def test_projection(man, Y):
print("test projection")
N = 10
ret = np.zeros(N)
for i in range(N):
U = man._rand_ambient()
Upr = man.proj(Y, U)
ret[i] = check_zero(man._vec_range_J(man.J(Y, Upr)))
# print(ret)
if check_zero(ret) > 1e-9:
print("not all works")
else:
print("All good")
print("test randvec")
ret_pr = np.zeros(N)
for i in range(N):
XX = man.rand()
H = man.randvec(XX)
U = man._rand_ambient()
Upr = man.proj(XX, U)
ret_pr[i] = man.inner(XX, U, H) - man.inner(XX, Upr, H)
ret[i] = (check_zero(XX.T.conjugate() @ H + H.T.conjugate() @ XX))
print(ret)
if check_zero(ret) > 1e-9:
print("not all works")
else:
print("All good")
print("check inner of projection = inner of original")
print(ret_pr)
if check_zero(ret_pr) > 1e-9:
print("not all works")
else:
print("All good")
def test_N_proj():
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 1)[0] * .02
n = 10
d = 6
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
S = man.rand()
"""
def proj_range_alt(man, S, U):
# projection. U is in ambient
# return one in tangent
al1 = man.alpha[1]
beta = man.beta
YTU = [email protected]
D0 = sym(U.tP + [email protected] - S.P@YTU)
D = _extended_lyapunov(al1, beta, S.P, D0, S.evl, S.evec)
return psd_ambient(
beta*S.Y@(S.Pinv@[email protected]) + U.tY - S.Y@([email protected]), al1*D)
"""
U = man.randvec(S)
Upr1 = super(ComplexPositiveSemidefinite, man).proj(S, U)
Upr2 = man.proj(S, U)
Upr3 = man.proj_range_alt(S, U)
print(check_zero(Upr1.tP - Upr2.tP))
print(check_zero(Upr1.tY - Upr2.tY))
print(check_zero(Upr1.tY - Upr3.tY))
print(check_zero(Upr1.tP - Upr3.tP))
def test_vectorize():
from ManNullRange.manifolds.tools import cvech, cunvech, cvecah, cunvecah
p = 10
mat = crandn(p, p)
mat += mat.T.conjugate()
v = cvech(mat)
mat2 = cunvech(v)
print(check_zero(mat - mat2))
# check inner product:
for i in range(p * p):
v = zeros(p * p)
v[i] = 1
h = cunvech(v)
assert (np.abs(trace(h @ h.T.conjugate()).real - 1) < 1e-10)
# now do skew
mat = crandn(p, p)
mat -= mat.T.conjugate()
v = cvecah(mat)
mat2 = cunvecah(v)
print(check_zero(mat - mat2))
for i in range(p * p):
v = zeros(p * p)
v[i] = 1
h = cunvecah(v)
assert (np.abs(trace(h @ h.T.conjugate()).real - 1) < 1e-10)
def test_covariance_deriv():
# now test full:
# do covariant derivatives
# check that it works, preseving everything
dvec = np.array([10, 3, 2, 3])
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
man = ComplexFlag(dvec, alpha=alpha)
n = man.n
d = man.d
Y = man.rand()
slp = np.random.randn(n * d)
aa = np.random.randn(n * d, n * d)
def omg_func(Y):
return (aa @ Y.reshape(-1) + slp).reshape(n, d)
xi = man.randvec(Y)
egrad = omg_func(Y)
ehess = (aa @ xi.reshape(-1)).reshape(n, d)
val1 = man.ehess2rhess(Y, egrad, ehess, xi)
def rgrad_func(W):
return man.proj_g_inv(W, omg_func(W))
if False:
d_xi_rgrad = num_deriv(man, Y, xi, rgrad_func)
rgrad = man.proj_g_inv(Y, egrad)
fourth = man.christoffel_form(Y, xi, rgrad)
val1c = man.proj(Y, d_xi_rgrad) + man.proj_g_inv(Y, fourth)
if False:
first = ehess
a = man.J(Y, man.g_inv(Y, egrad))
rgrad = man.proj_g_inv(Y, egrad)
second = -man.D_g(Y, xi, man.g_inv(Y, egrad))
aout = man.solve_J_g_inv_Jst(Y, a)
third = -man.proj(Y, man.D_g_inv_Jst(Y, xi, aout))
fourth = man.christoffel_form(Y, xi, rgrad)
val1a = man.proj_g_inv(Y, first + second + fourth) + third
d_xi_rgrad = num_deriv(man, Y, xi, rgrad_func)
rgrad = man.proj_g_inv(Y, egrad)
fourth = man.christoffel_form(Y, xi, rgrad)
val1b = man.proj(Y, d_xi_rgrad) + man.proj_g_inv(Y, fourth)
print(check_zero(val1 - val1b))
# nabla_v_xi, dxi, cxxi
# val2a, _, _ = calc_covar_numeric(man, Y, xi, omg_func)
val2, _, _ = calc_covar_numeric(man, Y, xi, rgrad_func)
# val2_p = project(prj, val2)
val2_p = man.proj(Y, val2)
# print(val1)
# print(val2_p)
print(check_zero(val1 - val2_p))
def test_covariance_deriv():
# now test full:
# do covariant derivatives
# check that it works, preseving everything
n, d = (5, 3)
A = make_sym_pos(n)
B = make_sym_pos(d)
man = RealStiefelWoodbury(n, d, A=A, B=B)
Y = man.rand()
slp = np.random.randn(n * d)
aa = np.random.randn(n * d, n * d)
def omg_func(Y):
return (aa @ Y.reshape(-1) + slp).reshape(n, d)
xi = man.randvec(Y)
egrad = omg_func(Y)
ehess = (aa @ xi.reshape(-1)).reshape(n, d)
val1 = man.ehess2rhess(Y, egrad, ehess, xi)
if False:
val1a = man.ehess2rhess_alt(Y, egrad, ehess, xi)
print(check_zero(val1 - val1a))
def rgrad_func(W):
return man.proj_g_inv(W, omg_func(W))
if False:
first = ehess
a = man.J(Y, man.g_inv(Y, egrad))
rgrad = man.proj_g_inv(Y, egrad)
second = -man.D_g(Y, xi, man.g_inv(Y, egrad))
aout = man.solve_J_g_inv_Jst(Y, a)
third = -man.proj(Y, man.D_g_inv_Jst(Y, xi, aout))
fourth = man.christoffel_form(Y, xi, rgrad)
val1a = man.proj_g_inv(Y, first + second + fourth) + third
d_xi_rgrad = num_deriv(man, Y, xi, rgrad_func)
rgrad = man.proj_g_inv(Y, egrad)
fourth = man.christoffel_form(Y, xi, rgrad)
val1b = man.proj(Y, d_xi_rgrad) + man.proj_g_inv(Y, fourth)
