def create_model():
# create solver instance
s = Model()
# add some variables
x = s.addVar("x", obj = -1.0, vtype = "I", lb=-10)
y = s.addVar("y", obj = 1.0, vtype = "I", lb=-1000)
z = s.addVar("z", obj = 1.0, vtype = "I", lb=-1000)
# add some constraint
s.addCons(314*x + 867*y + 860*z == 363)
s.addCons(87*x + 875*y - 695*z == 423)
# create conshdlr and include it to SCIP
conshdlr = MyConshdlr(shouldtrans=True, shouldcopy=False)
s.includeConshdlr(conshdlr, "PyCons", "custom constraint handler implemented in python",
sepapriority = 1, enfopriority = -1, chckpriority = 1, sepafreq = 10, propfreq = 50,
eagerfreq = 1, maxprerounds = -1, delaysepa = False, delayprop = False, needscons = True,
presoltiming = SCIP_PRESOLTIMING.FAST, proptiming = SCIP_PROPTIMING.BEFORELP)
cons1 = s.createCons(conshdlr, "cons1name")
ids.append(id(cons1))
cons2 = s.createCons(conshdlr, "cons2name")
ids.append(id(cons2))
conshdlr.createData(cons1, 10, "cons1_anothername")
conshdlr.createData(cons2, 12, "cons2_anothername")
# add these constraints
s.addPyCons(cons1)
s.addPyCons(cons2)
return s
def create_model():
# create solver instance
s = Model()
# add some variables
x = s.addVar("x", obj=-1.0, vtype="I", lb=-10)
y = s.addVar("y", obj=1.0, vtype="I", lb=-1000)
z = s.addVar("z", obj=1.0, vtype="I", lb=-1000)
# add some constraint
s.addCons(314 * x + 867 * y + 860 * z == 363)
s.addCons(87 * x + 875 * y - 695 * z == 423)
# create conshdlr and include it to SCIP
conshdlr = MyConshdlr(shouldtrans=True, shouldcopy=False)
s.includeConshdlr(conshdlr,
"PyCons",
"custom constraint handler implemented in python",
sepapriority=1,
enfopriority=1,
chckpriority=1,
sepafreq=10,
propfreq=50,
eagerfreq=1,
maxprerounds=-1,
delaysepa=False,
delayprop=False,
needscons=True,
presoltiming=SCIP_PRESOLTIMING.FAST,
proptiming=SCIP_PROPTIMING.BEFORELP)
cons1 = s.createCons(conshdlr, "cons1name")
ids.append(id(cons1))
cons2 = s.createCons(conshdlr, "cons2name")
ids.append(id(cons2))
conshdlr.createData(cons1, 10, "cons1_anothername")
conshdlr.createData(cons2, 12, "cons2_anothername")
# add these constraints
s.addPyCons(cons1)
s.addPyCons(cons2)
return s
def create_sudoku():
scip = Model("Sudoku")
x = {} # values of squares
for row in range(9):
for col in range(9):
# some variables are fix
if init[row*9 + col] != 0:
x[row,col] = scip.addVar(vtype = "I", lb = init[row*9 + col], ub = init[row*9 + col], name = "x(%s,%s)" % (row,col))
else:
x[row,col] = scip.addVar(vtype = "I", lb = 1, ub = 9, name = "x(%s,%s)" % (row,col))
var = x[row,col]
#print("built var ", var.name, " with bounds: (%d,%d)"%(var.getLbLocal(), var.getUbLocal()))
conshdlr = ALLDIFFconshdlr()
# hoping to get called when all vars have integer values
scip.includeConshdlr(conshdlr, "ALLDIFF", "All different constraint", propfreq = 1, enfopriority = -10, chckpriority = -10)
# row constraints; also we specify the domain of all variables here
# TODO/QUESTION: in principle domain is of course associated to the var and not the constraint. it should be "var.data"
# But ideally that information would be handle by SCIP itself... the reason we can't is because domain holes is not implemented, right?
