._process_branch_rtn(rtn)
# check attributes
self.assertTrue(isinstance(bb._node_queue.get(), BaseNode))
self.assertTrue(isinstance(bb._node_queue.get(), BaseNode))
self.assertTrue(bb._node_queue.empty())
# check function calls
bb = BranchAndBound(small_branch)
node = BaseNode(small_branch.lp, small_branch.integerIndices)
node.bound()
rtn = node.branch()
with patch.object(bb, '_process_rtn') as pr:
bb._process_branch_rtn(rtn)
self.assertTrue(pr.call_count == 1, 'should call rtn')
def test_branch(self):
node = BaseNode(small_branch.lp, small_branch.integerIndices, 0)
node.bound()
# check function calls
mock_pth = 'simple_mip_solver.nodes.base_node.BaseNode._most_fractional_index'
with patch(mock_pth, new_callable=PropertyMock) as mfi, \
patch.object(node, '_base_branch') as bb:
bb_rtn = {'left': 'mock', 'right': 'another_mock', 'next_node_idx': 3}
bb.return_value = bb_rtn
branch_rtn = node.branch(junk='stuff') # should work with extra args
self.assertTrue(mfi.call_count == 1, 'should call most frac idx')
self.assertTrue(bb.call_count == 1, 'should call base branch')
self.assertTrue(branch_rtn == bb_rtn)
class TestBranchAndBound(unittest.TestCase):
def setUp(self) -> None:
self.bb = BranchAndBound(small_branch)
self.unbounded_root = BaseNode(small_branch.lp,
small_branch.integerIndices)
self.bound_root = BaseNode(small_branch.lp,
small_branch.integerIndices)
self.bound_root.bound()
self.root_branch_rtn = self.bound_root.branch()
def test_init(self):
bb = BranchAndBound(small_branch)
self.assertTrue(isinstance(bb, Utils))
self.assertTrue(bb.global_upper_bound == float('inf'))
self.assertTrue(bb._node_queue.empty())
self.assertFalse(bb._unbounded)
self.assertFalse(bb._best_solution)
self.assertFalse(bb.solution)
self.assertTrue(bb.status == 'unsolved')
self.assertFalse(bb.objective_value)
self.assertTrue(isinstance(bb.tree, BranchAndBoundTree))
self.assertTrue(list(bb.tree.nodes.keys()) == [0])
self.assertTrue(bb.tree.nodes[0].attr['node'] is bb.root_node)
self.assertFalse(bb.solve_time, 'solve time should exist and be 0')
self.assertTrue(bb.mip_gap, 'mip gap should be an attribute')
def test_init_fails_asserts(self):
bb = BranchAndBound(small_branch)
queue = PriorityQueue()
# node_queue asserts
for func in reversed(bb._queue_funcs):
queue.__dict__[func] = 5
self.assertRaisesRegex(AssertionError,
f'node_queue needs a {func} function',
BranchAndBound, small_branch, BaseNode,
queue)
# node limit asserts
self.assertRaisesRegex(AssertionError,
f'node limit',
BranchAndBound,
model=small_branch,
node_limit=-5)
# mip gap asserts
self.assertRaisesRegex(AssertionError,
f'mip_gap',
BranchAndBound,
model=small_branch,
mip_gap=-5)
# kwargs asserts
self.assertRaisesRegex(AssertionError,
f'keys "right"',
BranchAndBound,
model=small_branch,
right=-5)
def test_current_gap(self):
bb = BranchAndBound(small_branch, node_limit=1)
bb.solve()
self.assertTrue(bb.current_gap is None)
bb.node_limit = 10
bb.solve()
self.assertTrue(bb.current_gap == .125)
bb.node_limit = float('inf')
bb.solve()
self.assertTrue(bb.current_gap == 0)
print()
def test_solve_stopped_on_iterations(self):
# check and make sure we're good with both nodes
for Node in [BaseNode, PCBDFSNode]:
bb = BranchAndBound(small_branch,
Node=Node,
node_limit=1,
pseudo_costs={})
bb.solve()
self.assertTrue(bb.status == 'stopped on iterations')