print(check_zero(val1 - val1b))
# nabla_v_xi, dxi, cxxi
val2a, _, _ = calc_covar_numeric(man, Y, xi, omg_func)
val2, _, _ = calc_covar_numeric(man, Y, xi, rgrad_func)
# val2_p = project(prj, val2)
val2_p = man.proj(Y, val2)
# print(val1)
# print(val2_p)
print(val1 - val2_p)
def testN(man, X):
m, n, p = (man.m, man.n, man.p)
B = crandn(m-p, p)
C = crandn(n-p, p)
D = crandn(p, p)
ee = N(man, X, B, C, D)
print(check_zero(stU(X, ee)))
print(check_zero(stV(X, ee)))
print(check_zero(symP(X, ee)))
print(check_zero(Hz(man, X, ee)))
nmat = make_N_mat(man, X)
ee2 = man._unvec(nmat @ np.concatenate(
[cvec(B), cvec(C), cvec(D)]))
print(check_zero(man._vec(ee - ee2)))
def testN(man, X):
m, n, p = (man.m, man.n, man.p)
B = randn(m - p, p)
C = randn(n - p, p)
D = randn(p, p)
ee = N(man, X, B, C, D)
print(check_zero(stU(X, ee)))
print(check_zero(stV(X, ee)))
print(check_zero(symP(X, ee)))
print(check_zero(Hz(man, X, ee)))
nmat = make_N_mat(man, X)
ee2 = man._unvec(nmat @ np.concatenate(
[B.reshape(-1), C.reshape(-1),
D.reshape(-1)]))
print(check_zero(man._vec(ee - ee2)))
def testNTgN(man, X):
m, n, p = (man.m, man.n, man.p)
B0 = crandn(m-p, p)
C0 = crandn(n-p, p)
D0 = crandn(p, p)
out1 = NTgN @ np.concatenate(
[cvec(B0), cvec(C0), cvec(D0)])
out2a = NTgN_opt(X, B0, C0, D0)
out2 = np.concatenate(
[cvec(out2a[0]), cvec(out2a[1]), cvec(out2a[2])])
print(check_zero(out1-out2))
out2b = solveNTgN(X, *out2a)
print(check_zero(out2b[2]-D0))
print(check_zero(out2b[1]-C0))
print(check_zero(out2b[0]-B0))
def testNTgN(man, X):
m, n, p = (man.m, man.n, man.p)
B0 = randn(m - p, p)
C0 = randn(n - p, p)
D0 = randn(p, p)
out1 = NTgN @ np.concatenate(
[B0.reshape(-1), C0.reshape(-1),
D0.reshape(-1)])
out2a = NTgN_opt(X, B0, C0, D0)
out2 = np.concatenate(
[out2a[0].reshape(-1), out2a[1].reshape(-1), out2a[2].reshape(-1)])
print(check_zero(out1 - out2))
out2b = solveNTgN(X, *out2a)
print(check_zero(out2b[2] - D0))
print(check_zero(out2b[1] - C0))
print(check_zero(out2b[0] - B0))
def test_christ_flat():
"""now test that christofel preserve metrics:
on the flat space
d_xi <v M v> = 2 <v M nabla_xi v>
v = proj(W) @ (aa W + b)
"""
alpha = randint(1, 10, 2) * .1
gamma = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .1
m = 4
n = 5
p = 3
man = ComplexFixedRank(m, n, p, alpha=alpha, beta=beta, gamma=gamma)
S = man.rand()
xi = man.randvec(S)
aaU = crandn(m*p, m*p)
bbU = crandn(m*p)
aaV = crandn(n*p, n*p)
bbV = crandn(n*p)
cc = crandn(p*p, p*p)
dd = hsym(crandn(p, p))
def v_func_flat(S):
# a function from the manifold
# to ambient
csp = hsym((cc @ S.P.reshape(-1)).reshape(p, p))
return fr_ambient(
(aaU @ S.U.reshape(-1) + bbU).reshape(m, p),
(aaV @ S.V.reshape(-1) + bbV).reshape(n, p),
csp + dd)
vv = v_func_flat(S) # vv is not horizontal
dlt = 1e-7
Snew = fr_point(
S.U + dlt * xi.tU,
S.V + dlt * xi.tV,
S.P + dlt * xi.tP)
vnew = v_func_flat(Snew)
val = man.inner(S, vv)
valnew = man.inner(Snew, vnew)
d1 = (valnew - val)/dlt
dv = (vnew - vv).scalar_mul(1/dlt)
nabla_xi_v = dv + man.g_inv(
S, man.christoffel_form(S, xi, vv))
# not equal bc vv is not horizontal:
nabla_xi_va = dv + man.g_inv(
S, super(ComplexFixedRank, man).christoffel_form(S, xi, vv))
print(check_zero(man._vec(nabla_xi_v) - man._vec(nabla_xi_va)))
d2 = man.inner(S, vv, nabla_xi_v)
print(d1)
print(2*d2)
def diff_i_j(i, j):
U = zeros_like(Y)
U[ii, jj] = 1
ju = man.J(Y, U)
asum = 0
gdc = man._g_idx
for r, s in ju:
br, er = gdc[r]
if r == s:
asum += check_zero(ju[r, r] - alpha[r - 1, r] *
Y[:, br:er].T.conjugate() @ U[:, br:er])
else:
bs, es = gdc[s]
asum += check_zero(ju[r, s] -
Y[:, br:er].T.conjugate() @ U[:, bs:es] -
U[:, br:er].T.conjugate() @ Y[:, bs:es])
return asum / len(ju)
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 test_Jst(man, S, jmat):
print("test JST")
for ii in range(10):
a = man._rand_range_J()
avec = man._vec_range_J(a)
jtout = jmat.T @ avec
jtout2 = man._vec(man.Jst(S, a))
# print(np.where(np.abs(jtout - jtout2) > 1e-9))
diff = check_zero(jtout - jtout2)
print(diff)
def test_Jst(man, Y, jmat):
print("test JST")
for ii in range(10):
a = man._rand_range_J()
avec = man._vec_range_J(a)
jtout = man._unvec(jmat.T.conjugate() @ avec)
jtout2 = man.Jst(Y, a)
# print(np.where(np.abs(jtout - jtout2) > 1e-9))
diff = check_zero(jtout - jtout2)
print(diff)
def test_chris_vectorfields():
# now test that it works on embedded metrics
# we test that D_xi (eta g eta) = 2(eta g nabla_xi eta)
n, d = (20, 3)
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 1)[0] * .1
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
S0 = man.rand()
aa = crandn(n * d, n * d)
intc = crandn(n * d)
cc = crandn(d * d, d * d)
p_intc = hsym(crandn(d, d))
inct_xi = man._rand_ambient()
aa_xi = crandn(n * d, n * d)
cc_xi = crandn(d * d, d * d)
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))
SS = psd_point(S0.Y, S0.P)
xi = man.proj(SS, inct_xi)
