x0 = matrix(-lmbda[0]+1.0, (n,1))
s0 = +w
s0[::n+1] += x0
# Initial feasible z is identity.
z0 = matrix(0.0, (n,n))
z0[::n+1] = 1.0
dims = {'l': 0, 'q': [], 's': [n]}
sol = solvers.conelp(c, Fs, w[:], dims, kktsolver = Fkkt,
primalstart = {'x': x0, 's': s0[:]}, dualstart = {'z': z0[:]})
return sol['x'], matrix(sol['z'], (n,n))
n = 10
w = normal(n,n)
run_go = True
if len(sys.argv[1:]) > 0:
if sys.argv[1] == "-sp":
helpers.sp_reset("./sp.data")
helpers.sp_activate()
run_go = False
x, z = mcsdp(w)
if run_go:
print "\n *** running GO test ***"
helpers.run_go_test("../testmcsdp", {'x': x, 'z': z, 'data': w})
else:
print "w ", helpers.strSpe(w, "%.17f")
#print "x=\n", helpers.strSpe(x, "%.17f")
#print "z=\n", helpers.strSpe(z, "%.17f")
b = matrix([ 1.5, .0, -1.2, -.7, .0])
m, n = A.size
I = matrix(0.0, (n,n))
I[::n+1] = 1.0
G = matrix([-I, matrix(0.0, (1,n)), I])
h = matrix(n*[0.0] + [1.0] + n*[0.0])
dims = {'l': n, 'q': [n+1], 's': []}
P = A.T*A
q = -A.T*b
#localcones.options.update(opts)
#sol = localcones.coneqp(P, q, G, h, dims, kktsolver='chol')
solvers.options.update(opts)
sol = solvers.coneqp(P, q, G, h, dims)
if sol['status'] == 'optimal':
print "x=\n", helpers.strSpe(sol['x'], "%.5f")
print "s=\n", helpers.strSpe(sol['s'], "%.5f")
print "z=\n", helpers.strSpe(sol['z'], "%.5f")
print "\n *** running GO test ***"
helpers.run_go_test("../testconeqp", {'x': sol['x'], 's': sol['s'], 'z': sol['z']})
if len(sys.argv[1:]) > 0:
if sys.argv[1] == "-sp":
helpers.sp_reset("./sp.data")
helpers.sp_activate()
testqp({'maxiters': 20})