self.assertTrue(bb.solve_time)
def test_solve_optimal(self):
# check and make sure we're good with both nodes
for Node in [BaseNode, PCBDFSNode]:
bb = BranchAndBound(small_branch, Node=Node, pseudo_costs={})
bb.solve()
self.assertTrue(bb.status == 'optimal')
self.assertTrue(all(s.is_integer for s in bb.solution))
self.assertTrue(bb.objective_value == -2)
self.assertTrue(bb.solve_time)
def test_solve_infeasible(self):
# check and make sure we're good with both nodes
for Node in [BaseNode, PCBDFSNode]:
bb = BranchAndBound(infeasible, Node=Node, pseudo_costs={})
bb.solve()
self.assertTrue(bb.status == 'infeasible')
self.assertFalse(bb.solution)
self.assertTrue(bb.objective_value == float('inf'))
self.assertTrue(bb.solve_time)
def test_solve_unbounded(self):
# check and make sure we're good with both nodes
for Node in [BaseNode, PCBDFSNode]:
bb = BranchAndBound(unbounded, Node=Node, pseudo_costs={})
bb.solve()
self.assertTrue(bb.status == 'unbounded')
self.assertTrue(bb.solve_time)
# check we quit even if node_queue nonempty
with patch.object(bb, '_evaluate_node') as en:
bb = BranchAndBound(unbounded)
bb._unbounded = True
bb.solve()
self.assertFalse(en.called)
def test_solve_past_node_limit(self):
bb = BranchAndBound(unbounded, node_limit=10)
# check we quit even if node_queue nonempty
with patch.object(bb, '_evaluate_node') as en:
bb.evaluated_nodes = 10
bb.solve()
self.assertFalse(en.called, 'were past the node limit')
def test_evaluate_node_infeasible(self):
bb = BranchAndBound(infeasible)
bb._evaluate_node(bb.root_node)
# check attributes
self.assertTrue(bb._node_queue.empty(),
'inf model should create no nodes')
self.assertFalse(bb._best_solution, 'best solution should not change')
self.assertTrue(bb.global_upper_bound == float('inf'),
'shouldnt change')
self.assertTrue(bb.global_lower_bound == float('inf'),
'shouldnt change')
self.assertTrue(bb.evaluated_nodes == 1,
'only one node should be evaluated')
# check function calls - recycle object since it has attrs already set
with patch.object(bb, '_process_rtn') as pr, \
patch.object(bb, '_process_branch_rtn') as pbr, \
patch.object(bb.root_node, 'bound') as bd, \
patch.object(bb.root_node, 'branch') as bh:
bb._evaluate_node(bb.root_node)
self.assertTrue(pr.call_count == 1)
self.assertTrue(pbr.call_count == 0)
self.assertTrue(bd.call_count == 1)
self.assertTrue(bh.call_count == 0)
def test_evaluate_node_fractional(self):
bb = BranchAndBound(small_branch,
Node=PCBDFSNode,
pseudo_costs={},
strong_branch_iters=5)
bb._evaluate_node(bb.root_node)
# check attributes
self.assertFalse(bb._best_solution, 'best solution should not change')
self.assertTrue(bb.global_upper_bound == float('inf'),
'shouldnt change')
self.assertTrue(bb.global_lower_bound > -float('inf'), 'should change')
self.assertTrue(bb._node_queue.qsize() == 2,
'should branch and add two nodes')
self.assertTrue(bb._kwargs['pseudo_costs'], 'something should be set')
self.assertTrue(bb._kwargs['strong_branch_iters'],
'something should be set')
self.assertTrue(bb.evaluated_nodes == 1,
'only one node should be evaluated')
# check function calls - recycle object since it has attrs already set
with patch.object(bb, '_process_rtn') as pr, \
patch.object(bb, '_process_branch_rtn') as pbr, \
patch.object(bb.root_node, 'bound') as bd, \
patch.object(bb.root_node, 'branch') as bh:
