def solve_restricted_primal(self, demands, demands_next, p):
self.obj = 0.
m = Model("multi-unit-auction")
# self.m.params.LogToConsole = 0
self.allocation_vars = dict()
for agent in self.agents:
for i in range(1, self.supply + 1):
self.allocation_vars[agent.id, i] = m.addVar(lb=0., ub=1., vtype=GRB.CONTINUOUS,
name='x_%s_%s' % (agent.id, i))
m.update()
for agent in self.agents:
if len(demands[agent.id]) > 0 and len(demands_next[agent.id]) > 0:
m.addConstr(quicksum(self.allocation_vars[agent.id, i] for i in range(1, self.supply + 1)),
GRB.EQUAL, 1, name="u_%s_strict" % agent.id)
else:
m.addConstr(quicksum(self.allocation_vars[agent.id, i] for i in range(1, self.supply + 1)),
GRB.LESS_EQUAL, 1, name="u_%s" % agent.id)
for j in range(1, self.supply + 1):
if j not in [demand.quantity for demand in demands[agent.id]]:
m.addConstr(self.allocation_vars[agent.id, j], GRB.EQUAL, 0, name='x_%s_%s_undemanded' % (agent.id, j))
if p > 0:
m.addConstr(
quicksum(self.allocation_vars[agent.id, i] * i for i in range(1, self.supply + 1) for agent in self.agents),
GRB.EQUAL, self.supply, name="price_strict")
else:
m.addConstr(
quicksum(self.allocation_vars[agent.id, i] * i for i in range(1, self.supply + 1) for agent in self.agents),
GRB.LESS_EQUAL, self.supply, name="price")
obj_expr = LinExpr()
for agent in self.agents:
for valuation in agent.valuations:
obj_expr.addTerms(valuation.quantity, self.allocation_vars[agent.id, valuation.quantity])
m.setObjective(obj_expr, GRB.MAXIMIZE)
m.update()
m.optimize()
m.write('optimal-lp.lp')
if m.status == GRB.OPTIMAL:
m.write('optimal-lp.sol')
for v in [v for v in m.getVars() if v.x != 0.]:
print('%s %g' % (v.varName, v.x))
print ''
print 'CONSTRAINTS:'
for l in m.getConstrs():
if l.Pi > 0:
print('%s %g' % (l.constrName, l.Pi))
print m.getObjective().getValue()
return m
m.addConstr(x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24, GRB.LESS_EQUAL, 2, name="all")
m.addConstr(x12, GRB.LESS_EQUAL, x11, name="c11")
m.addConstr(x13, GRB.LESS_EQUAL, x12, name="c12")
m.addConstr(x14, GRB.LESS_EQUAL, x13, name="c13")
m.addConstr(x22, GRB.LESS_EQUAL, x21, name="c21")
m.addConstr(x23, GRB.LESS_EQUAL, x22, name="c22")
m.addConstr(x24, GRB.LESS_EQUAL, x23, name="c23")
m.addConstr(x14, GRB.GREATER_EQUAL, 0, name="11")
m.addConstr(x24, GRB.GREATER_EQUAL, 0, name="12")
m.addConstr(x11, GRB.LESS_EQUAL, .5, name="u1")
m.addConstr(x21, GRB.LESS_EQUAL, .5, name="u2")
#m.addConstr(x11 + 2*x12 + 3*x13 + 4*x14 + x21 + 2*x22 + 3*x23 + 4*x24, GRB.LESS_EQUAL, 4, name="p_an")
m.setObjective(x11 * 6 + x12 * 0 + x13 * 0 + x14 * 3 + x21 * 1 + x22 * 3 + x23 * 0 + x24 * 2, GRB.MAXIMIZE)
m.optimize()
for v in [v for v in m.getVars() if v.x != 0.]:
print('%s %g' % (v.varName, v.x))
print 'CONSTRS'
for l in m.getConstrs():
if l.Pi > 0:
print('%s %g' % (l.constrName, l.Pi))
print m.getObjective().getValue()
class OptimalSolver:
def __init__(self, supply, agents, gap, restriced=False):
print ''
print 'Optimal Solver:'
self.m = Model("multi-unit-auction")
self.m.params.LogToConsole = 0
self.allocation_vars = dict()
