def test_geodesics():
from scipy.linalg import expm
alpha = np.random.randint(1, 10, (2)) * .1
beta = alpha[1] * .1
m, d = (5, 3)
man = ComplexPositiveSemidefinite(m, d, alpha=alpha, beta=beta)
X = man.rand()
alf = alpha[1] / alpha[0]
def calc_gamma(man, X, xi, eta):
g_inv_Jst_solve_J_g_in_Jst_DJ = man.g_inv(
X, man.Jst(X, man.solve_J_g_inv_Jst(X, man.D_J(X, xi, eta))))
proj_christoffel = man.proj_g_inv(X, man.christoffel_form(X, xi, eta))
return g_inv_Jst_solve_J_g_in_Jst_DJ + proj_christoffel
eta = man.randvec(X)
g1 = calc_gamma(man, X, eta, eta)
g2 = man.christoffel_gamma(X, eta, eta)
print(man._vec(g1 - g2))
egrad = man._rand_ambient()
print(man.base_inner_ambient(g1, egrad))
print(man.rhess02_alt(X, eta, eta, egrad, 0))
print(man.rhess02(X, eta, eta, egrad, man.zerovec(X)))
# second solution:
A = X.Y.T.conj() @ eta.tY
t = 2
K = eta.tY - X.Y @ (X.Y.T.conj() @ eta.tY)
Yp, R = np.linalg.qr(K)
x_mat = np.bmat([[2 * alf * A, -R.T.conj()], [R, zeros((d, d))]])
Yt = np.bmat([X.Y, Yp]) @ expm(t*x_mat)[:, :d] @ \
expm(t*(1-2*alf)*A)
x_d_mat = x_mat[:, :d].copy()
x_d_mat[:d, :] += (1 - 2 * alf) * A
Ydt = np.bmat([X.Y, Yp]) @ expm(t*x_mat) @ x_d_mat @\
expm(t*(1-2*alf)*A)
x_dd_mat = x_mat @ x_d_mat + x_d_mat @ ((1 - 2 * alf) * A)
Yddt = np.bmat([X.Y, Yp]) @ expm(t*x_mat) @ x_dd_mat @\
expm(t*(1-2*alf)*A)
sqrtP = X.evec @ np.diag(np.sqrt(X.evl)) @ X.evec.T.conj()
isqrtP = X.evec @ np.diag(1 / np.sqrt(X.evl)) @ X.evec.T.conj()
Pinn = t * isqrtP @ eta.tP @ isqrtP
ePinn = expm(Pinn)
Pt = sqrtP @ ePinn @ sqrtP
Pdt = eta.tP @ isqrtP @ ePinn @ sqrtP
Pddt = eta.tP @ isqrtP @ ePinn @ isqrtP @ eta.tP
Xt = psd_point(np.array(Yt), np.array(Pt))
Xdt = psd_ambient(np.array(Ydt), np.array(Pdt))
Xddt = psd_ambient(np.array(Yddt), np.array(Pddt))
gcheck = Xddt + calc_gamma(man, Xt, Xdt, Xdt)
print(man._vec(gcheck))
Xt1 = man.exp(X, t * eta)
print((Xt1.Y - Xt.Y))
print((Xt1.P - Xt.P))
def v_func(S):
# a function from the manifold
# to ambient
csp = hsym((cc @ (S.P - S0.P).reshape(-1)).reshape(d, d))
return man.proj(
S,
psd_ambient((aa @ (S.Y - S0.Y).reshape(-1) + intc).reshape(n, d),
csp + p_intc))
def solve_dist_with_man(man, A, X0, maxiter, check_deriv=False):
import pymanopt
from pymanopt import Problem
from pymanopt.solvers import TrustRegions
@pymanopt.function.Callable
def cost(S):
"""
if not(S.P.dtype == np.float):
raise(ValueError("Non real"))
"""
diff = (A - S.Y @ S.P @ S.Y.T.conjugate())
val = trace(diff @ diff.T.conjugate()).real
# print('val=%f' % val)
return val
@pymanopt.function.Callable
def egrad(S):
return psd_ambient(-4 * A @ S.Y @ S.P,
2 * (S.P - S.Y.T.conjugate() @ A @ S.Y))
@pymanopt.function.Callable
def ehess(S, xi):
return psd_ambient(
-4 * A @ (xi.tY @ S.P + S.Y @ xi.tP),
2 * (xi.tP - xi.tY.T.conjugate() @ A @ S.Y -
S.Y.T.conjugate() @ A @ xi.tY))
if check_deriv:
xi = man.randvec(X0)
dlt = 1e-7
S1 = psd_point(X0.Y + dlt * xi.tY, X0.P + dlt * xi.tP)
print((cost(S1) - cost(X0)) / dlt)
print(man.base_inner_ambient(egrad(X0), xi))
h1 = egrad(S1) - egrad(X0)
h1 = psd_ambient(h1.tY / dlt, h1.tP / dlt)
h2 = ehess(X0, xi)
print(check_zero(h1.tY - h2.tY) + check_zero(h1.tP - h2.tP))
return
