def test_model():
"""Test model."""
from pympl import Model, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
model = Model()
values = [15, 10, 9, 5]
weights = [1, 5, 3, 4]
xvars = []
for i in range(len(values)):
var = model.add_var(lb=0, ub=1, vtype="I")
xvars.append(var)
profit = model.add_var(lb=0, vtype="C")
model.add_con([(x, w) for x, w in zip(xvars, weights)], "<=", 8)
model.add_con(profit, "=", [(x, v) for x, v in zip(xvars, values)])
model.set_obj("max", profit)
lp_out = "tmp/model.lp"
mps_out = "tmp/model.mps"
mod_out = "tmp/model.mod"
model.write(lp_out)
model.write(mps_out)
model.write(mod_out)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
assert varvalues[profit] == 29
out, varvalues = Tools.script(
"glpk_wrapper.sh", mps_out, options="--max", verbose=True
)
assert varvalues[profit] == 29
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
assert varvalues[profit] == 29
def main():
"""Solve 'wolseyR2network.mod' and 'wolseyR1gamma.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
a, a0 = [65, 64, 41, 22, 13, 12, 8, 2], 80
aS = abs(2 * a0 + 1 - sum(a))
if a0 < (sum(a) - 1) // 2:
a0 += aS
a.append(aS)
print("self-dual:", a, a0)
m = len(a)
W = [a0] + [1] * len(a)
w = [[a[i]] + [1 if j == i else 0 for j in range(m)] for i in range(m)]
labels = [i + 1 for i in range(m)]
bounds = [1 if w[i] <= W else 0 for i in range(m)]
# wolseyR2network:
print("-" * 10)
print("Primal:")
mod_in = "wolseyR2network.mod"
mod_out = "tmp/wolseyR2network.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/wolseyR2network.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=False)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=False)
print("")
print("varvalues:",
[(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
# check the solution objective value:
assert abs(varvalues["Z0"] - 9) < 1e-5
# wolseyR1gamma:
print("-" * 10)
print("wolseyR1gamma:")
mod_in = "wolseyR1gamma.mod"
mod_out = "tmp/wolseyR1gamma.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/wolseyR1gamma.mod.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=False)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=False)
print("")
print("varvalues:",
[(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
# check the solution objective value:
assert abs(varvalues['theta(T)'] - 9) < 1e-5
def test_model():
"""Test model."""
from pympl import Model, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
model = Model()
values = [15, 10, 9, 5]
weights = [1, 5, 3, 4]
xvars = []
for i in range(len(values)):
var = model.add_var(lb=0, ub=1, vtype="I")
xvars.append(var)
profit = model.add_var(lb=0, vtype="C")
model.add_con([(x, w) for x, w in zip(xvars, weights)], "<=", 8)
model.add_con(profit, "=", [(x, v) for x, v in zip(xvars, values)])
model.set_obj("max", profit)
lp_out = "tmp/model.lp"
mps_out = "tmp/model.mps"
mod_out = "tmp/model.mod"
model.write(lp_out)
model.write(mps_out)
model.write(mod_out)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
assert varvalues[profit] == 29
out, varvalues = Tools.script("glpk_wrapper.sh",
mps_out,
options="--max",
verbose=True)
assert varvalues[profit] == 29
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
assert varvalues[profit] == 29
def main():
"""Solve 'wolseyR2network.mod' and 'wolseyR1gamma.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
a, a0 = [65, 64, 41, 22, 13, 12, 8, 2], 80
aS = abs(2 * a0 + 1 - sum(a))
if a0 < (sum(a) - 1) // 2:
a0 += aS
a.append(aS)
print("self-dual:", a, a0)
m = len(a)
W = [a0] + [1] * len(a)
w = [[a[i]] + [1 if j == i else 0 for j in range(m)] for i in range(m)]
labels = [i + 1 for i in range(m)]
bounds = [1 if w[i] <= W else 0 for i in range(m)]
# wolseyR2network:
print("-" * 10)
print("Primal:")
mod_in = "wolseyR2network.mod"
mod_out = "tmp/wolseyR2network.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/wolseyR2network.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=False)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=False)
