def test_mip_file(solver: str, instance: str):
"""Tests optimization of MIP models stored in .mps or .lp files"""
m = Model(solver_name=solver)
# optional file for optimal LP basis
bas_file = ""
iname = ""
for ext in EXTS:
if instance.endswith(ext):
bas_file = instance.replace(ext, ".bas")
if not exists(bas_file):
bas_file = ""
iname = basename(instance.replace(ext, ""))
break
assert iname in BOUNDS.keys()
lb = BOUNDS[iname][0]
ub = BOUNDS[iname][1]
assert lb <= ub + TOL
has_opt = abs(ub - lb) <= TOL
max_dif = max(max(abs(ub), abs(lb)) * 0.01, TOL)
m.read(instance)
if bas_file:
m.verbose = True
m.read(bas_file)
m.optimize(relax=True)
print("Basis loaded!!! Obj value: %f" % m.objective_value)
m.optimize(max_nodes=MAX_NODES)
if m.status in [OptimizationStatus.OPTIMAL, OptimizationStatus.FEASIBLE]:
assert m.num_solutions >= 1
m.check_optimization_results()
assert m.objective_value >= lb - max_dif
if has_opt and m.status == OptimizationStatus.OPTIMAL:
assert abs(m.objective_value - ub) <= max_dif
elif m.status == OptimizationStatus.NO_SOLUTION_FOUND:
assert m.objective_bound <= ub + max_dif
else:
assert m.status not in [
OptimizationStatus.INFEASIBLE,
OptimizationStatus.INT_INFEASIBLE,
OptimizationStatus.UNBOUNDED,
OptimizationStatus.ERROR,
OptimizationStatus.CUTOFF,
]
assert m.objective_bound <= ub + max_dif
for (i, j) in product(V - {0}, V - {0}):
if i != j:
model += y[i] - (n + 1) * x[i][j] >= y[j] - n
# optimizing
model.threads = 4
model.optimize(max_nodes=75)
# checking if a solution was found
if model.num_solutions:
out.write("route with total distance %g found: %s" %
(model.objective_value, 0))
nc = 0
while True:
nc = [i for i in V if x[nc][i].x >= 0.99][0]
out.write(" -> %s" % nc)
if nc == 0:
break
out.write("\n")
# sanity tests
from mip import OptimizationStatus
if model.status == OptimizationStatus.OPTIMAL:
assert round(model.objective_value) == 7013
elif model.status == OptimizationStatus.FEASIBLE:
assert round(model.objective_value) >= 7013
else:
assert model.objective_bound <= 7013 + 1e-7
model.check_optimization_results()
for i in N:
m += xsum(x[i][c] for c in U) == r[i]
for i, j, c1, c2 in product(N, N, U, U):
if i != j and c1 <= c2 < c1 + d[i][j]:
m += x[i][c1] + x[j][c2] <= 1
for i, c1, c2 in product(N, U, U):
if c1 < c2 < c1 + d[i][i]:
m += x[i][c1] + x[i][c2] <= 1
for i, c in product(N, U):
m += z >= (c + 1) * x[i][c]
m.optimize(max_nodes=30)
if m.num_solutions:
for i in N:
print('Channels of node %d: %s' %
(i, [c for c in U if x[i][c].x >= 0.99]))
# sanity tests
from mip import OptimizationStatus
assert m.objective_bound <= 41 + 1e-10
if m.status == OptimizationStatus.OPTIMAL:
assert round(m.objective_value) == 41
elif m.status == OptimizationStatus.FEASIBLE:
assert m.objective_value >= 41 - 1e-10
m.check_optimization_results()
# checking if columns with negative reduced cost were produced and
# adding them into the restricted master problem
if pricing.objective_value < -1e-5:
pattern = [a[i].x for i in range(m)]
column = Column(constraints, pattern)
lambdas.append(
master.add_var(obj=1,
column=column,
name='lambda_%d' % (len(lambdas) + 1)))
print('new pattern = {pattern}'.format(**locals()))
# if no column with negative reduced cost was produced, then linear
# relaxation of the restricted master problem is solved
else:
new_vars = False
pricing.write('pricing.lp')
# printing the solution
print('')
print('Objective value: {master.objective_value:.3}'.format(**locals()))
print('Solution: ', end='')
for v in lambdas:
if v.x > 1e-6:
print('{v.name} = {v.x:.3} {v.column}'.format(**locals()))
print(' ', end='')
# sanity checks
master.check_optimization_results()