prob = Problem(man, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=maxiter, use_rand=False)
opt = solver.solve(prob, x=X0, Delta_bar=250)
return opt
def ehess(S, xi):
return psd_ambient(
-4 * A @ (xi.tY @ S.P + S.Y @ xi.tP),
2 * (xi.tP - xi.tY.T.conjugate() @ A @ S.Y -
S.Y.T.conjugate() @ A @ xi.tY))
def egrad(S):
return psd_ambient(-4 * A @ S.Y @ S.P,
2 * (S.P - S.Y.T.conjugate() @ A @ S.Y))
def eta_field(Sin):
return man.proj(
S,
psd_ambient(m1 @ (Sin.Y - S.Y) @ m2,
hsym((m_p @ (Sin.P - S.P).reshape(-1)).reshape(
d, d)))) + eeta
def omg_func(S):
csp = hsym((cc @ S.P.reshape(-1)).reshape(d, d))
return psd_ambient(
(aa @ S.Y.reshape(-1) + icpt.tY.reshape(-1)).reshape(n, d),
csp + icpt.tP)
def test_covariance_deriv():
# now test full:
# do covariant derivatives
# check that it works, preseving everything
n, d = (5, 3)
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .01
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
S = man.rand()
aa = crandn(n * d, n * d)
cc = crandn(d * d, d * d)
icpt = man._rand_ambient()
def omg_func(S):
csp = hsym((cc @ S.P.reshape(-1)).reshape(d, d))
return psd_ambient(
(aa @ S.Y.reshape(-1) + icpt.tY.reshape(-1)).reshape(n, d),
csp + icpt.tP)
xi = man.randvec(S)
egrad = omg_func(S)
ecsp = hsym((cc @ xi.tP.reshape(-1)).reshape(d, d))
ehess = psd_ambient((aa @ xi.tY.reshape(-1)).reshape(n, d), ecsp)
val1 = man.ehess2rhess(S, egrad, ehess, xi)
def rgrad_func(W):
return man.proj_g_inv(W, omg_func(W))
if False:
first = ehess
a = man.J_g_inv(S, egrad)
rgrad = man.proj_g_inv(S, egrad)
second = man.D_g(S, xi, man.g_inv(S, egrad)).scalar_mul(-1)
aout = man.solve_J_g_inv_Jst(S, a)
third = man.proj(S, man.D_g_inv_Jst(S, xi, aout)).scalar_mul(-1)
fourth = man.christoffel_form(S, xi, rgrad)
val1a1 = man.proj_g_inv(S, first + second + fourth) + third
print(check_zero(man._vec(val1 - val1a1)))
elif True:
d_xi_rgrad = num_deriv_amb(man, S, xi, rgrad_func)
rgrad = man.proj_g_inv(S, egrad)
fourth = man.christoffel_form(S, xi, rgrad)
val1a = man.proj(S, d_xi_rgrad) + man.proj_g_inv(S, fourth)
print(check_zero(man._vec(val1 - val1a)))
# nabla_v_xi, dxi, cxxi
val2a, _, _ = calc_covar_numeric(man, S, xi, omg_func)
val2, _, _ = calc_covar_numeric(man, S, xi, rgrad_func)
# val2_p = project(prj, val2)
val2_p = man.proj(S, val2)
# print(val1)
# print(val2_p)
print(check_zero(man._vec(val1) - man._vec(val2_p)))
if True:
H = xi
valrangeA_ = ehess + man.g(S, man.D_proj(
S, H, man.g_inv(S, egrad))) - man.D_g(
S, H, man.g_inv(S, egrad)) +\
man.christoffel_form(S, H, man.proj_g_inv(S, egrad))
valrangeB = man.proj_g_inv(S, valrangeA_)
valrange = man.ehess2rhess_alt(S, egrad, ehess, xi)
print(check_zero(man._vec(valrange) - man._vec(val2_p)))
print(check_zero(man._vec(valrange) - man._vec(val1)))
print(check_zero(man._vec(valrange) - man._vec(valrangeB)))
def xi_func(S):
csp_xi = hsym((cc_xi @ (S.P - S0.P).reshape(-1)).reshape(d, d))
xi_amb = psd_ambient((aa_xi @ (S.Y - S0.Y).reshape(-1) +
inct_xi.tY.reshape(-1)).reshape(n, d),
csp_xi + inct_xi.tP)
return man.proj(S, xi_amb)
def v_func_flat(S):
# a function from the manifold
# to ambient
csp = hsym((cc @ S.P.reshape(-1)).reshape(d, d))
return psd_ambient((aa @ S.Y.reshape(-1) + bb).reshape(n, d), csp + dd)
