def test_options(self):
from cvxopt import glpk, solvers
c,G,h,A,b = self._prob_data
glpk.options = {'msg_lev' : 'GLP_MSG_OFF'}
sol1 = glpk.lp(c,G,h)
self.assertTrue(sol1[0]=='optimal')
sol2 = glpk.lp(c,G,h,A,b)
self.assertTrue(sol2[0]=='optimal')
sol3 = glpk.lp(c,G,h,options={'msg_lev' : 'GLP_MSG_ON'})
self.assertTrue(sol3[0]=='optimal')
sol4 = glpk.lp(c,G,h,A,b,options={'msg_lev' : 'GLP_MSG_ERR'})
self.assertTrue(sol4[0]=='optimal')
sol5 = solvers.lp(c,G,h,solver='glpk',options={'glpk':{'msg_lev' : 'GLP_MSG_ON'}})
self.assertTrue(sol5['status']=='optimal')
sol1 = glpk.ilp(c,G,h,None,None,set(),set([0,1]))
self.assertTrue(sol1[0]=='optimal')
sol2 = glpk.ilp(c,G,h,A,b,set([0,1]),set())
self.assertTrue(sol2[0]=='optimal')
sol3 = glpk.ilp(c,G,h,None,None,set(),set([0,1]),options={'msg_lev' : 'GLP_MSG_ALL'})
self.assertTrue(sol3[0]=='optimal')
sol4 = glpk.ilp(c,G,h,A,b,set(),set([0]),options={'msg_lev' : 'GLP_MSG_ALL'})
self.assertTrue(sol4[0]=='optimal')
solvers.options['glpk'] = {'msg_lev' : 'GLP_MSG_ON'}
sol5 = solvers.lp(c,G,h,solver='glpk')
self.assertTrue(sol5['status']=='optimal')
def test_options(self):
from cvxopt import glpk, solvers
c, G, h, A, b = self._prob_data
glpk.options = {'msg_lev': 'GLP_MSG_OFF'}
sol1 = glpk.lp(c, G, h)
self.assertTrue(sol1[0] == 'optimal')
sol2 = glpk.lp(c, G, h, A, b)
self.assertTrue(sol2[0] == 'optimal')
sol3 = glpk.lp(c, G, h, options={'msg_lev': 'GLP_MSG_ON'})
self.assertTrue(sol3[0] == 'optimal')
sol4 = glpk.lp(c, G, h, A, b, options={'msg_lev': 'GLP_MSG_ERR'})
self.assertTrue(sol4[0] == 'optimal')
sol5 = solvers.lp(c,
G,
h,
solver='glpk',
options={'glpk': {
'msg_lev': 'GLP_MSG_ON'
}})
self.assertTrue(sol5['status'] == 'optimal')
sol1 = glpk.ilp(c, G, h, None, None, None, None, set(), set([0, 1]))
self.assertTrue(sol1[0] == 'optimal')
sol2 = glpk.ilp(c, G, h, A, b, None, None, set([0, 1]), set())
self.assertTrue(sol2[0] == 'optimal')
sol3 = glpk.ilp(c,
G,
h,
None,
None,
None,
None,
set(),
set([0, 1]),
options={'msg_lev': 'GLP_MSG_ALL'})
self.assertTrue(sol3[0] == 'optimal')
sol4 = glpk.ilp(c,
G,
h,
A,
b,
None,
None,
set(),
set([0]),
options={'msg_lev': 'GLP_MSG_ALL'})
self.assertTrue(sol4[0] == 'optimal')
solvers.options['glpk'] = {'msg_lev': 'GLP_MSG_ON'}
sol5 = solvers.lp(c, G, h, solver='glpk')
self.assertTrue(sol5['status'] == 'optimal')
def test_lp(self):
from cvxopt import solvers, glpk
c, G, h, A, b = self._prob_data
sol1 = solvers.lp(c, G, h)
self.assertTrue(sol1['status'] == 'optimal')
sol2 = solvers.lp(c, G, h, A, b)
self.assertTrue(sol2['status'] == 'optimal')
sol3 = solvers.lp(c, G, h, solver='glpk')
self.assertTrue(sol3['status'] == 'optimal')
sol4 = solvers.lp(c, G, h, A, b, solver='glpk')
self.assertTrue(sol4['status'] == 'optimal')
sol5 = glpk.lp(c, G, h)