for agent in agents:
for i in range(1, supply + 1):
self.allocation_vars[agent.id, i] = self.m.addVar(lb=0., ub=1., vtype=GRB.CONTINUOUS, name='x_%s_%s' % (agent.id, i))
self.m.update()
for agent in agents:
self.m.addConstr(quicksum(self.allocation_vars[agent.id, i] for i in range(1, supply + 1)), GRB.LESS_EQUAL, 1, name="u_%s" % agent.id)
if restriced:
for valuation in agent.valuations:
if valuation.valuation > 0:
self.m.addConstr(self.allocation_vars[agent.id, valuation.quantity] >= epsilon, name="not_zero_%s_%s" % (agent.id, valuation.quantity))
self.m.addConstr(quicksum(self.allocation_vars[agent.id, i]*i for i in range(1, supply + 1) for agent in agents), GRB.LESS_EQUAL, supply, name="price")
obj_expr = LinExpr()
for agent in agents:
for valuation in agent.valuations:
obj_expr.addTerms(valuation.valuation, self.allocation_vars[agent.id, valuation.quantity])
self.m.setObjective(obj_expr, GRB.MAXIMIZE)
self.m.update()
self.m.optimize()
#
# for agent in agents:
# for i in range(1, supply + 1):
# self.allocation_vars[agent.id, i] = self.m.addVar(vtype=GRB.CONTINUOUS, lb=0,
# name='x_%s_%s' % (agent.id, i))
#
# self.m.update()
#
# self.m.addConstr(quicksum(self.allocation_vars[agent.id, i] for i in range(1, supply + 1) for agent in agents),
# GRB.LESS_EQUAL, supply / gap, name="price")
# for agent in agents:
# for i in range(1, supply):
# self.m.addConstr(self.allocation_vars[agent.id, i + 1] - self.allocation_vars[agent.id, i],
# GRB.LESS_EQUAL, 0, name="chain_%s_%s" % (agent.id, i))
# self.m.addConstr(self.allocation_vars[agent.id, i], GRB.LESS_EQUAL, 1. / gap,
# name="p_%s_%s" % (agent.id, i))
# self.m.addConstr(self.allocation_vars[agent.id, supply], GRB.GREATER_EQUAL, 0, name="greater_%s" % agent.id)
# self.m.addConstr(self.allocation_vars[agent.id, 1], GRB.LESS_EQUAL, 1. / gap, name="u_%s" % agent.id)
#
# # m.addConstr(x11 + 2*x12 + 3*x13 + 4*x14 + x21 + 2*x22 + 3*x23 + 4*x24, GRB.LESS_EQUAL, 4, name="p_an")
#
# obj_expr = LinExpr()
# for agent in agents:
# prev_val = None
# for valuation in agent.valuations:
# try:
# prev_val = next(val for val in agent.valuations if val.quantity == valuation.quantity - 1)
# except StopIteration:
# pass
# marginal_value = valuation.valuation - (prev_val.valuation if prev_val else 0)
# obj_expr.addTerms(marginal_value, self.allocation_vars[agent.id, valuation.quantity])
# self.m.setObjective(obj_expr, GRB.MAXIMIZE)
#
# self.m.optimize()
for v in [v for v in self.m.getVars() if v.x != 0.]:
print('%s %g' % (v.varName, v.x))
print ''
print 'CONSTRAINTS:'
for l in self.m.getConstrs():
if l.Pi > 0:
print('%s %g' % (l.constrName, l.Pi))
print self.m.getObjective().getValue()
# print 'Optimal solution:'
# for v in self.m.getVars():
# print('%s %g' % (v.varName, v.x))
# for l in self.m.getConstrs():
# if l.Pi > 0:
# print('%s %g' % (l.constrName, l.Pi))
print 'OPT social welfare %s | %s/%s=%s' % (
self.m.getObjective().getValue(), self.m.getObjective().getValue(), gap,
self.m.getObjective().getValue() / gap)
self.m.write('optimal-lp.lp')
self.m.write('optimal-lp.sol')