print("")
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
# check the solution objective value:
assert abs(varvalues["Z0"] - 9) < 1e-5
# wolseyR1gamma:
print("-" * 10)
print("wolseyR1gamma:")
mod_in = "wolseyR1gamma.mod"
mod_out = "tmp/wolseyR1gamma.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/wolseyR1gamma.mod.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=False)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=False)
print("")
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
# check the solution objective value:
assert abs(varvalues["theta(T)"] - 9) < 1e-5
def main():
"""Parses 'instance_vbp.mod'"""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "instance_vbp.mod"
mod_out = "tmp/instance_vbp.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/instance_vbp.lp"
glpkutils.mod2lp(mod_out, lp_out, True)
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=True
)
sol = parser["VBP_FLOW"].extract(
lambda varname: varvalues.get(varname, 0),
verbose=True
)
print("")
print("sol:", sol)
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items())])
print("")
assert varvalues["Z"] == 33 # check the solution objective value
def main():
"""Solve 'vpsolver_vbp_flow.mod' and 'vpsolver_vbp_graph.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
for model in ["vpsolver_vbp_graph", "vpsolver_vbp_flow"]:
mod_in = "{}.mod".format(model)
mod_out = "tmp/{}.out.mod".format(model)
parser = PyMPL()
parser.parse(mod_in, mod_out)
lp_out = "tmp/{}.lp".format(model)
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=True
)
sol = parser["MVP_FLOW"].extract(
lambda varname: varvalues.get(varname, 0),
verbose=True
)
print("")
print("sol:", sol)
print("varvalues:", [
(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")
])
print("")
assert varvalues["Z"] == 33 # check the solution objective value
def main():
"""Parses 'instance.mod'"""
mod_in = "instance.mod"
mod_out = "tmp/instance.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/instance.lp"
glpkutils.mod2lp(mod_out, lp_out, True)
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=True
)
sol = parser["VBP_FLOW"].extract(
lambda varname: varvalues.get(varname, 0),
verbose=True
)
print("")
print("sol:", sol)
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items())])
print("")
assert varvalues["Z"] == 33 # check the solution objective value
exit_code = os.system("glpsol --math {0}".format(mod_out))
assert exit_code == 0
def main():
"""Parses 'twostage.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "twostage.mod"
mod_out = "tmp/twostage.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/twostage.lp"
glpkutils.mod2lp(mod_out, lp_out, True)
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=True
)
print("")
print("varvalues:", [
(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")
])
print("")
assert varvalues["Z"] == 15 # check the solution objective value
parser["VBP_FLOW"].extract(
lambda name: varvalues.get(name, 0),
verbose=True
)
def solve_glpk(model):
"""Solve a lot-sizing model using GLPK."""
from pympl import Tools
out, varvalues = Tools.script("glpk_wrapper.sh", model, verbose=False)
try:
return varvalues["cost"]
except:
return inf
def solve_glpk(model):
"""Solve a lot-sizing model using GLPK."""
from pympl import Tools
out, varvalues = Tools.script("glpk_wrapper.sh", model, verbose=False)
try:
return varvalues["cost"]
except:
return inf
def test_glpkutils():
"""Test glpkutils."""
from pympl import Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in, mod_out = "pwl.mod", "tmp/pwl.out.mod"
lp_out, mps_out = "tmp/pwl.lp", "tmp/pwl.mps"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
glpkutils.mod2mps(mod_out, mps_out, verbose=True)
Tools.clear()
Tools.set_verbose(False)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", mps_out, verbose=True)
def test_glpkutils():
"""Test glpkutils."""