nabla_xi_v, dv, cxv = calc_covar_numeric(man, SS, xi, v_func)
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)
vv = v_func(SS)
nabla_v_xi, dxi, cxxi = calc_covar_numeric(man, SS, vv, xi_func)
diff = nabla_xi_v - nabla_v_xi
print(diff.tY, diff.tP)
# now do Lie bracket:
dlt = 1e-7
SnewXi = psd_point(SS.Y + dlt * xi.tY, SS.P + dlt * xi.tP)
Snewvv = psd_point(SS.Y + dlt * vv.tY, SS.P + dlt * vv.tP)
vnewxi = v_func(SnewXi)
xnewv = xi_func(Snewvv)
dxiv = (vnewxi - vv).scalar_mul(1 / dlt)
dvxi = (xnewv - xi).scalar_mul(1 / dlt)
diff2 = man.proj(SS, dxiv - dvxi)
print(check_zero(man._vec(diff) - man._vec(diff2)))
def test_chris_vectorfields():
# now test that it works on embedded metrics
# we test that D_xi (eta g eta) = 2(eta g nabla_xi eta)
dvec = np.array([10, 3, 2, 3])
p = dvec.shape[0]-1
alpha = randint(1, 10, (p, p+1)) * .1
man = RealFlag(dvec, alpha=alpha)
n = man.n
d = man.d
slp = randn(n*d)
Y0 = man.rand()
slpxi = randn(n*d)
aa = randn(n*d, n*d)
aaxi = randn(n*d, n*d)
def v_func(Y):
return man.proj(Y, (aa @ (Y-Y0).reshape(-1)
+ slp).reshape(n, d))
YY = Y0.copy()
xi = man.proj(YY, slpxi.reshape(n, d))
nabla_xi_v, dv, cxv = calc_covar_numeric(
man, YY, xi, v_func)
def xi_func(Y):
return man.proj(Y, (aaxi @ (Y-Y0).reshape(-1)
+ slpxi).reshape(n, d))
vv = v_func(YY)
nabla_v_xi, dxi, cxxi = calc_covar_numeric(
man, YY, vv, xi_func)
diff = nabla_xi_v - nabla_v_xi
# print(diff)
# now do Lie bracket:
dlt = 1e-7
YnewXi = YY + dlt * xi
Ynewvv = YY + dlt * vv
vnewxi = v_func(YnewXi)
xnewv = xi_func(Ynewvv)
dxiv = (vnewxi - vv)/dlt
dvxi = (xnewv - xi)/dlt
diff2 = man.proj(YY, dxiv-dvxi)
print(check_zero(diff - diff2))
def test_christ_flat():
"""now test that christofel preserve metrics:
on the flat space
d_xi <v M v> = 2 <v M nabla_xi v>
v = proj(W) @ (aa W + b)
"""
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .1
n = 20
d = 3
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
S = man.rand()
xi = man.randvec(S)
xi = man.randvec(S)
aa = crandn(n * d, n * d)
bb = crandn(n * d)
cc = crandn(d * d, d * d)
dd = hsym(crandn(d, d))
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)
vv = v_func_flat(S)
dlt = 1e-7
Snew = psd_point(S.Y + dlt * xi.tY, S.P + dlt * xi.tP)
vnew = v_func_flat(Snew)
val = man.inner_product_amb(S, vv)
valnew = man.inner_product_amb(Snew, vnew)
d1 = (valnew - val) / dlt
dv = (vnew - vv).scalar_mul(1 / dlt)
nabla_xi_v = dv + man.g_inv(S, man.christoffel_form(S, xi, vv))
nabla_xi_va = dv + man.g_inv(
S,
super(ComplexPositiveSemidefinite, man).christoffel_form(S, xi, vv))
print(check_zero(man._vec(nabla_xi_v) - man._vec(nabla_xi_va)))
d2 = man.inner(S, vv, nabla_xi_v)
print(d1)
print(2 * d2)
def test_projection(man, X):
print("test projection")
NN = 10
ret = np.zeros(NN)
for i in range(NN):
omg = man._rand_ambient()
Upr = man.proj(X, omg)
ret[i] = check_zero(
np.concatenate([
stU(X, Upr).reshape(-1),
stV(X, Upr).reshape(-1),
symP(X, Upr).reshape(-1),
Hz(man, X, Upr).reshape(-1)
]))
print(ret)
if check_zero(ret) > 1e-8:
print("not all works")
else:
print("All good")
print("test randvec")
ret_pr = np.zeros(NN)
for i in range(NN):
XX = man.rand()
H = man.randvec(XX)
Ue = man._rand_ambient()
Upr = man.proj(XX, Ue)
ret_pr[i] = man.inner(XX, Ue, H) - man.inner(XX, Upr, H)
ret[i] = check_zero(stU(XX, H)) + check_zero(stV(XX, H)) +\
check_zero(symP(XX, H)) + check_zero(Hz(man, XX, H))
print(ret)
if check_zero(ret) > 1e-9:
print("not all works")
else:
print("All good")
print("check inner of projection = inner of original")
print(ret_pr)
if check_zero(ret_pr) > 1e-8:
print("not all works")
else:
print("All good")
def test_lyapunov():
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 1)[0] * .02
n = 5
d = 3
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
S = man.rand()
P = S.P
B = crandn(d, d)
alpha1 = alpha[1]
def L(X, P):
Piv = la.inv(P)
return (alpha1 - 2 * beta) * X + beta * (P @ X @ Piv + Piv @ X @ P)
X = extended_lyapunov(alpha1, beta, P, B)
# L(X, P)
print(check_zero(B - L(X, P)))
def test_chris_vectorfields():
# now test that it works on embedded metrics
# we test that D_xi (eta g eta) = 2(eta g nabla_xi eta)
n, d = (5, 3)
A = make_sym_pos(n)
B = make_sym_pos(d)
man = RealStiefelWoodbury(n, d, A=A, B=B)
slp = randn(n * d)
Y0 = man.rand()
slpxi = randn(n * d)
aa = randn(n * d, n * d)
aaxi = randn(n * d, n * d)
def v_func(Y):
return man.proj(Y, (aa @ (Y - Y0).reshape(-1) + slp).reshape(n, d))
YY = Y0.copy()
xi = man.proj(YY, slpxi.reshape(n, d))
nabla_xi_v, dv, cxv = calc_covar_numeric(man, YY, xi, v_func)
def xi_func(Y):
return man.proj(Y, (aaxi @ (Y - Y0).reshape(-1) + slpxi).reshape(n, d))
vv = v_func(YY)
nabla_v_xi, dxi, cxxi = calc_covar_numeric(man, YY, vv, xi_func)
diff = nabla_xi_v - nabla_v_xi
print(diff)
# now do Lie bracket:
dlt = 1e-7
YnewXi = YY + dlt * xi
Ynewvv = YY + dlt * vv
vnewxi = v_func(YnewXi)
xnewv = xi_func(Ynewvv)
dxiv = (vnewxi - vv) / dlt