bb._evaluate_node(bb.root_node)
self.assertTrue(pr.call_count == 1) # direct calls
self.assertTrue(pbr.call_count == 1)
self.assertTrue(0 == pbr.call_args.args[0],
'root node id should be first call arg')
self.assertTrue(bd.call_count == 1)
self.assertTrue(bh.call_count == 1)
def test_evaluate_node_integer(self):
bb = BranchAndBound(no_branch)
bb._evaluate_node(bb.root_node)
# check attributes
self.assertTrue(all(bb._best_solution == [1, 1, 0]))
self.assertTrue(bb.global_upper_bound == -2)
self.assertTrue(bb.global_lower_bound == -2,
'should match upper bound when optimal')
self.assertTrue(bb._node_queue.empty(),
'immediately optimal model should create no nodes')
self.assertTrue(bb.evaluated_nodes == 1,
'only one node should be evaluated')
# check function calls - recycle object since it has attrs already set
with patch.object(bb, '_process_rtn') as pr, \
patch.object(bb, '_process_branch_rtn') as pbr, \
patch.object(bb.root_node, 'bound') as bd, \
patch.object(bb.root_node, 'branch') as bh:
bb._evaluate_node(bb.root_node)
self.assertTrue(pr.call_count == 1)
self.assertTrue(pbr.call_count == 0)
self.assertTrue(bd.call_count == 1)
self.assertTrue(bh.call_count == 0)
def test_evaluate_node_unbounded(self):
bb = BranchAndBound(unbounded)
bb._evaluate_node(bb.root_node)
# check attributes
self.assertTrue(bb._unbounded)
self.assertTrue(bb.evaluated_nodes == 1,
'only one node should be evaluated')
def test_evaluate_node_properly_prunes(self):
bb = BranchAndBound(no_branch)
bb.global_upper_bound = -2
called_node = BaseNode(bb.model.lp,
bb.model.integerIndices,
lower_bound=-4)
pruned_node = BaseNode(bb.model.lp,
bb.model.integerIndices,
lower_bound=0)
with patch.object(called_node, 'bound') as cnb, \
patch.object(pruned_node, 'bound') as pnb:
cnb.return_value = {}
pnb.return_value = {}
bb._node_queue.put(called_node)
bb._node_queue.put(pruned_node)
bb._evaluate_node(bb._node_queue.get())
bb._evaluate_node(bb._node_queue.get())
self.assertTrue(cnb.call_count == 1, 'first node should run')
self.assertFalse(pnb.call_count, 'second node should get pruned')
self.assertTrue(bb._node_queue.empty())
self.assertTrue(
bb.evaluated_nodes == 1,
'only one node should be evaluated since other pruned')
def test_process_branch_rtn_fails_asserts(self):
self.assertRaisesRegex(AssertionError, 'rtn must be a dictionary',
self.bb._process_branch_rtn, 0, 'fish')
rtn = {'right': 5, 'left': 5}
self.assertRaisesRegex(AssertionError, 'must be int',
self.bb._process_branch_rtn, '0', rtn)
self.assertRaisesRegex(AssertionError, 'must already exist in tree',
self.bb._process_branch_rtn, 1, rtn)
self.assertRaisesRegex(AssertionError, 'value must be type',
self.bb._process_branch_rtn, 0, rtn)
del rtn['left']
self.assertRaisesRegex(AssertionError, 'must be in the returned',
self.bb._process_branch_rtn, 0, rtn)
self.root_branch_rtn['right'].idx = 0
# test node index against rest of tree
rtn = self.bound_root.branch(next_node_idx=1)
rtn['left'].idx = 0
self.assertRaisesRegex(AssertionError, 'give unique node ID',
self.bb._process_branch_rtn, 0, rtn)
def test_process_branch_rtn(self):
bb = BranchAndBound(small_branch)
node = BaseNode(small_branch.lp, small_branch.integerIndices, idx=0)
node.bound()
rtn = node.branch(next_node_idx=1)
left_node = rtn['left']
right_node = rtn['right']
bb._process_branch_rtn(node.idx, rtn)