from pympl import Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in, mod_out = "pwl.mod", "tmp/pwl.out.mod"
lp_out, mps_out = "tmp/pwl.lp", "tmp/pwl.mps"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
glpkutils.mod2mps(mod_out, mps_out, verbose=True)
Tools.clear()
Tools.set_verbose(False)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", mps_out, verbose=True)
def script_wsol(script_name, model, options=None, verbose=None):
"""Call a solver script and return the solution."""
from pympl import Tools
if verbose is None:
verbose = VPSolver.VERBOSE
if isinstance(model, (LP, MPS)):
model = model.filename
return Tools.script(script_name=script_name,
model=model,
options=options,
verbose=verbose)
def script_wsol(script_name, model, options=None, verbose=None):
"""Call a solver script and return the solution."""
from pympl import Tools
if verbose is None:
verbose = VPSolver.VERBOSE
if isinstance(model, (LP, MPS)):
model = model.filename
return Tools.script(
script_name=script_name, model=model, options=options,
verbose=verbose
)
def solve_gurobi(model):
"""Solve a lot-sizing model using Gurobi."""
from pympl import Tools
out, varvalues = Tools.script(
"gurobi_wrapper.sh", model,
options="Threads=1 Presolve=0 Heuristics=0.25 MIPGap=0",
verbose=False
)
try:
return varvalues["cost"]
except:
return inf
def solve_gurobi(model):
"""Solve a lot-sizing model using Gurobi."""
from pympl import Tools
out, varvalues = Tools.script(
"gurobi_wrapper.sh", model,
options="Threads=1 Presolve=0 Heuristics=0.25 MIPGap=0",
verbose=False
)
try:
return varvalues["cost"]
except:
return inf
def main():
"""Solve 'pwl.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "pwl.mod"
mod_out = "tmp/pwl.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/pwl.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items()) if not k.startswith("_")])
def main():
"""Solve 'sos2.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "sos2.mod"
mod_out = "tmp/sos2.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/sos2.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
print("varvalues:",
[(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")])
def main():
"""Solve 'twostage.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "twostage.mod"
mod_out = "tmp/twostage.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/twostage.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
print("")
print("varvalues:",
[(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
assert varvalues["Z"] == 11 # check the solution objective value
exctacted_solution = parser["VBP_FLOW"].extract(
lambda name: varvalues.get(name, 0), verbose=True)
solution = {}
for zvar, value, sol in exctacted_solution:
solution[zvar] = []
for r, pattern in sol:
solution[zvar] += [pattern] * r
assert value == len(solution[zvar])
def pop_pattern(zvar):
pattern = []
for it in solution[zvar].pop():
if it not in solution:
pattern.append(it)
else:
pattern.append((it, pop_pattern(it)))
return pattern
print("\n\nSolution:")
for i in range(varvalues["Z"]):
pattern = pop_pattern("Z")
print("Sheet {}: {}".format(i + 1, pattern))
def equivknapsack(a, a0, bounds=None):
"""Compute a minimal equivalent knapsack inequalitie."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
if bounds is None:
bounds = [a0]*len(a)
for i in range(len(a)):
bounds[i] = min(bounds[i], a0//a[i] if a[i] != 0 else 0)
sum_a = sum(x*y for x, y in zip(a, bounds))
aS = abs(2*a0+1-sum_a)
if a0 < (sum_a-1)//2:
a0 += aS
fix_as = 1
else:
fix_as = 0
if aS > a0:
return [0]*len(a), 0, bounds
a = list(a)+[aS]
bounds = bounds+[1]
mod_in = "equivknapsack.mod"
mod_out = "tmp/equivknapsack.out.mod"
parser = PyMPL(locals_=locals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/equivknapsack.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
# exit_code = os.system("glpsol --math {0}".format(mod_out))
# assert exit_code == 0
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=True
)
b = [varvalues.get("pi({0})".format(i+1), 0) for i in range(len(a))]
b0 = varvalues.get("pi(0)", 0)
if fix_as == 1:
b0 -= b[-1]
b = b[:-1]
else:
b = b[:-1]
return tuple(b), b0, tuple(bounds)
def main():
"""Solve 'twostage.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "twostage.mod"
mod_out = "tmp/twostage.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/twostage.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
print("")
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
assert varvalues["Z"] == 11 # check the solution objective value
exctacted_solution = parser["VBP_FLOW"].extract(lambda name: varvalues.get(name, 0), verbose=True)
