def test_model():
# create solver instance
s = Model()
# add some variables
x = s.addVar("x", vtype = 'C', obj = 1.0)
y = s.addVar("y", vtype = 'C', obj = 2.0)
assert x.getObj() == 1.0
assert y.getObj() == 2.0
s.setObjective(4.0 * y + 10.5, clear = False)
assert x.getObj() == 1.0
assert y.getObj() == 4.0
assert s.getObjoffset() == 10.5
# add some constraint
c = s.addCons(x + 2 * y >= 1.0)
assert c.isLinear()
s.chgLhs(c, 5.0)
s.chgRhs(c, 6.0)
assert s.getLhs(c) == 5.0
assert s.getRhs(c) == 6.0
# solve problem
s.optimize()
solution = s.getBestSol()
# print solution
assert (s.getVal(x) == s.getSolVal(solution, x))
assert (s.getVal(y) == s.getSolVal(solution, y))
assert round(s.getVal(x)) == 5.0
assert round(s.getVal(y)) == 0.0
assert s.getSlack(c, solution) == 0.0
assert s.getSlack(c, solution, 'lhs') == 0.0
assert s.getSlack(c, solution, 'rhs') == 1.0
assert s.getActivity(c, solution) == 5.0
s.writeProblem('model')
s.writeProblem('model.lp')
s.freeProb()
s = Model()
x = s.addVar("x", vtype = 'C', obj = 1.0)
y = s.addVar("y", vtype = 'C', obj = 2.0)
c = s.addCons(x + 2 * y <= 1.0)
s.setMaximize()
s.delCons(c)
s.optimize()
assert s.getStatus() == 'unbounded'
def test_model():
# create solver instance
s = Model()
# add some variables
x = s.addVar("x", vtype='C', obj=1.0)
y = s.addVar("y", vtype='C', obj=2.0)
assert x.getObj() == 1.0
assert y.getObj() == 2.0
s.setObjective(4.0 * y + 10.5, clear=False)
assert x.getObj() == 1.0
assert y.getObj() == 4.0
assert s.getObjoffset() == 10.5
# add some constraint
c = s.addCons(x + 2 * y >= 1.0)
s.chgLhs(c, 5.0)
s.chgRhs(c, 6.0)
assert s.getLhs(c) == 5.0
assert s.getRhs(c) == 6.0
badsolution = s.createSol()
s.setSolVal(badsolution, x, 2.0)
s.setSolVal(badsolution, y, 2.0)
assert s.getSlack(c, badsolution) == 0.0
assert s.getSlack(c, badsolution, 'lhs') == 1.0
assert s.getSlack(c, badsolution, 'rhs') == 0.0
assert s.getActivity(c, badsolution) == 6.0
s.freeSol(badsolution)
# solve problem
s.optimize()
solution = s.getBestSol()
# print solution
assert (s.getVal(x) == s.getSolVal(solution, x))
assert (s.getVal(y) == s.getSolVal(solution, y))
assert round(s.getVal(x)) == 5.0
assert round(s.getVal(y)) == 0.0
assert s.getSlack(c, solution) == 0.0
assert s.getSlack(c, solution, 'lhs') == 0.0
assert s.getSlack(c, solution, 'rhs') == 1.0
assert s.getActivity(c, solution) == 5.0
s.freeProb()
s = Model()
x = s.addVar("x", vtype='C', obj=1.0)
y = s.addVar("y", vtype='C', obj=2.0)
c = s.addCons(x + 2 * y <= 1.0)
s.setMaximize()
s.delCons(c)
s.optimize()
assert s.getStatus() == 'unbounded'
def test_lp():
# create solver instance
s = Model()
# add some variables
x = s.addVar("x", vtype='C', obj=1.0)
y = s.addVar("y", vtype='C', obj=2.0)
assert x.getObj() == 1.0
assert y.getObj() == 2.0
s.setObjective(4.0 * y, clear=False)
assert x.getObj() == 1.0
assert y.getObj() == 4.0
# add some constraint
c = s.addCons(x + 2 * y >= 1.0)
s.chgLhs(c, 5.0)
s.chgRhs(c, 5.0)
# solve problem
s.optimize()
solution = s.getBestSol()
# print solution
assert (s.getVal(x) == s.getSolVal(solution, x))
assert (s.getVal(y) == s.getSolVal(solution, y))
assert round(s.getVal(x)) == 5.0
assert round(s.getVal(y)) == 0.0
s.freeProb()
s = Model()
x = s.addVar("x", vtype='C', obj=1.0)
y = s.addVar("y", vtype='C', obj=2.0)
c = s.addCons(x + 2 * y <= 1.0)
s.setMaximize()
s.delCons(c)
s.optimize()
assert s.getStatus() == 'unbounded'
def contains_scip(self,x):
"""Uses pyscipopt... found to be much slower than cvxopt."""