# check attributes
self.assertTrue(isinstance(bb._node_queue.get(), BaseNode))
self.assertTrue(isinstance(bb._node_queue.get(), BaseNode))
self.assertTrue(bb._node_queue.empty())
children = bb.tree.get_children(node.idx)
self.assertTrue(len(children) == 2, 'there should be two kids created')
for child in children:
self.assertFalse(bb.tree.get_children(child),
'children shouldnt have kids')
self.assertTrue(bb.tree.get_node(1).attr['node'] is left_node)
self.assertTrue(bb.tree.get_node(2).attr['node'] is right_node)
# check function calls
bb = BranchAndBound(small_branch)
node = BaseNode(small_branch.lp, small_branch.integerIndices, 0)
node.bound()
rtn = node.branch()
with patch.object(bb, '_process_rtn') as pr, \
patch.object(bb.tree, 'add_left_child') as alc, \
patch.object(bb.tree, 'add_right_child') as arc:
bb._process_branch_rtn(0, rtn)
self.assertTrue(pr.call_count == 1, 'should call process rtn')
self.assertTrue(alc.call_count == 1, 'should call add left child')
self.assertTrue(arc.call_count == 1, 'should call add right child')
def test_dual_bound_fails_asserts(self):
bb = BranchAndBound(small_branch)
self.assertRaisesRegex(AssertionError,
'must solve this instance before',
bb.dual_bound, CyLPArray([2.5, 4.5]))
bb.solve()
self.assertRaisesRegex(AssertionError, 'only works with CyLP arrays',
bb.dual_bound, np.array([2.5, 4.5]))
self.assertRaisesRegex(AssertionError,
'shape of the RHS being added should match',
bb.dual_bound, CyLPArray([4.5]))
bb = BranchAndBound(infeasible2)
bb.root_node.lp += np.matrix(
[[0, -1, -1]]) * bb.root_node.lp.getVarByName('x') >= CyLPArray(
[-2.5])
bb.solve()
self.assertRaisesRegex(
AssertionError,
'feature expects the root node to have a single constraint object',
bb.dual_bound, CyLPArray([2.5, 4.5]))
def test_dual_bound(self):
# Ensure that BranchAndBound.dual_bound generates the dual function
# that we saw in ISE 418 HW 3 problem 1
bb = BranchAndBound(h3p1)
bb.solve()
bound = bb.dual_bound(CyLPArray([3.5, -3.5]))
self.assertTrue(bb.objective_value == bound,
'dual should be strong at original rhs')
prob = {
0: h3p1_0,
1: h3p1_1,
2: h3p1_2,
3: h3p1_3,
4: h3p1_4,
5: h3p1_5
}
sol_new = {0: 0, 1: 1, 2: 1, 3: 2, 4: 2, 5: 3}
sol_bound = {0: 0, 1: .5, 2: 1, 3: 2, 4: 2, 5: 2.5}
for beta in range(6):
new_bb = BranchAndBound(prob[beta])
new_bb.solve()
bound = bb.dual_bound(CyLPArray(np.array([beta, -beta])))
self.assertTrue(
isclose(sol_new[beta], new_bb.objective_value, abs_tol=.01),
'new branch and bound objective should match expected')
self.assertTrue(isclose(sol_bound[beta], bound),
'new dual bound value should match expected')
self.assertTrue(
bound <= new_bb.objective_value + .01,
'dual bound value should be at most the value function for this rhs'
)
bb = BranchAndBound(small_branch)
bb.solve()
bound = bb.dual_bound(CyLPArray([2.5, 4.5]))
# just make sure the dual bound works here too
self.assertTrue(
bound <= -5.99,
'dual bound value should be at most the value function for this rhs'
)
# check function calls
bb = BranchAndBound(small_branch)
bb.solve()
bound_duals = [
bb._bound_dual(n.lp)
for n in bb.tree.get_node_instances([6, 12, 10, 8, 2])
]
with patch.object(bb, '_bound_dual') as bd:
bd.side_effect = bound_duals
bound = bb.dual_bound(CyLPArray([3, 3]))
self.assertTrue(bd.call_count == 5)
bb = BranchAndBound(small_branch)
bb.solve()
bound = bb.dual_bound(CyLPArray([3, 3]))