solution = {}
for zvar, value, sol in exctacted_solution:
solution[zvar] = []
for r, pattern in sol:
solution[zvar] += [pattern] * r
assert value == len(solution[zvar])
def pop_pattern(zvar):
pattern = []
for it in solution[zvar].pop():
if it not in solution:
pattern.append(it)
else:
pattern.append((it, pop_pattern(it)))
return pattern
print("\n\nSolution:")
for i in range(varvalues["Z"]):
pattern = pop_pattern("Z")
print("Sheet {}: {}".format(i + 1, pattern))
def equivknapsack(a, a0, bounds=None):
"""Compute a minimal equivalent knapsack inequalitie."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
if bounds is None:
bounds = [a0] * len(a)
for i in range(len(a)):
bounds[i] = min(bounds[i], a0 // a[i] if a[i] != 0 else 0)
sum_a = sum(x * y for x, y in zip(a, bounds))
aS = abs(2 * a0 + 1 - sum_a)
if a0 < (sum_a - 1) // 2:
a0 += aS
fix_as = 1
else:
fix_as = 0
if aS > a0:
return [0] * len(a), 0, bounds
a = list(a) + [aS]
bounds = bounds + [1]
mod_in = "equivknapsack.mod"
mod_out = "tmp/equivknapsack.out.mod"
parser = PyMPL(locals_=locals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/equivknapsack.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
# exit_code = os.system("glpsol --math {0}".format(mod_out))
# assert exit_code == 0
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
b = [varvalues.get("pi({0})".format(i + 1), 0) for i in range(len(a))]
b0 = varvalues.get("pi(0)", 0)
if fix_as == 1:
b0 -= b[-1]
b = b[:-1]
else:
b = b[:-1]
return tuple(b), b0, tuple(bounds)
def main():
"""Parses 'instance_mvp.mod'"""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "instance_mvp.mod"
mod_out = "tmp/instance_mvp.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/instance_mvp.lp"
glpkutils.mod2lp(mod_out, lp_out, True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
sol = parser["MVP_FLOW"].extract(lambda varname: varvalues.get(varname, 0),
verbose=True)
print("")
print("sol:", sol)
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items())])
print("")
assert varvalues["cost"] == 8 # check the solution objective value
def main():
"""Solve 'ppbymip_ps.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "ppbymip_ps.mod"
mod_out = "tmp/ps.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/ps.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script(
"gurobi_wrapper.sh",
lp_out,
options="Threads=1 Presolve=0 Heuristics=0.25 MIPGap=0",
verbose=True)
print("varvalues:",
[(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")])
def main():
"""Solve 'ppbymip_mp.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
mod_in = "ppbymip_mp.mod"
mod_out = "tmp/mp.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/mp.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script(
"gurobi_wrapper.sh", lp_out,
options="Threads=1 Presolve=0 Heuristics=0.25 MIPGap=0",
verbose=True
)
print("varvalues:", [
(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")
])
def equivknapsack01(a, a0):
"""Compute a minimal equivalent 0-1 knapsack inequalitie."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
aS = abs(2*a0+1-sum(a))
if a0 < (sum(a)-1)//2:
a0 += aS
fix_as = 1
else:
fix_as = 0
if aS > a0:
return [0]*len(a), 0
a = list(a)+[aS]
mod_in = "equivknapsack01.mod"
mod_out = "tmp/equivknapsack01.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/equivknapsack01.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
# exit_code = os.system("glpsol --math {0}".format(mod_out))
# assert exit_code == 0
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=False
)
b = [varvalues.get("pi({0})".format(i+1), 0) for i in range(len(a))]
b0 = varvalues.get("pi(0)", 0)
if fix_as == 1:
b0 -= b[-1]
b = b[:-1]
else:
b = b[:-1]
return tuple(b), b0
def equivknapsack01(a, a0):
"""Compute a minimal equivalent 0-1 knapsack inequalitie."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
aS = abs(2 * a0 + 1 - sum(a))
if a0 < (sum(a) - 1) // 2:
a0 += aS
fix_as = 1
else:
fix_as = 0
if aS > a0:
return [0] * len(a), 0
a = list(a) + [aS]
mod_in = "equivknapsack01.mod"
mod_out = "tmp/equivknapsack01.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/equivknapsack01.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
# exit_code = os.system("glpsol --math {0}".format(mod_out))
# assert exit_code == 0
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=False)
b = [varvalues.get("pi({0})".format(i + 1), 0) for i in range(len(a))]
b0 = varvalues.get("pi(0)", 0)
if fix_as == 1:
b0 -= b[-1]
b = b[:-1]
else:
b = b[:-1]
return tuple(b), b0
def main():
"""Solve 'vpsolver_vbp_flow.mod' and 'vpsolver_vbp_graph.mod'."""