dvxi = (xnewv - xi) / dlt
diff2 = man.proj(YY, dxiv - dvxi)
print(check_zero(diff - diff2))
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 test_chris_vectorfields():
# now test that it works on embedded metrics
# we test that D_xi (eta g eta) = 2(eta g nabla_xi eta)
alpha = randint(1, 10, 2) * .1
gamma = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .1
m = 4
n = 5
p = 3
man = ComplexFixedRank(m, n, p, alpha=alpha, beta=beta, gamma=gamma)
S0 = man.rand()
aaU = crandn(m*p, m*p)
intcU = crandn(m*p)
aaV = crandn(n*p, n*p)
intcV = crandn(n*p)
cc = crandn(p*p, p*p)
p_intc = hsym(crandn(p, p))
inct_xi = man._rand_ambient()
aa_xiU = crandn(m*p, m*p)
aa_xiV = crandn(n*p, n*p)
cc_xi = crandn(p*p, p*p)
def v_func(S):
# a function from the manifold
# to ambient
csp = hsym((cc @ (S.P-S0.P).reshape(-1)).reshape(p, p))
return man.proj(S, fr_ambient(
(aaU @ (S.U-S0.U).reshape(-1) + intcU).reshape(m, p),
(aaV @ (S.V-S0.V).reshape(-1) + intcV).reshape(n, p),
csp + p_intc))
SS = fr_point(S0.U, S0.V, S0.P)
xi = man.proj(SS, inct_xi)
nabla_xi_v, dv, cxv = calc_covar_numeric(
man, SS, xi, v_func)
def xi_func(S):
csp_xi = hsym((cc_xi @ (S.P-S0.P).reshape(-1)).reshape(p, p))
xi_amb = fr_ambient(
(aa_xiU @ (S.U-S0.U).reshape(-1) +
inct_xi.tU.reshape(-1)).reshape(m, p),
(aa_xiV @ (S.V-S0.V).reshape(-1) +
inct_xi.tV.reshape(-1)).reshape(n, p),
csp_xi + inct_xi.tP)
return man.proj(S, xi_amb)
vv = v_func(SS)
nabla_v_xi, dxi, cxxi = calc_covar_numeric(
man, SS, vv, xi_func)
diff = nabla_xi_v - nabla_v_xi
print(diff.tU, diff.tV, diff.tP)
# now do Lie bracket:
dlt = 1e-7
SnewXi = fr_point(SS.U+dlt*xi.tU,
SS.V+dlt*xi.tV,
SS.P+dlt*xi.tP)
Snewvv = fr_point(SS.U+dlt*vv.tU,
SS.V+dlt*vv.tV,
SS.P+dlt*vv.tP)
vnewxi = v_func(SnewXi)
xnewv = xi_func(Snewvv)
dxiv = (vnewxi - vv).scalar_mul(1/dlt)
dvxi = (xnewv - xi).scalar_mul(1/dlt)
diff2 = man.proj(SS, dxiv-dvxi)
print(check_zero(man._vec(diff) - man._vec(diff2)))
def test_all_projections():
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 1)[0] * .02
n = 5
d = 3
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
print(man)
S = man.rand()
test_inner(man, S)
test_J(man, S)
# now check metric, Jst etc
# check Jst: vectorize the operator J then compare Jst with jmat.T
jmat = make_j_mat(man, S)
test_Jst(man, S, jmat)
ginv_mat = make_g_inv_mat(man, S)
ee = man._rand_ambient()
g1 = man._unvec(ginv_mat @ man._vec(ee))
print(check_zero(man.g_inv(S, ee).tP - g1.tP))
print(check_zero(man.g_inv(S, ee).tY - g1.tY))
# test g_inv_Jst
for ii in range(10):
a = man._rand_range_J()
avec = man._vec_range_J(a)
jtout = ginv_mat @ jmat.T @ avec
jtout2 = man._vec(man.g_inv_Jst(S, a))
diff = check_zero(jtout - jtout2)
print(diff)
# test projection
test_projection(man, S)
# now diff projection
for i in range(1):
e = man._rand_ambient()
S1 = man.rand()
xi = man.randvec(S1)
dlt = 1e-7
S2 = psd_point(S1.Y + dlt * xi.tY, S1.P + dlt * xi.tP)
# S = psd_point(S1.Y, S1.P)
d1P = (man.proj(S2, e).tP - man.proj(S1, e).tP) / dlt
d1Y = (man.proj(S2, e).tY - man.proj(S1, e).tY) / dlt
d2 = man.D_proj(S1, xi, e)
print(check_zero(d1P - d2.tP) + check_zero(d1Y - d2.tY))
for i in range(10):
a = man._rand_range_J()
eta = man._rand_ambient()
print(man.base_inner_ambient(eta, man.Jst(S, a)))
print((trace((eta.tP.T.conjugate() - eta.tP) @ a['P'] +
(man.alpha[1] * eta.tY.T.conjugate() @ S.Y + man.beta *
(S.Pinv @ eta.tP.T.conjugate() -
eta.tP.T.conjugate() @ S.Pinv)) @ a['YP'])).real)
print((trace(2 * eta.tP.T.conjugate() @ a['P']) + trace(
(man.alpha[1] * eta.tY.T.conjugate() @ S.Y + man.beta *
(S.Pinv @ eta.tP.T.conjugate() - eta.tP.T.conjugate() @ S.Pinv))
@ a['YP'])).real)
print((trace(
eta.tP.T.conjugate() @ (2 * a['P'] + man.beta *
(a['YP'] @ S.Pinv - S.Pinv @ a['YP']))) +
trace(eta.tY.T.conjugate() @ (man.alpha[1] * S.Y @ a['YP']))
).real)
print(man.base_inner_E_J(man.J(S, eta), a))
print((
trace((eta.tP - eta.tP.T.conjugate()).T.conjugate() @ a['P'] +
(man.alpha[1] * S.Y.T.conjugate() @ eta.tY + man.beta *
(eta.tP @ S.Pinv - S.Pinv @ eta.tP)).T.conjugate() @ a['YP']
)).real)
for i in range(10):
a = man._rand_range_J()
beta = man.beta
alf = man.alpha
anew1 = man.J(S, man.g_inv_Jst(S, a))
anew = {}
saYP = a['YP'] + a['YP'].T.conjugate()
anew['P'] = 4 / beta * S.P @ a['P'] @ S.P + S.P @ saYP - saYP @ S.P
anew['YP'] = alf[1] * a['YP'] + beta * (
((2 / man.beta) * S.P @ a['P'] @ S.P + S.P @ a['YP'] -
a['YP'] @ S.P) @ S.Pinv - S.Pinv @ (
(2 / man.beta) * S.P @ a['P'] @ S.P + S.P @ a['YP'] -
a['YP'] @ S.P))
anew['YP'] = alf[1] * a['YP'] + (
(2 * S.P @ a['P'] + beta * S.P @ a['YP'] @ S.Pinv - beta * a['YP'])
- (2 * a['P'] @ S.P + beta * a['YP'] -
beta * S.Pinv @ a['YP'] @ S.P))
anew['YP'] = (alf[1] - 2 * beta) * a['YP'] + (
(2 * S.P @ a['P'] + beta * S.P @ a['YP'] @ S.Pinv) -
(2 * a['P'] @ S.P - beta * S.Pinv @ a['YP'] @ S.P))
anew['YP'] = (alf[1] - 2 * beta) * a['YP'] + (
(2 * S.P @ a['P'] - 2 * a['P'] @ S.P +
