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BnB: Add ability to specify integers #17

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5870062
Add ability to specify integers
tuncbkose Jun 20, 2024
c1877c2
Refactor integrality implementation
tuncbkose Jul 18, 2024
ea03283
Fix existing tests broken by integrality implementation
tuncbkose Jul 18, 2024
f576396
Fix bnb_visual
tuncbkose Jul 19, 2024
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33 changes: 29 additions & 4 deletions gilp/simplex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -629,9 +629,10 @@ def branch_and_bound_iteration(lp: LP,
best_bound: float,
manual: bool = False,
feas_tol: float = 1e-7,
int_feas_tol: float = 1e-7
int_feas_tol: float = 1e-7,
int_mask: np.ndarray = None
) -> Tuple[bool, np.ndarray, float, LP, LP]:
"""Exectue one iteration of branch and bound on the given node.
"""Execute one iteration of branch and bound on the given node.

Execute one iteration of branch and bound on the given node (LP). Update
the current incumbent and best bound if needed. Use the given primal
Expand All @@ -641,6 +642,7 @@ def branch_and_bound_iteration(lp: LP,
lp (LP): Branch and bound node.
incumbent (np.ndarray): Current incumbent solution.
best_bound (float): Current best bound.
int_mask (Union[np.ndarray, List, Tuple]): A binary mask of length lp.n where 1 indicates the corresponding decision variable is an int.
manual (bool): True if the user can choose the variable to branch on.
feas_tol (float): Primal feasibility tolerance (1e-7 default).
int_feas_tol (float): Integer feasibility tolerance (1e-7 default).
Expand Down Expand Up @@ -671,6 +673,17 @@ def branch_and_bound_iteration(lp: LP,
best_bound=best_bound, left_LP=None, right_LP=None)
else:
frac_comp = ~np.isclose(x, np.round(x), atol=int_feas_tol)[:lp.n]

mask = None
# No mask, all decision variables are integers
if int_mask is None:
mask = np.ones((lp.n,1), dtype=bool)
else:
# make sure to ignore slack variables that were added lated
mask = np.zeros((lp.n, 1), dtype=bool)
mask[:len(int_mask)] = int_mask
frac_comp &= mask # only consider variables that are integers

if np.sum(frac_comp) > 0:
pos_i = np.nonzero(frac_comp)[0] # list of indices to branch on
if manual:
Expand Down Expand Up @@ -709,19 +722,22 @@ def create_branch(lp, i, bound, branch):


def branch_and_bound(lp: LP,
int_mask: Union[np.ndarray, List, Tuple] = None,
manual: bool = False,
feas_tol: float = 1e-7,
int_feas_tol: float = 1e-7
) -> Tuple[np.ndarray, float]:
"""Execute branch and bound on the given LP.

Execute branch and bound on the given LP assuming that all decision
variables must be integer. Use a primal feasibility tolerance of feas_tol
Execute branch and bound on the given LP. If int_mask is not given,
all decision variables are assumed to be integer.
Use a primal feasibility tolerance of feas_tol
(with default vlaue of 1e-7) and an integer feasibility tolerance of
int_feas_tol (with default vlaue of 1e-7).

Args:
lp (LP): LP on which to run the branch and bound algorithm.
int_mask (Union[np.ndarray, List, Tuple]): A binary mask of length lp.n where 1 indicates the corresponding decision variable is an int.
manual (bool): True if the user can choose the variable to branch on.
feas_tol (float): Primal feasibility tolerance (1e-7 default).
int_feas_tol (float): Integer feasibility tolerance (1e-7 default).
Expand All @@ -736,11 +752,20 @@ def branch_and_bound(lp: LP,
best_bound = None
unexplored = [lp]

# No mask, all decision variables are integers
if int_mask is None:
int_mask = np.ones((lp.n,1), dtype=bool)
else:
int_mask = _validate(vector=_vectorize(int_mask), sizes=lp.n, name='int_mask') # convert to expected shape
assert ((int_mask==0) | (int_mask==1)).all(), "int_mask is should be binary"
int_mask = int_mask.astype(bool)

while len(unexplored) > 0:
sub = unexplored.pop()
iteration = branch_and_bound_iteration(lp=sub,
incumbent=incumbent,
best_bound=best_bound,
int_mask=int_mask,
manual=manual,
feas_tol=feas_tol,
int_feas_tol=int_feas_tol)
Expand Down