def _kernighan_lin_sweep(edges, side):
"""
This is a modified form of Kernighan-Lin, which moves single nodes at a
time, alternating between sides to keep the bisection balanced. We keep
two min-heaps of swap costs to make optimal-next-move selection fast.
"""
costs0, costs1 = costs = BinaryHeap(), BinaryHeap()
for u, side_u, edges_u in zip(count(), side, edges):
cost_u = sum(w if side[v] else -w for v, w in edges_u)
costs[side_u].insert(u, cost_u if side_u else -cost_u)
def _update_costs(costs_x, x):
for y, w in edges[x]:
costs_y = costs[side[y]]
cost_y = costs_y.get(y)
if cost_y is not None:
cost_y += 2 * (-w if costs_x is costs_y else w)
costs_y.insert(y, cost_y, True)
i = totcost = 0
while costs0 and costs1:
u, cost_u = costs0.pop()
_update_costs(costs0, u)
v, cost_v = costs1.pop()
_update_costs(costs1, v)
totcost += cost_u + cost_v
yield totcost, i, (u, v)
def _kernighan_lin_sweep(edges, side):
costs0, costs1 = costs = BinaryHeap(), BinaryHeap()
for u, side_u, edges_u in zip(count(), side, edges):
cost_u = sum(w if side[v] else -w for v, w in edges_u)
costs[side_u].insert(u, cost_u if side_u else -cost_u)
def _update_costs(costs_x, x):
for y, w in edges[x]:
costs_y = costs[side[y]]
cost_y = costs_y.get(y)
if cost_y is not None:
cost_y += 2 * (-w if costs_x is costs_y else w)
costs_y.insert(y, cost_y, True)
i = totcost = 0
while costs0 and costs1:
u, cost_u = costs0.pop()
_update_costs(costs0, u)
v, cost_v = costs1.pop()
_update_costs(costs1, v)
totcost += cost_u + cost_v
yield totcost, i, (u, v)
i += 1