def _generate_model(self,
zvars,
Ws,
ws,
b,
bounds=None,
binary=False,
labels=None,
prefix=""):
"""Generate an arc-flow model."""
from pyvpsolver import MVP, AFG
ndims = len(Ws[0])
m = len(ws)
bb = [0] * m
bvars = []
maxW = [max(Wi[d] for Wi in Ws) for d in range(ndims)]
for i in range(m):
if isinstance(b[i], six.string_types):
minw = [min(wi[d] for wi in ws[i]) for d in range(ndims)]
bb[i] = min(maxW[d] // minw[d] for d in range(ndims)
if minw[d] != 0)
if bounds is not None:
bb[i] = min(bb[i], bounds[i])
bvars.append(b[i])
else:
bb[i] = b[i]
Cs = [1] * len(Ws)
Qs = [-1] * len(Ws)
instance = MVP(Ws, Cs, Qs, ws, bb, binary=binary, verbose=False)
graph = AFG(instance, verbose=Tools.VERBOSE).graph()
vnames = {(T, graph.S, graph.LOSS): zvar
for zvar, T in zip(zvars, graph.Ts)}
ub = {}
varl, cons = graph.get_flow_cons(vnames)
assocs = graph.get_assocs(vnames)
graph.names = vnames
nopts = [len(ws[i]) for i in range(m)]
if labels is None:
names = ["i={}".format(i + 1) for i in range(m)]
for i in range(m):
if isinstance(b[i], six.string_types):
names[i] = b[i]
labels = {(i, j): "{} opt={}".format(names[i], j + 1)
for i, j in instance.labels}
elif isinstance(labels, list):
labels = dict(enumerate(labels))
graph.set_labels({(u, v, lbl): [labels[lbl]]
for (u, v, lbl) in graph.A if lbl in labels})
for i in range(m):
lincomb = []
for j in range(nopts[i]):
lincomb += assocs.get((i, j), [])
if bounds is not None:
for var in lincomb:
ub[var] = bounds[i]
if isinstance(b[i], six.string_types):
varl.append(b[i])
cons.append((lincomb, "=", b[i]))
else:
if b[i] > 1:
cons.append((lincomb, ">=", b[i]))
else:
cons.append((lincomb, "=", b[i]))
model = Model()
for var in varl:
model.add_var(name=var, lb=0, ub=ub.get(var, None), vtype="I")
for lincomb, sign, rhs in cons:
model.add_con(lincomb, sign, rhs)
for i, zvar in enumerate(zvars):
flowvar = "_total_flow_{}".format(i)
model.add_var(name=flowvar, vtype="I")
model.add_con(flowvar, "=", zvar)
declared_vars = set(bvars)
def var_name(name):
if name in zvars:
return name
elif name in declared_vars:
return name
else:
return prefix + name
def con_name(name):
return prefix + name
model.rename_vars(var_name)
model.rename_cons(con_name)
return graph, model, declared_vars
def _generate_model(self, zvars, Ws, ws, b, bounds=None, binary=False,
labels=None, prefix=""):
"""Generate an arc-flow model."""
from pyvpsolver import MVP, AFG
ndims = len(Ws[0])
m = len(ws)
bb = [0]*m
bvars = []
maxW = [max(Wi[d] for Wi in Ws) for d in range(ndims)]
for i in range(m):
if isinstance(b[i], six.string_types):
minw = [min(wi[d] for wi in ws[i]) for d in range(ndims)]
bb[i] = min(
maxW[d]//minw[d] for d in range(ndims) if minw[d] != 0
)
if bounds is not None:
bb[i] = min(bb[i], bounds[i])
bvars.append(b[i])
else:
bb[i] = b[i]
Cs = [1]*len(Ws)
Qs = [-1]*len(Ws)
instance = MVP(Ws, Cs, Qs, ws, bb, binary=binary, verbose=False)
graph = AFG(instance, verbose=Tools.VERBOSE).graph()
vnames = {
(T, graph.S, graph.LOSS): zvar
for zvar, T in zip(zvars, graph.Ts)
}
ub = {}
varl, cons = graph.get_flow_cons(vnames)
assocs = graph.get_assocs(vnames)
graph.names = vnames
nopts = [len(ws[i]) for i in range(m)]
if labels is None:
names = ["i={}".format(i+1) for i in range(m)]
for i in range(m):
if isinstance(b[i], six.string_types):
names[i] = b[i]
labels = {
(i, j): "{} opt={}".format(names[i], j+1)
for i, j in instance.labels
}
elif isinstance(labels, list):
labels = dict(enumerate(labels))
graph.set_labels({
(u, v, lbl): [labels[lbl]]
for (u, v, lbl) in graph.A
if lbl in labels
})
for i in range(m):
lincomb = []
for j in range(nopts[i]):
lincomb += assocs.get((i, j), [])
if bounds is not None:
for var in lincomb:
ub[var] = bounds[i]
if isinstance(b[i], six.string_types):
varl.append(b[i])
cons.append((lincomb, "=", b[i]))
else:
if b[i] > 1:
cons.append((lincomb, ">=", b[i]))
else:
cons.append((lincomb, "=", b[i]))
model = Model()
for var in varl:
model.add_var(name=var, lb=0, ub=ub.get(var, None), vtype="I")
for lincomb, sign, rhs in cons:
model.add_con(lincomb, sign, rhs)
for i, zvar in enumerate(zvars):
flowvar = "_total_flow_{}".format(i)
model.add_var(name=flowvar, vtype="I")
model.add_con(flowvar, "=", zvar)
declared_vars = set(bvars)
def var_name(name):
if name in zvars:
return name
elif name in declared_vars:
return name
else:
return prefix+name
def con_name(name):
return prefix+name
model.rename_vars(var_name)
model.rename_cons(con_name)
return graph, model, declared_vars
def _generate_model(self,
zvar,
W,
w,
b,
bounds=None,
binary=False,
labels=None,
prefix=""):
"""Generate an arc-flow model."""