beta * S.P @ a['YP'] @ S.Pinv + beta * S.Pinv @ a['YP'] @ S.P))
print(check_zero(man._vec_range_J(anew1) - man._vec_range_J(anew)))
for i in range(10):
a = man._rand_range_J()
b1 = man.J(S, man.g_inv_Jst(S, a))
b2 = man.J_g_inv_Jst(S, a)
print(check_zero(man._vec_range_J(b1) - man._vec_range_J(b2)))
a1 = man.solve_J_g_inv_Jst(S, b1)
print(check_zero(man._vec_range_J(a) - man._vec_range_J(a1)))
for ii in range(10):
E = man._rand_ambient()
a2 = man.J_g_inv(S, E)
a1 = man.J(S, man.g_inv(S, E))
print(check_zero(man._vec_range_J(a1) - man._vec_range_J(a2)))
for i in range(20):
Uran = man._rand_ambient()
Upr = man.proj(S, man.g_inv(S, Uran))
Upr2 = man.proj_g_inv(S, Uran)
print(check_zero(man._vec(Upr) - man._vec(Upr2)))
for ii in range(10):
a = man._rand_range_J()
xi = man.randvec(S)
jtout2 = man.Jst(S, a)
dlt = 1e-7
Snew = psd_point(S.Y + dlt * xi.tY, S.P + dlt * xi.tP)
jtout2a = man.Jst(Snew, a)
d1 = (jtout2a - jtout2).scalar_mul(1 / dlt)
d2 = man.D_Jst(S, xi, a)
print(check_zero(man._vec(d2) - man._vec(d1)))
for ii in range(10):
S1 = man.rand()
eta = man._rand_ambient()
xi = man.randvec(S1)
a1 = man.J(S1, eta)
dlt = 1e-8
Snew = psd_point(S1.Y + dlt * xi.tY, S1.P + dlt * xi.tP)
a2 = man.J(Snew, eta)
d1 = {
'P': (a2['P'] - a1['P']) / dlt,
'YP': (a2['YP'] - a1['YP']) / dlt
}
d2 = man.D_J(S1, xi, eta)
print(check_zero(man._vec_range_J(d2) - man._vec_range_J(d1)))
# derives metrics
for ii in range(10):
S1 = man.rand()
xi = man.randvec(S1)
omg1 = man._rand_ambient()
omg2 = man._rand_ambient()
dlt = 1e-7
S2 = psd_point(S1.Y + dlt * xi.tY, S1.P + dlt * xi.tP)
p1 = man.inner(S1, omg1, omg2)
p2 = man.inner(S2, omg1, omg2)
der1 = (p2 - p1) / dlt
der2 = man.base_inner_ambient(man.D_g(S1, xi, omg2), omg1)
print(der1 - der2)
if np.abs(der1 - der2) > 1e-4:
print(der1, der2)
break
# cross term for christofel
for i in range(10):
S1 = man.rand()
xi = man.randvec(S1)
eta1 = man.randvec(S1)
eta2 = man.randvec(S1)
dr1 = man.D_g(S1, xi, eta1)
x12 = man.contract_D_g(S1, eta1, eta2)
p1 = man.base_inner_ambient(dr1, eta2)
p2 = man.base_inner_ambient(x12, xi)
print(p1, p2, p1 - p2)
# now test christofel:
# two things: symmetric on vector fields
# and christofel relation
# in the case metric
for i in range(10):
S1 = man.rand()
xi = man.randvec(S1)
eta1 = man.randvec(S1)
eta2 = man.randvec(S1)
p1 = man.proj_g_inv(S1, man.christoffel_form(S1, xi, eta1))
p2 = man.proj_g_inv(S1, man.christoffel_form(S1, eta1, xi))
print(check_zero(man._vec(p1) - man._vec(p2)))
v1 = man.base_inner_ambient(man.christoffel_form(S1, eta1, eta2), xi)
v2 = man.base_inner_ambient(man.D_g(S1, eta1, eta2), xi)
v3 = man.base_inner_ambient(man.D_g(S1, eta2, eta1), xi)
v4 = man.base_inner_ambient(man.D_g(S1, xi, eta1), eta2)
print(v1, 0.5 * (v2 + v3 - v4), v1 - 0.5 * (v2 + v3 - v4))
def test_rhess_02():
alpha = randint(1, 10, 2) * .1
gamma = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .1
m = 4
n = 5
p = 3
man = ComplexFixedRank(m, n, p, alpha=alpha, beta=beta, gamma=gamma)
S = man.rand()
# simple function. Distance to a given matrix
# || S - A||_F^2 Basically SVD
A = crandn(m, n)
def f(S):
diff = (A - S.U @ S.P @ S.V.T.conj())
return rtrace(diff @ diff.T.conj())
def df(S):
return fr_ambient(-2*A @ S.V @ S.P,
-2*A.T.conj() @ S.U @S.P,
2*(S.P-S.U.T.conj() @ A @ S.V))
def ehess_vec(S, xi):
return fr_ambient(-2*A @ (xi.tV @ S.P + S.V @ xi.tP),
-2*A.T.conj() @ (xi.tU @S.P + [email protected]),
2*(xi.tP - xi.tU.T.conj()@[email protected] -
S.U.T.conj()@[email protected]))
def ehess_form(S, xi, eta):
ev = ehess_vec(S, xi)
return rtrace(ev.tU.T.conj() @ eta.tU) +\
rtrace(ev.tV.T.conj() @ eta.tV) +\
rtrace(ev.tP.T.conj() @ eta.tP)
xxi = man.randvec(S)
dlt = 1e-8
Snew = fr_point(
S.U+dlt*xxi.tU,
S.V+dlt*xxi.tV,
S.P + dlt*xxi.tP)
d1 = (f(Snew) - f(S))/dlt
d2 = df(S)
print(d1 - man.base_inner_ambient(d2, xxi))
dv1 = (df(Snew) - df(S)).scalar_mul(1/dlt)
dv2 = ehess_vec(S, xxi)
print(man._vec(dv1-dv2))
eeta = man.randvec(S)
d1 = man.base_inner_ambient((df(Snew) - df(S)), eeta) / dlt
ehess_val = ehess_form(S, xxi, eeta)
print(man.base_inner_ambient(dv2, eeta))
print(d1, ehess_val, d1-ehess_val)
# now check the formula: ehess = xi (eta_func(f)) - <D_xi eta, df(Y)>
# promote eta to a vector field.
mU1 = crandn(m, m)
mV1 = crandn(n, n)
m2 = crandn(p, p)
m_p = crandn(p*p, p*p)
def eta_field(Sin):
return man.proj(S, fr_ambient(
mU1 @ (Sin.U - S.U) @ m2,
mV1 @ (Sin.V - S.V) @ m2,
hsym((m_p @ (Sin.P - S.P).reshape(-1)).reshape(p, p)))) + eeta
# xietaf: should go to ehess(xi, eta) + df(Y) @ etafield)
xietaf = (man.base_inner_ambient(df(Snew), eta_field(Snew)) -
man.base_inner_ambient(df(S), eta_field(S))) / dlt
# appy eta_func to f: should go to tr(m1 @ xxi @ m2 @ df(Y).T.conj())
Dxietaf = man.base_inner_ambient(
(eta_field(Snew) - eta_field(S)), df(S))/dlt
# this is ehess. should be same as d1 or ehess_val
print(xietaf-Dxietaf)
print(xietaf-Dxietaf-ehess_val)
