def testqp(opts):
A = matrix([ [ .3, -.4, -.2, -.4, 1.3 ],
[ .6, 1.2, -1.7, .3, -.3 ],
[-.3, .0, .6, -1.2, -2.0 ] ])
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']})
def solve(opts):
c = matrix([-6., -4., -5.])
G = matrix([[
16., 7., 24., -8., 8., -1., 0., -1., 0., 0., 7., -5., 1., -5., 1., -7.,
1., -7., -4.
],
[
-14., 2., 7., -13., -18., 3., 0., 0., -1., 0., 3., 13.,
-6., 13., 12., -10., -6., -10., -28.
],
[
5., 0., -15., 12., -6., 17., 0., 0., 0., -1., 9., 6., -6.,
6., -7., -7., -6., -7., -11.
]])
h = matrix([
-3., 5., 12., -2., -14., -13., 10., 0., 0., 0., 68., -30., -19., -30.,
99., 23., -19., 23., 10.
])
A = matrix(0.0, (0, c.size[0]))
b = matrix(0.0, (0, 1))
dims = {'l': 2, 'q': [4, 4], 's': [3]}
#localcones.options.update(opts)
#sol = localcones.conelp(c, G, h, dims, kktsolver='qr')
solvers.options.update(opts)
sol = solvers.conelp(c, G, h, dims)
print("\nStatus: " + sol['status'])
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")
rungotest(sol)
def testgp(opts):
Aflr = 1000.0
Awall = 100.0
alpha = 0.5
beta = 2.0
gamma = 0.5
delta = 2.0
F = matrix( [[-1., 1., 1., 0., -1., 1., 0., 0.],
[-1., 1., 0., 1., 1., -1., 1., -1.],
[-1., 0., 1., 1., 0., 0., -1., 1.]])
g = log( matrix( [1.0, 2/Awall, 2/Awall, 1/Aflr, alpha, 1/beta, gamma,
1/delta]) )
K = [1, 2, 1, 1, 1, 1, 1]
solvers.options.update(opts)
sol = solvers.gp(K, F, g)
#localcvx.options.update(opts)
#sol = localcvx.gp(K, F, g, kktsolver='chol')
if sol['status'] == 'optimal':
x = sol['x']
print "x=\n", helpers.strSpe(x, "%.17f")
h, w, d = exp(x)
print("\n h = %f, w = %f, d = %f.\n" %(h,w,d))
print "\n *** running GO test ***"
helpers.run_go_test("../testgp", {'x': x})
def testcp(opts):
G = matrix(
[[0., -1., 0., 0., -21., -11., 0., -11., 10., 8., 0., 8., 5.],
[0., 0., -1., 0., 0., 10., 16., 10., -10., -10., 16., -10., 3.],
[0., 0., 0., -1., -5., 2., -17., 2., -6., 8., -17., -7., 6.]])
h = matrix(
[1.0, 0.0, 0.0, 0.0, 20., 10., 40., 10., 80., 10., 40., 10., 15.])
dims = {'l': 0, 'q': [4], 's': [3]}
if opts:
solvers.options.update(opts)
sol = solvers.cp(F, G, h, dims)
#sol = localcvx.cp(F, G, h, dims)
if sol['status'] == 'optimal':
print("\nx = \n")
print helpers.strSpe(sol['x'], "%.17f")
print helpers.strSpe(sol['znl'], "%.17f")
print "\n *** running GO test ***"
helpers.run_go_test("../testcp", {'x': sol['x']})
def testcp(opts):
G = matrix([
[0., -1., 0., 0., -21., -11., 0., -11., 10., 8., 0., 8., 5.],
[0., 0., -1., 0., 0., 10., 16., 10., -10., -10., 16., -10., 3.],
[0., 0., 0., -1., -5., 2., -17., 2., -6., 8., -17., -7., 6.]
