def constr_get_expr(self, constr: Constr) -> LinExpr:
self.flush_rows()
nnz = ffi.new("int *")
# obtaining number of non-zeros
st = GRBgetconstrs(
self._model, nnz, ffi.NULL, ffi.NULL, ffi.NULL, constr.idx, 1
)
if st != 0:
raise Exception(
"Could not get info for constraint {}".format(constr.idx)
)
nz = nnz[0]
# creating arrays to hold indices and coefficients
cbeg = ffi.new("int[2]")
cind = ffi.new("int[{}]".format(nz))
cval = ffi.new("double[{}]".format(nz))
# obtaining variables and coefficients
st = GRBgetconstrs(self._model, nnz, cbeg, cind, cval, constr.idx, 1)
if st != 0:
raise Exception("Could not query constraint contents")
# obtaining sense and rhs
c_sense = ffi.new("char *")
rhs = ffi.new("double *")
st = GRBgetcharattrelement(
self._model, "Sense".encode("utf-8"), constr.idx, c_sense
)
if st != 0:
raise Exception(
"Could not query sense for constraint {}".format(constr.idx)
)
st = GRBgetdblattrelement(
self._model, "RHS".encode("utf-8"), constr.idx, rhs
)
if st != 0:
raise Exception(
"Could not query RHS for constraint {}".format(constr.idx)
)
ssense = c_sense[0].decode("utf-8")
# translating sense
sense = ""
if ssense == "<":
sense = LESS_OR_EQUAL
elif ssense == ">":
sense = GREATER_OR_EQUAL
elif ssense == "=":
sense = EQUAL
expr = LinExpr(const=-rhs[0], sense=sense)
for i in range(nz):
expr.add_var(self.model.vars[cind[i]], cval[i])
return expr
def maximize_sum(plfs, max_costs):
# pylint: disable=too-many-locals
m = Model("bid-landscapes")
costs = LinExpr()
objective = LinExpr()
xs = []
ws = []
for (i, plf) in enumerate(plfs):
k = len(plf)
w = [m.add_var(var_type=CONTINUOUS) for _ in range(0, k)]
x = [m.add_var(var_type=BINARY) for _ in range(0, k - 1)]
xs.append(x)
ws.append(w)
m += xsum(w[i] for i in range(0, k)) == 1
for i in range(0, k):
m += w[i] >= 0
m += w[0] <= x[0]
for i in range(1, k - 1):
m += w[i] <= x[i - 1] + x[i]
m += w[k - 1] <= x[k - 2]
m += xsum(x[k] for k in range(0, k - 1)) == 1
for i in range(0, k):
costs.add_term(w[i] * plf.a[i])
objective.add_term(w[i] * plf.b[i])
m += costs <= max_costs
m.objective = maximize(objective)
start = monotonic()
print(m.optimize())
print(f"DURATION: {(monotonic() - start) * 1000} ms")
optimum = []
for (i, plf) in enumerate(plfs):
k = len(plf)
u_i = sum(ws[i][j].x * plf.a[j] for j in range(0, k))
v_i = sum(ws[i][j].x * plf.b[j] for j in range(0, k))
optimum.append(Line(cost=u_i, revenue=v_i, plf=plf))
return optimum