# now check: rhess. Need to make sure xi, eta in the tangent space.
# first compare this with numerical differentiation
xi1 = man.proj(S, xxi)
eta1 = man.proj(S, eeta)
egvec = df(S)
ehvec = ehess_vec(S, xi1)
rhessvec = man.ehess2rhess(S, egvec, ehvec, xi1)
# check it numerically:
def rgrad_func(Y):
return man.proj_g_inv(Y, df(Y))
# val2a, _, _ = calc_covar_numeric_raw(man, W, xi1, df)
val2, _, _ = calc_covar_numeric(man, S, xi1, rgrad_func)
val2_p = man.proj(S, val2)
# print(rhessvec)
# print(val2_p)
print(man._vec(rhessvec-val2_p))
rhessval = man.inner(S, rhessvec, eta1)
print(man.inner(S, val2, eta1))
print(rhessval)
# check symmetric:
ehvec_e = ehess_vec(S, eta1)
rhessvec_e = man.ehess2rhess(S, egvec, ehvec_e, eta1)
rhessval_e = man.inner(S, rhessvec_e, xi1)
print(rhessval_e)
# the above computed inner_prod(Nabla_xi Pi * df, eta)
# in the following check. Extend eta1 to eta_proj
# (Pi Nabla_hat Pi g_inv df, g eta)
# = D_xi (Pi g_inv df, g eta) - (Pi g_inv df g Pi Nabla_hat eta)
def eta_proj(S):
return man.proj(S, eta_field(S))
print(check_zero(man._vec(eta1-eta_proj(S))))
e1 = man.inner(S, man.proj_g_inv(S, df(S)), eta_proj(S))
e1a = man.base_inner_ambient(df(S), eta_proj(S))
print(e1, e1a, e1-e1a)
Snew = fr_point(
S.U + dlt*xi1.tU,
S.V + dlt*xi1.tV,
S.P + dlt*xi1.tP)
e2 = man.inner(Snew, man.proj_g_inv(Snew, df(Snew)), eta_proj(Snew))
e2a = man.base_inner_ambient(df(Snew), eta_proj(Snew))
print(e2, e2a, e2-e2a)
first = (e2 - e1)/dlt
first1 = (man.base_inner_ambient(df(Snew), eta_proj(Snew)) -
man.base_inner_ambient(df(S), eta_proj(S)))/dlt
print(first-first1)
val3, _, _ = calc_covar_numeric(man, S, xi1, eta_proj)
second = man.inner(S, man.proj_g_inv(S, df(S)), man.proj(S, val3))
second2 = man.inner(S, man.proj_g_inv(S, df(S)), val3)
print(second, second2, second-second2)
print('same as rhess_val %f' % (first-second))
def test_all_projections():
dvec = np.array([10, 3, 2, 3])
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
man = ComplexFlag(dvec, alpha=alpha)
Y = man.rand()
U = man._rand_ambient()
Upr = man.proj(Y, U)
test_inner(man, Y)
test_J(man, Y)
# now check metric, Jst etc
# check Jst: vectorize the operator J then compare Jst with jmat.T.conjugate()
jmat = make_j_mat(man, Y)
test_Jst(man, Y, jmat)
ginv_mat = make_g_inv_mat(man, Y)
# test g_inv_Jst
for ii in range(10):
a = man._rand_range_J()
avec = man._vec_range_J(a)
jtout = man._unvec(ginv_mat @ jmat.T @ avec)
jtout2 = man.g_inv_Jst(Y, a)
diff = check_zero(jtout - jtout2)
print(diff)
# test projection
test_projection(man, Y)
for i in range(20):
Uran = man._rand_ambient()
Upr = man.proj(Y, man.g_inv(Y, Uran))
Upr2 = man.proj_g_inv(Y, Uran)
print(check_zero(Upr - Upr2))
for ii in range(10):
a = man._rand_range_J()
xi = man._rand_ambient()
jtout2 = man.Jst(Y, a)
dlt = 1e-7
Ynew = Y + dlt * xi
jtout2a = man.Jst(Ynew, a)
d1 = (jtout2a - jtout2) / dlt
d2 = man.D_Jst(Y, xi, a)
print(check_zero(d2 - d1))
for ii in range(10):
Y = man.rand()
eta = man._rand_ambient()
xi = man.randvec(Y)
a1 = man.J(Y, eta)
dlt = 1e-7
Ynew = Y + dlt * xi
a2 = man.J(Ynew, eta)
d1 = (man._vec_range_J(a2) - man._vec_range_J(a1)) / dlt
d2 = man._vec_range_J(man.D_J(Y, xi, eta))
print(check_zero(d2 - d1))
for ii in range(10):
a = man._rand_range_J()
xi = man._rand_ambient()
jtout2 = man.g_inv_Jst(Y, a)
dlt = 1e-7
Ynew = Y + dlt * xi
jtout2a = man.g_inv_Jst(Ynew, a)
d1 = (jtout2a - jtout2) / dlt
d2 = man.D_g_inv_Jst(Y, xi, a)
print(check_zero(d2 - d1))
for ii in range(10):
arand = man._rand_range_J()
a2 = man.solve_J_g_inv_Jst(Y, arand)
a1 = man.J(Y, man.g_inv_Jst(Y, a2))
print(check_zero(man._vec_range_J(a1) - man._vec_range_J(arand)))
# derives
for ii in range(10):
Y1 = man.rand()
xi = man.randvec(Y1)
omg1 = man._rand_ambient()
omg2 = man._rand_ambient()
dlt = 1e-7
Y2 = Y1 + dlt * xi
p1 = man.inner(Y1, omg1, omg2)
p2 = man.inner(Y2, omg1, omg2)
der1 = (p2 - p1) / dlt
der2 = man.base_inner_ambient(man.D_g(Y1, xi, omg2), omg1)
print(check_zero(der1 - der2))
# cross term for christofel
for i in range(10):
Y1 = man.rand()
xi = man.randvec(Y1)
omg1 = man._rand_ambient()
omg2 = man._rand_ambient()
dr1 = man.D_g(Y1, xi, omg1)
x12 = man.contract_D_g(Y1, omg1, omg2)
p1 = trace(dr1 @ omg2.T.conjugate()).real
p2 = trace(x12 @ xi.T.conjugate()).real
print(p1, p2, p1 - p2)
# now test christofel:
# two things: symmetric on vector fields
# and christofel relation
# in the case metric
for i in range(10):
Y1 = man.rand()
xi = man.randvec(Y1)
eta1 = man.randvec(Y1)
eta2 = man.randvec(Y1)
p1 = man.proj_g_inv(Y1, man.christoffel_form(Y1, xi, eta1))
p2 = man.proj_g_inv(Y1, man.christoffel_form(Y1, eta1, xi))
print(check_zero(p1 - p2))
v1 = man.base_inner_ambient(man.christoffel_form(Y1, eta1, eta2), xi)
v2 = man.base_inner_ambient(man.D_g(Y1, eta1, eta2), xi)
v3 = man.base_inner_ambient(man.D_g(Y1, eta2, eta1), xi)
v4 = man.base_inner_ambient(man.D_g(Y1, xi, eta1), eta2)
print(v1, 0.5 * (v2 + v3 - v4), v1 - 0.5 * (v2 + v3 - v4))
"""
def test_rhess_02():
np.random.seed(0)
dvec = np.array([10, 3, 2, 3])
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
man = ComplexFlag(dvec, alpha=alpha)
n = man.n
d = man.d
Y = man.rand()
UU = {}
p = alpha.shape[0]
VV = {}
gidx = man._g_idx
for rr in range(p):
UU[rr] = make_sym_pos(n)
VV[rr] = crandn(n, dvec[rr + 1])
def f(Y):
ss = 0
for rr in range(p):
br, er = gidx[rr + 1]
wr = Y[:, br:er]
ss += trace(UU[rr] @ wr @ wr.T.conjugate()).real
return ss
def df(W):
ret = np.zeros_like(W)
for rr in range(p):
br, er = gidx[rr + 1]
wr = W[:, br:er]
ret[:, br:er] += 2 * UU[rr] @ wr
return ret
def ehess_form(W, xi, eta):
ss = 0
for rr in range(p):
br, er = gidx[rr + 1]
ss += 2 * trace(
UU[rr] @ xi[:, br:er] @ eta[:, br:er].T.conjugate()).real
return ss
def ehess_vec(W, xi):
ret = np.zeros_like(W)
for rr in range(p):
br, er = gidx[rr + 1]
ret[:, br:er] += 2 * UU[rr] @ xi[:, br:er]
return ret
xxi = crandn(n, d)
dlt = 1e-8
Ynew = Y + dlt * xxi
d1 = (f(Ynew) - f(Y)) / dlt
d2 = df(Y)
print(d1 - trace(d2 @ xxi.T.conjugate()).real)
eeta = crandn(n, d)
d1 = trace((df(Ynew) - df(Y)) @ eeta.T.conjugate()).real / dlt
ehess_val = ehess_form(Y, xxi, eeta)
# ehess_val2 = ehess_form(Y, eeta, xxi)
dv2 = ehess_vec(Y, xxi)
print(trace(dv2 @ eeta.T.conjugate()).real)
print(d1, ehess_val, d1 - ehess_val)
# now check the formula: ehess = xi (eta_func(f)) - <D_xi eta, df(Y)>
# promote eta to a vector field.
m1 = crandn(n, n)
m2 = crandn(d, d)
def eta_field(Yin):
return m1 @ (Yin - Y) @ m2 + eeta
# xietaf: should go to ehess(xi, eta) + df(Y) @ etafield)
xietaf = trace(
df(Ynew) @ eta_field(Ynew).T.conjugate() -
df(Y) @ eta_field(Y).T.conjugate()).real / dlt
# appy eta_func to f: should go to tr(m1 @ xxi @ m2 @ df(Y).T.conjugate())
Dxietaf = trace(
(eta_field(Ynew) - eta_field(Y)) @ df(Y).T.conjugate()).real / dlt
# this is ehess. should be same as d1 or ehess_val
print(xietaf - Dxietaf)
print(xietaf - Dxietaf - ehess_val)
