def generate_constrs(self, model: Model):
G = nx.DiGraph()
r = [(v, v.x) for v in model.vars if v.name.startswith('x(')]
U = [v.name.split('(')[1].split(',')[0] for v, f in r]
V = [v.name.split(')')[0].split(',')[1] for v, f in r]
N = list(set(U + V))
cp = CutPool()
for i in range(len(U)):
G.add_edge(U[i], V[i], capacity=r[i][1])
for (u, v) in product(N, N):
if u == v:
continue
val, (S, NS) = nx.minimum_cut(G, u, v)
if val <= 0.99:
arcsInS = [(v, f) for i, (v, f) in enumerate(r)
if U[i] in S and V[i] in S]
if sum(f for v, f in arcsInS) >= (len(S) - 1) + 1e-4:
cut = xsum(1.0 * v for v, fm in arcsInS) <= len(S) - 1
cp.add(cut)
if len(cp.cuts) > 256:
for cut in cp.cuts:
model.add_cut(cut)
return
for cut in cp.cuts:
model.add_cut(cut)
return
def generate_cuts(self, model: Model):
G = nx.DiGraph()
r = [(v, v.x) for v in model.vars if v.name.startswith('x(')]
U = [int(v.name.split('(')[1].split(',')[0]) for v, f in r]
V = [int(v.name.split(')')[0].split(',')[1]) for v, f in r]
for v in model.vars:
print('{} = {} '.format(v.name, v.x), end='')
print()
input()
cp = CutPool()
for i in range(len(U)):
G.add_edge(U[i], V[i], capacity=r[i][1])
for (u, v) in F:
if u not in U or v not in V:
continue
val, (S, NS) = nx.minimum_cut(G, u, v)
if val <= 0.99:
arcsInS = [(v, f) for i, (v, f) in enumerate(r)
if U[i] in S and V[i] in S]
if sum(f for v, f in arcsInS) >= (len(S) - 1) + 1e-4:
cut = xsum(1.0 * v for v, fm in arcsInS) <= len(S) - 1
cp.add(cut)
if len(cp.cuts) > 256:
for cut in cp.cuts:
model.add_cut(cut)
return
for cut in cp.cuts:
model.add_cut(cut)
return
def generate_cuts(self, model: Model):
def row(vname: str) -> str:
return int(vname.split('(')[1].split(',')[0].split(')')[0])
def col(vname: str) -> str:
return int(vname.split('(')[1].split(',')[1].split(')')[0])
x = {(row(v.name), col(v.name)): v for v in model.vars}
for p, k in enumerate(range(2 - n, n - 2 + 1)):
cut = xsum(x[i, j] for i in range(n)
for j in range(n) if i - j == k) <= 1
if cut.violation > 0.001:
model.add_cut(cut)
for p, k in enumerate(range(3, n + n)):
cut = xsum(x[i, j] for i in range(n)
for j in range(n) if i + j == k) <= 1
if cut.violation > 0.001:
model.add_cut(cut)