domains = {}
for row in range(9):
vars = []
for col in range(9):
var = x[row,col]
vars.append(var)
vals = set(range(int(round(var.getLbLocal())), int(round(var.getUbLocal())) + 1))
domains[var.ptr()] = vals
# this is kind of ugly, isn't it?
cons = scip.createCons(conshdlr, "row_%d" % row)
#print("in test: received a constraint with id ", id(cons)) ### DELETE
cons.data = SimpleNamespace() # so that data behaves like an instance of a class (ie, cons.data.whatever is allowed)
cons.data.vars = vars
cons.data.domains = domains
scip.addPyCons(cons)
# col constraints
for col in range(9):
vars = []
for row in range(9):
var = x[row,col]
vars.append(var)
cons = scip.createCons(conshdlr, "col_%d"%col)
cons.data = SimpleNamespace()
cons.data.vars = vars
cons.data.domains = domains
scip.addPyCons(cons)
# square constraints
for idx1 in range(3):
for idx2 in range(3):
vars = []
for row in range(3):
for col in range(3):
var = x[3*idx1 + row, 3*idx2 + col]
vars.append(var)
cons = scip.createCons(conshdlr, "square_%d-%d"%(idx1, idx2))
cons.data = SimpleNamespace()
cons.data.vars = vars
cons.data.domains = domains
scip.addPyCons(cons)
#scip.setObjective()
return scip, x
def create_sudoku():
scip = Model("Sudoku")
x = {} # values of squares
for row in range(9):
for col in range(9):
# some variables are fix
if init[row*9 + col] != 0:
x[row,col] = scip.addVar(vtype = "I", lb = init[row*9 + col], ub = init[row*9 + col], name = "x(%s,%s)" % (row,col))
else:
x[row,col] = scip.addVar(vtype = "I", lb = 1, ub = 9, name = "x(%s,%s)" % (row,col))
var = x[row,col]
#print("built var ", var.name, " with bounds: (%d,%d)"%(var.getLbLocal(), var.getUbLocal()))
conshdlr = ALLDIFFconshdlr()
# hoping to get called when all vars have integer values
scip.includeConshdlr(conshdlr, "ALLDIFF", "All different constraint", propfreq = 1, enfopriority = -10, chckpriority = -10)
# row constraints; also we specify the domain of all variables here
# TODO/QUESTION: in principle domain is of course associated to the var and not the constraint. it should be "var.data"
# But ideally that information would be handle by SCIP itself... the reason we can't is because domain holes is not implemented, right?
domains = {}
for row in range(9):
vars = []
for col in range(9):
var = x[row,col]
vars.append(var)
vals = set(range(int(round(var.getLbLocal())), int(round(var.getUbLocal())) + 1))
domains[var.ptr()] = vals
# this is kind of ugly, isn't it?
cons = scip.createCons(conshdlr, "row_%d" % row)
#print("in test: received a constraint with id ", id(cons)) ### DELETE
cons.data = SimpleNamespace() # so that data behaves like an instance of a class (ie, cons.data.whatever is allowed)
cons.data.vars = vars
cons.data.domains = domains
scip.addPyCons(cons)
# col constraints
for col in range(9):
vars = []
for row in range(9):
var = x[row,col]
vars.append(var)
cons = scip.createCons(conshdlr, "col_%d"%col)
cons.data = SimpleNamespace()
cons.data.vars = vars
cons.data.domains = domains
scip.addPyCons(cons)
# square constraints
for idx1 in range(3):
for idx2 in range(3):
vars = []
for row in range(3):
for col in range(3):
var = x[3*idx1 + row, 3*idx2 + col]
vars.append(var)
cons = scip.createCons(conshdlr, "square_%d-%d"%(idx1, idx2))
cons.data = SimpleNamespace()
cons.data.vars = vars
cons.data.domains = domains
scip.addPyCons(cons)
#scip.setObjective()
return scip, x