# now check: rhess. Need to make sure xi, eta in the tangent space.
# first compare this with numerical differentiation
xi1 = man.proj(Y, xxi)
eta1 = man.proj(Y, eeta)
egvec = df(Y)
ehvec = ehess_vec(Y, xi1)
rhessvec = man.ehess2rhess(Y, egvec, ehvec, xi1)
# check it numerically:
def rgrad_func(Y):
return man.proj_g_inv(Y, df(Y))
val2, _, _ = calc_covar_numeric(man, Y, xi1, rgrad_func)
val2_p = man.proj(Y, val2)
# print(rhessvec)
# print(val2_p)
print(check_zero(rhessvec - val2_p))
rhessval = man.inner(Y, rhessvec, eta1)
print(man.inner(Y, val2, eta1))
print(rhessval)
# check symmetric:
ehvec_e = ehess_vec(Y, eta1)
ehess_valp = ehess_form(Y, xi1, eta1)
rhessvec_e = man.ehess2rhess(Y, egvec, ehvec_e, eta1)
rhessval_e = man.inner(Y, rhessvec_e, xi1)
rhessval_e1 = man.rhess02(Y, xi1, eta1, egvec, ehess_valp)
# rhessval_e2 = man.rhess02_alt(Y, xi1, eta1, egvec,
# trace([email protected]()).real)
# print(rhessval_e, rhessval_e1, rhessval_e2)
print(rhessval_e, rhessval_e1, rhessval_e - rhessval_e1)
print('rhessval_e %f ' % rhessval_e)
# the above computed inner_prod(Nabla_xi Pi * df, eta)
# in the following check. Extend eta1 to eta_proj
# (Pi Nabla_hat Pi g_inv df, g eta)
# = D_xi (Pi g_inv df, g eta) - (Pi g_inv df g Pi Nabla_hat eta)
def eta_proj(Y):
return man.proj(Y, eta_field(Y))
print(check_zero(eta1 - eta_proj(Y)))
e1 = man.inner(Y, man.proj_g_inv(Y, df(Y)), eta_proj(Y))
e1a = trace(df(Y) @ eta_proj(Y).T.conjugate()).real
print(e1, e1a, e1 - e1a)
Ynew = Y + xi1 * dlt
e2 = man.inner(Ynew, man.proj_g_inv(Ynew, df(Ynew)), eta_proj(Ynew))
e2a = trace(df(Ynew) @ eta_proj(Ynew).T.conjugate()).real
print(e2, e2a, e2 - e2a)
first = (e2 - e1) / dlt
first1 = trace(
df(Ynew) @ eta_proj(Ynew).T.conjugate() -
df(Y) @ eta_proj(Y).T.conjugate()).real / dlt
print(first - first1)
val3, _, _ = calc_covar_numeric(man, Y, xi1, eta_proj)
second = man.inner(Y, man.proj_g_inv(Y, df(Y)), man.proj(Y, val3))
second2 = man.inner(Y, man.proj_g_inv(Y, df(Y)), val3)
print(second, second2, second - second2)
print('same as rhess_val %f' % (first - second))
def test_rhess_02():
n, d = (50, 3)
alpha = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .1
man = ComplexPositiveSemidefinite(n, d, alpha=alpha, beta=beta)
S = man.rand()
# simple function. Distance to a given matrix
# || S - A||_F^2
A = hsym(crandn(n, n))
def f(S):
diff = (A - S.Y @ S.P @ S.Y.T.conjugate())
return trace(diff @ diff.T.conjugate())
def df(S):
return psd_ambient(-4 * A @ S.Y @ S.P,
2 * (S.P - S.Y.T.conjugate() @ A @ S.Y))
def ehess_form(S, xi, eta):
return (
trace(-4 * A @ (xi.tY @ S.P + S.Y @ xi.tP) @ eta.tY.T.conjugate())
+ 2 * trace(
(xi.tP - xi.tY.T.conjugate() @ A @ S.Y -
S.Y.T.conjugate() @ A @ xi.tY) @ eta.tP.T.conjugate())).real
def ehess_vec(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))
xxi = man.randvec(S)
dlt = 1e-8
Snew = psd_point(S.Y + dlt * xxi.tY, S.P + dlt * xxi.tP)
d1 = (f(Snew) - f(S)) / dlt
d2 = df(S)
print(d1 - man.base_inner_ambient(d2, xxi))
eeta = man.randvec(S)
d1 = man.base_inner_ambient((df(Snew) - df(S)), eeta) / dlt
ehess_val = ehess_form(S, xxi, eeta)
dv2 = ehess_vec(S, xxi)
print(man.base_inner_ambient(dv2, eeta))
print(d1, ehess_val, d1 - ehess_val)
# now check the formula: ehess = xi (eta_func(f)) - <D_xi eta, df(Y)>
# promote eta to a vector field.
m1 = crandn(n, n)
m2 = crandn(d, d)
m_p = crandn(d * d, d * d)
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
# xietaf: should go to ehess(xi, eta) + df(Y) @ etafield)
xietaf = (man.base_inner_ambient(df(Snew), eta_field(Snew)) -
man.base_inner_ambient(df(S), eta_field(S))) / dlt
# appy eta_func to f: should go to tr(m1 @ xxi @ m2 @ df(Y).T)
Dxietaf = man.base_inner_ambient(
(eta_field(Snew) - eta_field(S)), df(S)) / dlt
# this is ehess. should be same as d1 or ehess_val
print(xietaf - Dxietaf)
print(xietaf - Dxietaf - ehess_val)