self.assertTrue(sol5[0] == 'optimal')
sol6 = glpk.lp(c, G, h, A, b)
self.assertTrue(sol6[0] == 'optimal')
sol7 = glpk.lp(c, G, h, None, None)
self.assertTrue(sol7[0] == 'optimal')
def test_lp(self):
from cvxopt import solvers, glpk
c,G,h,A,b = self._prob_data
sol1 = solvers.lp(c,G,h)
self.assertTrue(sol1['status']=='optimal')
sol2 = solvers.lp(c,G,h,A,b)
self.assertTrue(sol2['status']=='optimal')
sol3 = solvers.lp(c,G,h,solver='glpk')
self.assertTrue(sol3['status']=='optimal')
sol4 = solvers.lp(c,G,h,A,b,solver='glpk')
self.assertTrue(sol4['status']=='optimal')
sol5 = glpk.lp(c,G,h)
self.assertTrue(sol5[0]=='optimal')
sol6 = glpk.lp(c,G,h,A,b)
self.assertTrue(sol6[0]=='optimal')
sol7 = glpk.lp(c,G,h,None,None)
self.assertTrue(sol7[0]=='optimal')
def custom_solve_lp(c, G, h, A, b):
status, x, z, y = glpk.lp(c, G, h, A, b, options=this_options)
if status == 'optimal':
pcost = blas.dot(c, x)
else:
pcost = None
return {'status': status, 'x': x, 'primal objective': pcost}
def custom_solve_ilp(c, G, h, A, b):
size = G.size[1]
I = {i for i in range(size)}
status, x, y, z = glpk.lp(c, G, h, A, b, options=this_options)
if status == "optimal":
status, x = glpk.ilp(c, G, h, A, b, I=I, options=this_options)
if status == 'optimal':
pcost = blas.dot(c, x)
else:
pcost = None
return {'status': status, 'x': x, 'primal objective': pcost}
else:
return {'status': status, 'x': None, 'primal objective': None}
from cvxopt import glpk, matrix, solvers
import numpy as np
c = matrix([-4.0, 2.0, -5.0, -6.0, -7.0])
G = matrix([[2.0, 2.0, -4.0, 4.0, 8.0], [-2.0, -1.0, 2.0, 1.0, 3.0],
[5.0, -2.0, 4.0, 4.0, 2.0], [-5.0, 2.0, -4.0, -4.0, -2.0],
[2.0, -2.0, 5.0, 3.0, 1.0], [-1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, -1.0, 0.0, 0.0, 0.0], [0.0, 0.0, -1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, -1.0, 0.0], [0.0, 0.0, 0.0, 0.0, -1.0]])
h = matrix([6.0, 1.0, 5.0, -5.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0])
(stat1, primalx, primaly) = glpk.lp(c, G.T, h)
print("Optimal value for Primal:", c.T * primalx * -1)
print("Solution for Primal: \n", primalx)
c1 = matrix([6.0, -1.0, 5.0, 4.0])
G1 = matrix([[-2.0, -2.0, -5.0, -2.0], [-2.0, -1.0, 2.0, 2.0],
[4.0, 2.0, -4.0, -5.0], [-4.0, 1.0, -4.0, -3.0],
[-8.0, 3.0, -2.0, -1.0], [-1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, -1.0]])
h1 = matrix([-4.0, 2.0, -5.0, -6.0, -7.0, 0.0, 0.0, 0.0])
(stat1, dualx, dualy) = glpk.lp(c1, G1.T, h1)
print("Optimal value for Dual:", c1.T * dualx)
print("Solution for Dual: \n", dualx)
print("Complementary Slackness for x")
for i in range(5):
print((G1[:, i].T * dualx - h1[i]) * primalx[i])
def admit_outer(self, l_idx, W_old, c_old):
"""
Algorithm 1 in JYC (outer approximation of the APS monotone convex set-valued operator): Admissibility.