])
h = matrix(
[1.0, 0.0, 0.0, 0.0, 20., 10., 40., 10., 80., 10., 40., 10., 15.])
dims = {'l': 0, 'q': [4], 's': [3]}
if opts:
solvers.options.update(opts)
sol = solvers.cp(F, G, h, dims)
#sol = localcvx.cp(F, G, h, dims)
if sol['status'] == 'optimal':
print("\nx = \n")
print helpers.strSpe(sol['x'], "%.17f")
print helpers.strSpe(sol['znl'], "%.17f")
print "\n *** running GO test ***"
helpers.run_go_test("../testcp", {'x': sol['x']})
def testqp(opts):
A = matrix([[0.3, -0.4, -0.2, -0.4, 1.3], [0.6, 1.2, -1.7, 0.3, -0.3], [-0.3, 0.0, 0.6, -1.2, -2.0]])
b = matrix([1.5, 0.0, -1.2, -0.7, 0.0])
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"]})
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")
def rungo(A, b, x, z):
print "\n ** running Go test ..."
helpers.run_go_test("../testqcl1", {'x': x, 'z': z, 'A': A, 'b': b})
setseed()
#m, n = 100, 100
m, n = 10, 10
A, b = normal(m, n), normal(m, 1)
run_go = True
if len(sys.argv[1:]) > 0 and sys.argv[1] == '-sp':
helpers.sp_reset("./sp.data")
helpers.sp_activate()
run_go = False
x, z = qcl1(A, b)
if x is None:
print("infeasible")
x = matrix(0.0, (0, 1))
z = matrix(0.0, (0, 1))
else:
print "x\n", helpers.str2(x, "%.9f")
print "z\n", helpers.str2(z, "%.9f")
if run_go:
rungo(A, b, x, z)
else:
print "A ", helpers.strSpe(A)
print "b ", helpers.strSpe(b)
print "\n ** running Go test ..."
helpers.run_go_test("../testqcl1", {'x': x, 'z': z, 'A': A, 'b': b})
setseed()
#m, n = 100, 100
m, n = 10, 10
A, b = normal(m,n), normal(m,1)
run_go = True
if len(sys.argv[1:]) > 0 and sys.argv[1] == '-sp':
helpers.sp_reset("./sp.data")
helpers.sp_activate()
run_go = False
x, z = qcl1(A, b)
if x is None:
print("infeasible")
x = matrix(0.0, (0, 1))
z = matrix(0.0, (0, 1))
else:
print "x\n", helpers.str2(x, "%.9f")
print "z\n", helpers.str2(z, "%.9f")
if run_go:
rungo(A, b, x, z)
else:
print "A ", helpers.strSpe(A)
print "b ", helpers.strSpe(b)
def solve(opts):
c = matrix([-6.0, -4.0, -5.0])
G = matrix(
[
[16.0, 7.0, 24.0, -8.0, 8.0, -1.0, 0.0, -1.0, 0.0, 0.0, 7.0, -5.0, 1.0, -5.0, 1.0, -7.0, 1.0, -7.0, -4.0],
[
-14.0,
2.0,
7.0,
-13.0,
-18.0,
3.0,
0.0,
0.0,
-1.0,
0.0,
3.0,
13.0,
-6.0,
13.0,
12.0,
-10.0,
-6.0,
-10.0,
-28.0,
],
[
5.0,
0.0,
-15.0,
12.0,
-6.0,
17.0,
0.0,
0.0,
0.0,
-1.0,
9.0,
6.0,
-6.0,
6.0,
-7.0,
-7.0,
-6.0,
-7.0,
-11.0,
],
]
)
h = matrix(
[
-3.0,
5.0,
12.0,
-2.0,
-14.0,
-13.0,
10.0,
0.0,
0.0,
0.0,
68.0,
-30.0,
-19.0,
-30.0,
99.0,
23.0,
-19.0,
23.0,
10.0,
]
)
A = matrix(0.0, (0, c.size[0]))
b = matrix(0.0, (0, 1))
dims = {"l": 2, "q": [4, 4], "s": [3]}
# localcones.options.update(opts)
# sol = localcones.conelp(c, G, h, dims, kktsolver='qr')
solvers.options.update(opts)
sol = solvers.conelp(c, G, h, dims)
print ("\nStatus: " + sol["status"])
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")
rungotest(sol)
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")