with patch.object(bb, '_bound_dual') as bd:
bound = bb.dual_bound(CyLPArray([1, 1]))
self.assertFalse(bd.called)
@unittest.skipIf(skip_longs, "debugging")
def test_dual_bound_many_times(self):
pattern = re.compile('evaluation_(\d+).mps')
fldr_pth = os.path.join(
os.path.dirname(os.path.abspath(inspect.getfile(example_models))),
'example_value_functions')
for count, sub_fldr in enumerate(os.listdir(fldr_pth)):
print(f'dual bound {count}')
sub_fldr_pth = os.path.join(fldr_pth, sub_fldr)
evals = {}
for file in os.listdir(sub_fldr_pth):
eval_num = int(pattern.search(file).group(1))
instance = MILPInstance(
file_name=os.path.join(sub_fldr_pth, file))
bb = BranchAndBound(instance,
PseudoCostBranchNode,
pseudo_costs={})
bb.solve()
evals[eval_num] = bb
instance_0 = evals[0]
for bb in evals.values():
# all problems were given as <=, so their constraints were flipped by default
self.assertTrue(
instance_0.dual_bound(CyLPArray(-bb.model.b)) <=
bb.objective_value + .01, 'dual_bound should be less')
def test_bound_dual(self):
bb = BranchAndBound(infeasible2)
bb.root_node.lp += np.matrix(
[[0, -1, -1]]) * bb.root_node.lp.getVarByName('x') >= CyLPArray(
[-2.5])
bb.solve()
terminal_nodes = bb.tree.get_leaves(0)
infeasible_nodes = [
n for n in terminal_nodes if n.lp_feasible is False
]
n = infeasible_nodes[0]
lp = bb._bound_dual(n.lp)
# test that we get a CyClpSimplex object back
self.assertTrue(isinstance(lp, CyClpSimplex),
'should return CyClpSimplex instance')
# same variables plus extra 's'
self.assertTrue(
{v.name
for v in lp.variables} == {'x', 's_0', 's_1'},
'x should already exist and s_1 and s_2 should be added')
old_x = n.lp.getVarByName('x')
new_x, s_0, s_1 = lp.getVarByName('x'), lp.getVarByName(
's_0'), lp.getVarByName('s_1')
# same variable bounds, plus s >= 0
self.assertTrue(
all(new_x.lower == old_x.lower)
and all(new_x.upper == old_x.upper),
'x should have the same bounds')
self.assertTrue(
all(s_0.lower == [0, 0]) and all(s_0.upper > [1e300, 1e300]),
's_0 >= 0')
self.assertTrue(
all(s_1.lower == [0]) and all(s_1.upper > 1e300), 's_1 >= 0')
# same constraints, plus slack s
self.assertTrue(lp.nConstraints == 3,
'should have same number of constraints')
self.assertTrue(
(lp.constraints[0].varCoefs[new_x] == np.array([[-1, -1, 0],
[0, 0,
-1]])).all(),
'x coefs should stay same')
self.assertTrue((lp.constraints[0].varCoefs[s_0] == np.matrix(
np.identity(2))).all(), 's_0 should have coef of 2-D identity')
self.assertTrue(
all(lp.constraints[1].varCoefs[new_x] == np.array([0, -1, -1])),
'x coefs should stay same')
self.assertTrue(
lp.constraints[1].varCoefs[s_1] == np.matrix(np.identity(1)),
's_0 should have coef of 1-D identity')
self.assertTrue(
all(lp.constraints[0].lower == np.array([1, -1]))
and all(lp.constraints[0].upper >= np.array([1e300])),
'constraint bounds should remain same')
self.assertTrue(
lp.constraints[1].lower == np.array([-2.5])
and lp.constraints[1].upper >= np.array([1e300]),
'constraint bounds should remain same')
# same objective, plus large s coefficient
self.assertTrue(
all(lp.objective == np.array([-1, -1, 0, bb._M, bb._M, bb._M])))
# problem is now feasible
self.assertTrue(lp.getStatusCode() == 0, 'lp should now be optimal')
def test_bound_dual_fails_asserts(self):
bb = BranchAndBound(infeasible)
bb.solve()
terminal_nodes = bb.tree.get_leaves(0)