"""
N_player, N_profile = self.u_vecm.shape
h_l = self.H[l_idx,:] # Current spherical code, direction l
w_set = np.zeros((N_player,N_profile)) # Preallocate
cplus = np.zeros((N_profile,1))
w_min = np.amin(W_old, axis=0)
w_max = np.amax(W_old, axis=0)
# Convert to CVXOPT dtype='d' matrix (vector defaults to column vector)
H_set = matrix(self.H)
c_old_set = matrix(c_old)
F_set_ub = matrix(np.eye(N_player)) # Feasible hypercube: GLPK
F_set_lb = matrix(np.eye(N_player))
w_ub = matrix(w_max)
w_lb = matrix(w_min)
# Loop over all action profile
for a_idx in range(N_profile):
# Weighted value from action-promise pair (a,w)
u_now = (1-self.DELTA)*self.u_vecm[:,a_idx] # Current payoff
current_payoff = h_l.dot(u_now) # weighted
continue_payoff = h_l*self.DELTA # weighted
# Max-min value from current deviation to (a',w_min)
v_maxmin = (1.-self.DELTA)*self.u_vecm_deviate[:,a_idx] \
+ self.DELTA*w_min
# Deviation payoff gain
v_diff = matrix(u_now - v_maxmin)
# LP problem over promises w
IC_set = matrix([[-self.DELTA, 0. ], \
[0., -self.DELTA ]])
A = matrix([ H_set, \
IC_set, \
-F_set_lb, \
F_set_ub ])
b = matrix([ c_old_set, \
v_diff, \
-w_lb, \
w_ub ])
# Linear objective function: continue_payoff
c = matrix( continue_payoff )
# Put to GLPK!
glpk.options['msg_lev']='GLP_MSG_OFF'
sol=glpk.lp(-c,A,b)
if (sol[0] == 'optimal'):
w_opt = np.array(sol[1]).reshape(N_player) # optimizers
cplus[a_idx] = current_payoff + np.dot(c, w_opt)
w_set[:,a_idx] = w_opt # Store optimizers
exitflag = 0
else:
cplus[a_idx] = -1e5
exitflag = -1
return cplus, w_set, exitflag
def admit_inner(self, l_idx, ConvexHull_W_old):
"""Algorithm 3 (Step 1(a)) (Inner Monotone Hyperplane Approx.) in JYC's paper. Uses SCIPY.SPATIAL's ConvexHull implementation of the QHULL software."""