# now check: rhess. Need to make sure xi, eta in the tangent space.
# first compare this with numerical differentiation
xi1 = man.proj(S, xxi)
eta1 = man.proj(S, eeta)
egvec = df(S)
ehvec = ehess_vec(S, xi1)
rhessvec = man.ehess2rhess(S, egvec, ehvec, xi1)
# check it numerically:
def rgrad_func(Y):
return man.proj_g_inv(Y, df(Y))
# val2a, _, _ = calc_covar_numeric_raw(man, W, xi1, df)
val2, _, _ = calc_covar_numeric(man, S, xi1, rgrad_func)
val2_p = man.proj(S, val2)
# print(rhessvec)
# print(val2_p)
print(man._vec(rhessvec - val2_p))
rhessval = man.inner_product_amb(S, rhessvec, eta1)
print(man.inner_product_amb(S, val2, eta1))
print(rhessval)
# check symmetric:
ehvec_e = ehess_vec(S, eta1)
rhessvec_e = man.ehess2rhess(S, egvec, ehvec_e, eta1)
rhessval_e = man.inner_product_amb(S, rhessvec_e, xi1)
print(rhessval_e)
# the above computed inner_prod(Nabla_xi Pi * df, eta)
# in the following check. Extend eta1 to eta_proj
# (Pi Nabla_hat Pi g_inv df, g eta)
# = D_xi (Pi g_inv df, g eta) - (Pi g_inv df g Pi Nabla_hat eta)
def eta_proj(S):
return man.proj(S, eta_field(S))
print(check_zero(man._vec(eta1 - eta_proj(S))))
e1 = man.inner(S, man.proj_g_inv(S, df(S)), eta_proj(S))
e1a = man.base_inner_ambient(df(S), eta_proj(S))
print(e1, e1a, e1 - e1a)
Snew = psd_point(S.Y + dlt * xi1.tY, S.P + dlt * xi1.tP)
e2 = man.inner(Snew, man.proj_g_inv(Snew, df(Snew)), eta_proj(Snew))
e2a = man.base_inner_ambient(df(Snew), eta_proj(Snew))
print(e2, e2a, e2 - e2a)
first = (e2 - e1) / dlt
first1 = (man.base_inner_ambient(df(Snew), eta_proj(Snew)) -
man.base_inner_ambient(df(S), eta_proj(S))) / dlt
print(first - first1)
val3, _, _ = calc_covar_numeric(man, S, xi1, eta_proj)
second = man.inner(S, man.proj_g_inv(S, df(S)), man.proj(S, val3))
second2 = man.inner(S, man.proj_g_inv(S, df(S)), val3)
print(second, second2, second - second2)
print('same as rhess_val %f' % (first - second))
def optim_test():
from pymanopt import Problem
from pymanopt.solvers import TrustRegions
from pymanopt.function import Callable
n = 300
# problem Tr(AXBX^T)
for i in range(1):
dvec = np.array([0, 5, 4, 10])
dvec[0] = n - dvec[1:].sum()
d = dvec[1:].sum()
alpha = randint(1, 10, 2) * .1
D = randint(1, 10, n) * 0.02 + 1
OO = random_orthogonal(n)
A = OO @ np.diag(D) @ OO.T.conjugate()
B = make_sym_pos(d)
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
alpha0 = randint(1, 10, (p))
# alpha0 = randint(1, 2, (p))
alpha = make_simple_alpha(alpha0, 0)
man = ComplexFlag(dvec, alpha)
@Callable
def cost(X):
return trace(A @ X @ B @ X.T.conjugate()).real
@Callable
def egrad(X):
return 2 * A @ X @ B
@Callable
def ehess(X, H):
return 2 * A @ H @ B
X = man.rand()
if False:
xi = man.randvec(X)
d1 = num_deriv(man, X, xi, cost)
d2 = trace(egrad(X) @ xi.T.conjugate()).real
print(check_zero(d1 - d2))
prob = Problem(man, cost, egrad=egrad)
XInit = man.rand()
prob = Problem(man, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=100)
opt = solver.solve(prob, x=XInit, Delta_bar=250)
print(cost(opt))
alpha0 = randint(1, 2, (p))
alpha1 = make_simple_alpha(alpha0, 0)
man1 = ComplexFlag(dvec, alpha=alpha1)
prob = Problem(man1, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=100)
opt = solver.solve(prob, x=XInit, Delta_bar=250)
alpha0 = randint(1, 2, (p))
alpha2 = make_simple_alpha(alpha0, -1)
man1 = ComplexFlag(dvec, alpha=alpha2)
prob = Problem(man1, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=100)
opt = solver.solve(prob, x=XInit, Delta_bar=250)
def optim_test3():
from pymanopt import Problem
from pymanopt.solvers import TrustRegions
from pymanopt.function import Callable
n = 1000
# problem Tr(AXBX^T)
for i in range(1):
# Stiefel manifold
dvec = np.array([0, 50])
dvec[0] = n - dvec[1:].sum()
d = dvec[1:].sum()
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
alpha0 = randint(1, 10, (p))
# alpha0 = randint(1, 2, (p))
alpha = make_simple_alpha(alpha0, 0)
B = np.diag(np.concatenate([randint(1, 10, d), np.zeros(n - d)]))
D = randint(1, 10, n) * 0.02 + 1
OO = random_orthogonal(n)
A = OO @ np.diag(D) @ OO.T
man = ComplexFlag(dvec, alpha)
cf = 10
B2 = B @ B
@Callable
def cost(X):
return cf * trace(
B @ X @ st(X) @ B2 @ X @ st(X) @ B) +\
trace(st(X) @ A @ X)
@Callable
def egrad(X):
R = cf * 4 * B2 @ X @ st(X) @ B2 @ X + 2 * A @ X
return R
@Callable
def ehess(X, H):
return 4*cf*B2 @ H @ st(X) @ B2 @ X +\
4*cf*B2 @ X @ st(H) @ B2 @ X +\
4*cf*B2 @ X @ st(X) @ B2 @ H + 2*A @ H
if False:
X = man.rand()
xi = man.randvec(X)
d1 = num_deriv(man, X, xi, cost)
d2 = trace(egrad(X) @ st(xi)).real
print(check_zero(d1 - d2))
d3 = num_deriv(man, X, xi, egrad)
d4 = ehess(X, xi)
print(check_zero(d3 - d4))
XInit = man.rand()
prob = Problem(man, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=100)
opt = solver.solve(prob, x=XInit, Delta_bar=2500)
print(cost(opt))
if False:
# print(opt)
# double check:
# print(cost(opt))
min_val = 1e190
# min_X = None
for i in range(100):
Xi = man.rand()
c = cost(Xi)
if c < min_val:
# min_X = Xi
min_val = c
if i % 1000 == 0:
print('i=%d min=%f' % (i, min_val))
print(min_val)
alpha0 = randint(1, 2, (p))
alpha1 = make_simple_alpha(alpha0, 0)
man1 = ComplexFlag(dvec, alpha=alpha1)
prob = Problem(man1, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=100)
opt = solver.solve(prob, x=XInit, Delta_bar=250)
alpha0 = randint(1, 2, (p))
alpha2 = make_simple_alpha(alpha0, -1)
man1 = ComplexFlag(dvec, alpha=alpha2)
# man1 = RealStiefel(n, d, alpha=np.array([1, 1]))
prob = Problem(man1, cost, egrad=egrad, ehess=ehess)
solver = TrustRegions(maxtime=100000, maxiter=100)
opt = solver.solve(prob, x=XInit, Delta_bar=250)
def test_covariance_deriv():
# now test full:
# do covariant derivatives
alpha = randint(1, 10, 2) * .1
gamma = randint(1, 10, 2) * .1
beta = randint(1, 10, 2)[0] * .1
m = 4
n = 5
p = 3
man = ComplexFixedRank(m, n, p, alpha=alpha, beta=beta, gamma=gamma)
S = man.rand()
aaU = crandn(m*p, m*p)
aaV = crandn(n*p, n*p)
cc = crandn(p*p, p*p)
icpt = man._rand_ambient()
def omg_func(S):
csp = hsym((cc @ S.P.reshape(-1)).reshape(p, p))
return fr_ambient(
(aaU @ S.U.reshape(-1) + icpt.tU.reshape(-1)).reshape(m, p),
(aaV @ S.V.reshape(-1) + icpt.tV.reshape(-1)).reshape(n, p),
csp + icpt.tP)
xi = man.randvec(S)
egrad = omg_func(S)
ecsp = hsym((cc @ xi.tP.reshape(-1)).reshape(p, p))
ehess = fr_ambient(
(aaU @ xi.tU.reshape(-1)).reshape(m, p),
(aaV @ xi.tV.reshape(-1)).reshape(n, p),
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(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)))