from pyvpsolver import VBP, AFG
m = len(w)
bb = [0] * m
bvars = []
for i in range(m):
if isinstance(b[i], six.string_types):
bb[i] = min(W[d] // w[i][d] for d in range(len(w[i]))
if w[i][d] != 0)
if bounds is not None:
bb[i] = min(bb[i], bounds[i])
bvars.append(b[i])
else:
bb[i] = b[i]
instance = VBP(W, w, bb, binary=binary, verbose=False)
graph = AFG(instance, verbose=Tools.VERBOSE).graph()
feedback = (graph.Ts[0], graph.S, graph.LOSS)
vnames = {}
vnames[feedback] = zvar
ub = {}
varl, cons = graph.get_flow_cons(vnames)
assocs = graph.get_assocs(vnames)
graph.names = vnames
if labels is None:
labels = {i: "i={0}".format(i + 1) for i in instance.labels}
for i in range(m):
if isinstance(b[i], six.string_types):
labels[i] = b[i]
elif isinstance(labels, list):
labels = dict(enumerate(labels))
graph.set_labels({(u, v, i): [labels[i]]
for (u, v, i) in graph.A if i in labels})
for i in range(m):
if i not in assocs:
assocs[i] = []
if bounds is not None:
for var in assocs[i]:
ub[var] = bounds[i]
if isinstance(b[i], six.string_types):
varl.append(b[i])
cons.append((assocs[i], "=", b[i]))
else:
if b[i] > 1:
cons.append((assocs[i], ">=", b[i]))
else:
cons.append((assocs[i], "=", b[i]))
model = Model()
for var in varl:
model.add_var(name=var, lb=0, ub=ub.get(var, None), vtype="I")
for lincomb, sign, rhs in cons:
model.add_con(lincomb, sign, rhs)
model.add_var(name="_total_flow", vtype="I")
model.add_con("_total_flow", "=", zvar)
declared_vars = set(bvars)
def var_name(name):
if name == zvar:
return name
elif name in declared_vars:
return name
else:
return prefix + name
def con_name(name):
return prefix + name
model.rename_vars(var_name)
model.rename_cons(con_name)
return graph, model, declared_vars
def _generate_model(self, zvar, W, w, b, bounds=None, binary=False,
labels=None, prefix=""):
"""Generate an arc-flow model."""
from pyvpsolver import VBP, AFG
m = len(w)
bb = [0]*m
bvars = []
for i in range(m):
if isinstance(b[i], six.string_types):
bb[i] = min(
W[d]//w[i][d] for d in range(len(w[i])) if w[i][d] != 0
)
if bounds is not None:
bb[i] = min(bb[i], bounds[i])
bvars.append(b[i])
else:
bb[i] = b[i]
instance = VBP(W, w, bb, binary=binary, verbose=False)
graph = AFG(instance, verbose=Tools.VERBOSE).graph()
feedback = (graph.Ts[0], graph.S, graph.LOSS)
vnames = {}
vnames[feedback] = zvar
ub = {}
varl, cons = graph.get_flow_cons(vnames)
assocs = graph.get_assocs(vnames)
graph.names = vnames
if labels is None:
labels = {i: "i={0}".format(i+1) for i in instance.labels}
for i in range(m):
if isinstance(b[i], six.string_types):
labels[i] = b[i]
elif isinstance(labels, list):
labels = dict(enumerate(labels))
graph.set_labels({
(u, v, i): [labels[i]]
for (u, v, i) in graph.A
if i in labels
})
for i in range(m):
if i not in assocs:
assocs[i] = []
if bounds is not None:
for var in assocs[i]:
ub[var] = bounds[i]
if isinstance(b[i], six.string_types):
varl.append(b[i])
cons.append((assocs[i], "=", b[i]))
else:
if b[i] > 1:
cons.append((assocs[i], ">=", b[i]))
else:
cons.append((assocs[i], "=", b[i]))
model = Model()
for var in varl:
model.add_var(name=var, lb=0, ub=ub.get(var, None), vtype="I")
for lincomb, sign, rhs in cons:
model.add_con(lincomb, sign, rhs)
model.add_var(name="_total_flow", vtype="I")
model.add_con("_total_flow", "=", zvar)
declared_vars = set(bvars)
def var_name(name):
if name == zvar:
return name
elif name in declared_vars:
return name
else:
return prefix+name
def con_name(name):
return prefix+name
model.rename_vars(var_name)
model.rename_cons(con_name)
return graph, model, declared_vars
