def if_feasible(self, z_new, lower_limit):
L = self.L
if (any(z_new[0:2 * self.L] <= lower_limit)):
# some g1 less equan then zero
return False
for p in range(0, self.L):
# ph
index_start = 2 * L + int(p) * 4 * L * L
index_end = 2 * L + int(p + 1) * 4 * L * L
zmatrix = transform_vec_to_matrix(
z_new[index_start:index_end],
2 * L) - lower_limit * np.identity(2 * L, dtype=np.float32)
try:
np.linalg.cholesky(zmatrix)
except:
#if cholesky failed, then fmatrix is not positive definite
return False
for p in range(0, self.L):
# pair1
index_start = 2 * L + 4 * L * L * L + int(p) * L * L
index_end = 2 * L + 4 * L * L * L + int(p + 1) * L * L
zmatrix = transform_vec_to_matrix(
z_new[index_start:index_end],
L) - lower_limit * np.identity(L, dtype=np.float32)
try:
np.linalg.cholesky(zmatrix)
except:
#if cholesky failed, then fmatrix is not positive definite
return False
for p in range(0, self.L):
# pair2
index_start = 2 * L + 5 * L * L * L + int(p) * L * L
index_end = 2 * L + 5 * L * L * L + int(p + 1) * L * L
zmatrix = transform_vec_to_matrix(
z_new[index_start:index_end],
L) - lower_limit * np.identity(L, dtype=np.float32)
try:
np.linalg.cholesky(zmatrix)
except:
#if cholesky failed, then fmatrix is not positive definite
return False
return True
def generate_two_ph_dual_zmatrix(self, p):
L = self.L
index_start = 2 * L + int(p) * 4 * L * L
index_end = 2 * L + int(p + 1) * 4 * L * L
zmatrix = transform_vec_to_matrix(self.vec_z[index_start:index_end],
2 * self.L)
return zmatrix
def generate_two_pair1_primal_matrix(self,p):
L = self.L
index_start = 2*L + 4*L*L*L + int(p)*L*L
index_end = 2*L + 4*L*L*L + int(p+1)*L*L
fmatrix = transform_vec_to_matrix( self.vec_x[index_start:index_end], self.L)
return fmatrix
def initialization(self):
L = self.L
mu = 0.1
linear_eq_A = np.dot(self.const_var.mat_C,
np.transpose(self.const_var.mat_C))
linear_eq_b = -self.const_var.vec_b - np.dot(self.const_var.mat_C,
self.vec_ham)
self.var_y = solve(linear_eq_A, linear_eq_b)
temp_x = -np.dot(np.transpose(self.const_var.mat_C),
self.var_y) - self.vec_ham
self.var_y = self.var_y
temp_x = temp_x
for i in range(0, 2 * L):
if (temp_x[i] > 0.0):
self.primal_var.vec_x[i] = temp_x[i]
self.dual_var.vec_z[i] = mu / temp_x[i]
else:
self.primal_var.vec_x[i] = 1.0
self.dual_var.vec_z[i] = mu
for p in range(0, L):
index_st = 2 * L + p * (L * L) * 4
index_en = index_st + 4 * L * L
temp_mat = transform_vec_to_matrix(temp_x[index_st:index_en],
2 * L)
eig = eigvals(temp_mat)
eig_min = np.min(eig)
if (eig_min > self.truncation):
self.primal_var.vec_x[index_st:index_en] = temp_x[
index_st:index_en].copy()
else:
temp_mat = temp_mat + (1.0 + abs(eig_min)) * np.identity(
2 * L, dtype=np.complex64)
self.primal_var.vec_x[index_st:index_en] = vectorize(
temp_mat, 2 * L)
self.dual_var.vec_z[index_st:index_en] = vectorize(
mu * inv(temp_mat), 2 * L)
for p in range(0, L):
index_st = 2 * L + 4 * L * L * L + p * (L * L)
index_en = index_st + (L * L)
temp_mat = transform_vec_to_matrix(temp_x[index_st:index_en], L)
eig = eigvals(temp_mat)
eig_min = np.min(eig)
if (eig_min > self.truncation):
self.primal_var.vec_x[index_st:index_en] = temp_x[
index_st:index_en].copy()
else:
temp_mat = temp_mat + (1.0 + abs(eig_min)) * np.identity(
L, dtype=np.complex64)
self.primal_var.vec_x[index_st:index_en] = vectorize(
temp_mat, L)
self.dual_var.vec_z[index_st:index_en] = vectorize(
mu * inv(temp_mat), L)
for p in range(0, L):
index_st = 2 * L + 5 * L * L * L + p * (L * L)
index_en = index_st + (L * L)
temp_mat = transform_vec_to_matrix(temp_x[index_st:index_en], L)
eig = eigvals(temp_mat)
eig_min = np.min(eig)
if (eig_min > self.truncation):
self.primal_var.vec_x[index_st:index_en] = temp_x[
index_st:index_en].copy()
else:
temp_mat = temp_mat + (1.0 + abs(eig_min)) * np.identity(
L, dtype=np.complex64)
self.primal_var.vec_x[index_st:index_en] = vectorize(
temp_mat, L)
self.dual_var.vec_z[index_st:index_en] = vectorize(
mu * inv(temp_mat), L)
rx = self.vec_ham + np.dot(np.transpose(self.const_var.mat_C),
(self.var_y)) - (self.dual_var.vec_z)
ry = -self.const_var.vec_b + np.dot(self.const_var.mat_C,
(self.primal_var.vec_x))