infeasible_nodes = [
n for n in terminal_nodes if n.lp_feasible is False
]
n = infeasible_nodes[0]
n.lp.addVariable('s_0', 1)
self.assertRaisesRegex(AssertionError,
"variable 's_0' is a reserved name",
bb._bound_dual, n.lp)
self.assertRaisesRegex(AssertionError,
"must give CyClpSimplex instance",
bb._bound_dual, n)
def test_find_strong_disjunctive_cut_fails_asserts(self):
bb = BranchAndBound(small_branch)
bb.solve()
self.assertRaisesRegex(AssertionError,
'parent must already exist in tree',
bb.find_strong_disjunctive_cut, 50)
terminal_nodes = bb.tree.get_leaves(0)
disjunctive_nodes = [
n for n in terminal_nodes if n.lp_feasible is not False
]
n = disjunctive_nodes[0]
n.lp.addVariable('d', 3)
self.assertRaisesRegex(
AssertionError,
'Each disjunctive term should have the same variables',
bb.find_strong_disjunctive_cut, 0)
# todo: get confirmation on bound matching
def test_find_strong_disjunctive_cut(self):
bb = BranchAndBound(square)
bb.solve()
pi, pi0 = bb.find_strong_disjunctive_cut(0)
# check cut is what we expect, i.e. x1 <= 1
assert_allclose(pi / pi0, np.array([1, 0]), atol=.01)
self.assertTrue((pi - .01 < 0).all())
self.assertTrue(pi0 - .01 < 0)
# check we get same bound
A = np.append(bb.root_node.lp.coefMatrix.toarray(), [pi], axis=0)
b = np.append(bb.root_node.lp.constraintsLower, pi0, axis=0)
warm_model = MILPInstance(A=A,
b=b,
c=bb.root_node.lp.objective,
l=bb.root_node.lp.variablesLower.copy(),
u=bb.root_node.lp.variablesUpper.copy(),
sense=['Min', '>='],
integerIndices=bb.root_node._integer_indices,
numVars=bb.root_node.lp.nVariables)
warm_bb = BranchAndBound(warm_model)
warm_bb.solve()
# self.assertTrue(bb.global_lower_bound == warm_bb.root_node.objective_value)
# try another problem
bb = BranchAndBound(small_branch, node_limit=10)
bb.solve()
pi, pi0 = bb.find_strong_disjunctive_cut(0)
# check cut is what we expect, i.e. x3 <= 1
assert_allclose(pi / pi0, np.array([0, 0, 1]), atol=.01)
self.assertTrue((pi - .01 < 0).all())
self.assertTrue(pi0 - .01 < 0)
# check we get same bound
A = np.append(bb.root_node.lp.coefMatrix.toarray(), [pi], axis=0)
b = np.append(bb.root_node.lp.constraintsLower, pi0, axis=0)
warm_model = MILPInstance(A=A,
b=b,
c=bb.root_node.lp.objective,
l=bb.root_node.lp.variablesLower.copy(),
u=bb.root_node.lp.variablesUpper.copy(),
sense=['Min', '>='],
integerIndices=bb.root_node._integer_indices,
numVars=bb.root_node.lp.nVariables)
warm_bb = BranchAndBound(warm_model)
warm_bb.solve()
# self.assertTrue(bb.global_lower_bound == warm_bb.root_node.objective_value)
@unittest.skipIf(skip_longs, "debugging")
def test_find_strong_disjunctive_cut_many_times(self):
fldr = os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(generate_random_variety))),
'example_models')
for i, file in enumerate(os.listdir(fldr)):
if i >= 10:
break
print(f'running test {i + 1}')
pth = os.path.join(fldr, file)
model = MILPInstance(file_name=pth)
bb = BranchAndBound(model)
bb.solve()
pi, pi0 = bb.find_strong_disjunctive_cut(0)
# ensure we cut off the root solution
self.assertTrue(sum(pi * bb.root_node.solution) <= pi0)
# ensure we don't cut off disjunctive mins
for n in bb.tree.get_leaves(0):
if n.lp_feasible:
self.assertTrue(sum(pi * n.solution) >= pi0 - .01)
def setUpClass(cls) -> None:
node = BaseNode(small_branch.lp, small_branch.integerIndices)
node.bound()
cls.children = node.branch()