from pympl import PyMPL, Tools, glpkutils
os.chdir(os.path.dirname(__file__) or os.curdir)
for model in ["vpsolver_vbp_graph", "vpsolver_vbp_flow"]:
mod_in = "{}.mod".format(model)
mod_out = "tmp/{}.out.mod".format(model)
parser = PyMPL()
parser.parse(mod_in, mod_out)
lp_out = "tmp/{}.lp".format(model)
glpkutils.mod2lp(mod_out, lp_out, verbose=True)
out, varvalues = Tools.script("glpk_wrapper.sh", lp_out, verbose=True)
sol = parser["MVP_FLOW"].extract(
lambda varname: varvalues.get(varname, 0), verbose=True)
print("")
print("sol:", sol)
print("varvalues:",
[(k, v)
for k, v in sorted(varvalues.items()) if not k.startswith("_")])
print("")
assert varvalues["Z"] == 33 # check the solution objective value
def main():
"""Parses 'graph.mod'"""
a, a0 = [65, 64, 41, 22, 13, 12, 8, 2], 80
aS = abs(2*a0+1-sum(a))
if a0 < (sum(a)-1)//2:
a0 += aS
a.append(aS)
print('self-dual:', a, a0)
m = len(a)
W = [a0]+[1]*len(a)
w = [[a[i]]+[1 if j == i else 0 for j in range(m)] for i in range(m)]
labels = [i+1 for i in range(m)]
bounds = [1 if w[i] <= W else 0 for i in range(m)]
# wolseyR2network:
print("-"*10)
print("Primal:")
mod_in = "wolseyR2network.mod"
mod_out = "tmp/wolseyR2network.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/wolseyR2network.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=False)
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=False
)
print("")
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items())])
print("")
# Check the solution objective value:
assert abs(varvalues["Z0"] - 9) < 1e-5
exit_code = os.system("glpsol --math {0}".format(mod_out))
assert exit_code == 0
# wolseyR1gamma:
print("-"*10)
print("wolseyR1gamma:")
mod_in = "wolseyR1gamma.mod"
mod_out = "tmp/wolseyR1gamma.mod.out.mod"
parser = PyMPL(locals_=locals(), globals_=globals())
parser.parse(mod_in, mod_out)
lp_out = "tmp/wolseyR1gamma.mod.lp"
glpkutils.mod2lp(mod_out, lp_out, verbose=False)
out, varvalues = Tools.script(
"glpk_wrapper.sh", lp_out, verbose=False
)
print("")
print("varvalues:", [(k, v) for k, v in sorted(varvalues.items())])
print("")
# Check the solution objective value:
assert abs(varvalues['theta(T)'] - 9) < 1e-5
exit_code = os.system("glpsol --math {0}".format(mod_out))
assert exit_code == 0