h_l = self.H[l_idx,:] # Current spherical code, direction l
N_player, N_profile = self.u_vecm.shape
Z_set = ConvexHull_W_old.points # Original points in W
vert_ind = ConvexHull_W_old.vertices # Extreme points/vertices
facet_eqn = ConvexHull_W_old.equations # Linear Inequalities for facets
# Worst values--attained at a vertex since co(W) is rep. by polytope
w_min = np.amin(Z_set[vert_ind,:], axis=0)
w_max = np.amax(Z_set[vert_ind,:], axis=0)
w_temp = np.zeros((N_player,N_profile))#np.tile(-np.inf, (N_player,N_profile)) # Preallocate
c_temp = np.zeros((N_profile,1)) #np.tile(-np.inf, (N_profile,1))#np.zeros((N_profile,1))
# Convert to CVXOPT dtype='d' matrix (vector defaults to column vector)
G_set = matrix(facet_eqn[:,0:N_player]) # Normals to facets of co(W)
#print np.sum(np.sum(G_set**2, axis=1))
c_set = matrix(-facet_eqn[:,-1]) # Levels of facets of co(W)
#print facet_eqn[:,-1].max(), facet_eqn[:,-1].min()
F_set_ub = matrix(np.eye(N_player)) # Feasible hypercubes: GLPK
F_set_lb = matrix(np.eye(N_player))
w_ub = matrix(w_max)
w_lb = matrix(w_min)
for a_idx in range(N_profile):
# Weighted value from action-promise pair (a,w)
u_now = (1-self.DELTA)*self.u_vecm[:,a_idx] # Current payoff
current_payoff = h_l.dot(u_now) # weighted
continue_payoff = h_l*self.DELTA # weighted
# Max-min value from current deviation to (a',w_min)
v_maxmin = (1.-self.DELTA)*self.u_vecm_deviate[:,a_idx] \
+ self.DELTA*w_min
# Deviation payoff gain
v_diff = matrix(u_now - v_maxmin)
# LP problem over promises w
IC_set = matrix([[-self.DELTA, 0. ], \
[0., -self.DELTA ]])
A = matrix([ G_set, \
IC_set, \
-F_set_lb, \
F_set_ub ])
b = matrix([ c_set, \
v_diff, \
-w_lb, \
w_ub ])
# Linear objective function: continue_payoff
c = matrix( continue_payoff )
# Put to GLPK!
glpk.options['msg_lev']='GLP_MSG_OFF'
sol=glpk.lp(-c,A,b)
if (sol[0] == 'optimal'):
w_opt = np.array(sol[1]).reshape(N_player) # optimizers
c_temp[a_idx] = current_payoff + np.dot(c, w_opt)
w_temp[:,a_idx] = w_opt # Store optimizers
exitflag = 0
else:
c_temp[a_idx] = -np.inf
exitflag = -1
return c_temp, w_temp, exitflag
[0.0, 0.0, 0.0, 0.0, 0.0, -1.0], [0.0, 0.0, 0.0, 0.0, 0.0, -1.0],
[0.0, 0.0, -1.0, 0.0, -1.0, 0.0]])
h = matrix([-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0])
(stat, sol) = glpk.ilp(c, G.T, h, I={0, 1, 2, 3, 4, 5}, B={0, 1, 2, 3, 4, 5})
print(sol)
print("Value of integer optimal solution: ", c.T * sol)
print("------------------------------------------------------------")
print("Solution to LP relaxation")
print("------------------------------------------------------------")
GLP = G.T
for i in range(6):
nc = np.zeros(6)
nc[i] = -1
nc2 = np.zeros(6)
nc2[i] = 1
GLP = np.vstack([GLP, nc])
GLP = np.vstack([GLP, nc])
h = np.vstack([h, 0])
h = np.vstack([h, 1])
hlp = matrix(h, tc='d')
GLP = matrix(GLP, tc='d')
(stat1, solx, soly) = glpk.lp(c, GLP, hlp)
print(solx)
print("Value of optimal solution: ", c.T * solx)
return round(x)
for i in range(nmbrOfNodes):
nc = np.zeros(nmbrOfNodes)
nc[i] = -1
nc2 = np.zeros(nmbrOfNodes)
nc2[i] = 1
G = np.vstack([G, nc])
G = np.vstack([G, nc])
h = np.vstack([h, 0])
h = np.vstack([h, 1])
GLP = matrix(G)
hlp = matrix(h)
(statlp, x, z) = glpk.lp(c, GLP, hlp)
print(np.sum(x))
print(correct_round(x))
print(np.array(x).T)
print("ROUNDED OPTIMAL SOLUTION GIVES SOLUTION:")
print(np.sum(correct_round(x)))
print("------------------------------------------------------------")
print("Solution by from given algorithm")
print("------------------------------------------------------------")
S = np.zeros(100)
for i in range(nmbrOfNodes):
for j in range(nmbrOfNodes - i): # Matrix is symmetric, hence we only need to look at