from pyscipopt import Model,quicksum
if not hasattr(self,'model'):
model = Model("Vpolytope")
M = 1000
zs = []
z0 = model.addVar(vtype="C", name="z0", lb=-M, ub=M)
zs.append(z0)
for i in xrange(self.vertices.shape[1]):
zi = model.addVar(vtype="C", name="z"+str(i+1), lb=-M, ub=M)
zs.append(zi)
# Set up inequality constraints
for i in xrange(self.vertices.shape[0]):
model.addCons(quicksum(zi*xi for (zi,xi) in zip(zs[1:],self.vertices[i])) <= 0,name="v"+str(i))
#set up bound
bnd = model.addCons(quicksum(zi*xi for (zi,xi) in zip(zs[1:],x))-z0 <= 1, name='bound')
model.data = zs,bnd
#TODO: need to upgrade pyscipopt
#self.model = model
else:
model = self.model
zs,bnd = model.data
z0 = zs[0]
model.chgLhs(bnd,quicksum(zi*xi for (zi,xi) in zip(zs[1:],x))-z0)
# Set up objective
model.setObjective(quicksum(zi*xi for (zi,xi) in zip(zs[1:],x))-z0, 'maximize')
t0 = time.time()
#print "Beginning SCIP optimization..."
model.hideOutput()
model.optimize()
t1 = time.time()
#print "SOLVE TIME",t1-t0
#print "STATUS:",model.getStatus()
if model.getStatus() != 'optimal':
return True
#print "Optimal value :",model.getObjVal()
if model.getObjVal() < 1e-6*math.sqrt(self.vertices.shape[1]):
return True
return False
def test_model():
# create solver instance
s = Model()
# test parameter methods
pric = s.getParam('lp/pricing')
s.setParam('lp/pricing', 'q')
assert 'q' == s.getParam('lp/pricing')
s.setParam('lp/pricing', pric)
s.setParam('visual/vbcfilename', 'vbcfile')
assert 'vbcfile' == s.getParam('visual/vbcfilename')
assert 'lp/pricing' in s.getParams()
s.setParams({'visual/vbcfilename': '-'})
assert '-' == s.getParam('visual/vbcfilename')
# add some variables
x = s.addVar("x", vtype='C', obj=1.0)
y = s.addVar("y", vtype='C', obj=2.0)
assert x.getObj() == 1.0
assert y.getObj() == 2.0
s.setObjective(4.0 * y + 10.5, clear=False)
assert x.getObj() == 1.0
assert y.getObj() == 4.0
assert s.getObjoffset() == 10.5
# add some constraint
c = s.addCons(x + 2 * y >= 1.0)
assert c.isLinear()
s.chgLhs(c, 5.0)
s.chgRhs(c, 6.0)
assert s.getLhs(c) == 5.0
assert s.getRhs(c) == 6.0
# solve problem
s.optimize()
solution = s.getBestSol()
# print solution
assert (s.getVal(x) == s.getSolVal(solution, x))
assert (s.getVal(y) == s.getSolVal(solution, y))
assert round(s.getVal(x)) == 5.0
assert round(s.getVal(y)) == 0.0
assert s.getSlack(c, solution) == 0.0
assert s.getSlack(c, solution, 'lhs') == 0.0
assert s.getSlack(c, solution, 'rhs') == 1.0
assert s.getActivity(c, solution) == 5.0
# check expression evaluations
expr = x * x + 2 * x * y + y * y
expr2 = x + 1
assert s.getVal(expr) == s.getSolVal(solution, expr)
assert s.getVal(expr2) == s.getSolVal(solution, expr2)
assert round(s.getVal(expr)) == 25.0
assert round(s.getVal(expr2)) == 6.0
s.writeProblem('model')
s.writeProblem('model.lp')
s.freeProb()
s = Model()
x = s.addVar("x", vtype='C', obj=1.0)
y = s.addVar("y", vtype='C', obj=2.0)
c = s.addCons(x + 2 * y <= 1.0)
s.setMaximize()
s.delCons(c)
s.optimize()
assert s.getStatus() == 'unbounded'
