def __init__(self):
info('Initializing Time control')
# watch is list [total_time, last_start, message_when_measured, count into total time]
self.watches = {}
self.last_measurement = 0
self.measuring = 0
tic()
def __init__(self):
info('Initializing Time control')
# watch is list [total_time, last_start, message_when_measured, count into total time, count into selected time]
self.watches = {}
self.last_measurement = 0
self.measuring = 0
tic()
def majorant_II_nd(u, Ve, w, W, y, f, u0, error_II, gamma, T, mesh, C_FD, dim,
v_deg, MAJORANT_OPTIMIZE):
tic()
m_d, m_f, l, L, m_T = update_majorant_II_components(
u, w, y, f, u0, mesh, dim, v_deg, W, Ve, T)
maj_II, beta = calculate_majorant_II(m_d, m_f, l, L, m_T, C_FD, gamma)
i_eff = sqrt(maj_II / error_II)
majorant_reconstruction_time = toc()
output_optimization_results(-1, maj_II, m_d, m_f, i_eff, beta, error_II)
if MAJORANT_OPTIMIZE:
#----------------------------------------------------------------------------#
# Optimization algorithm
#----------------------------------------------------------------------------#
tic()
S, K, L, z, g = get_matrices_of_optimization_problem_II(
W, u, y, f, u0, T, mesh, dim, v_deg)
w = Function(W)
W = w.vector()
OPT_ITER = 4
# Execute iterative process to optimize majorant with respect to beta and flux
for k in range(1, OPT_ITER):
#list_linear_solver_methods()
#list_krylov_solver_preconditioners()
# Solve system with respect to Y
solve((1 + beta) * S - (C_FD**2) * beta / (1 + beta) * K, W,
-L - (1 + beta) * z - (C_FD**2) * beta /
(1 + beta) * g) # lu 3.4 - 3.5
# Calculate majorant
m_d, m_f, l, L, m_T = update_majorant_II_components(
u, w, y, f, u0, mesh, dim, v_deg, W, Ve, T)
maj_II, beta = calculate_majorant_II(m_d, m_f, l, L, m_T, C_FD,
gamma)
i_eff = sqrt(maj_II / error_II)
output_optimization_results(k, maj_II, m_d, m_f, i_eff, beta,
error_II)
majorant_minimization_time = toc()
else:
majorant_minimization_time = 0.0
return maj_II, beta, majorant_reconstruction_time, majorant_minimization_time
def solve_problem_nd_t(self, mesh, V, VV, V_exact, VV_exact, H_div, f,
sq_f, phi, grad_phi, u_e, grad_u_e, sq_grad_u_e,
u_0, u0_boundary, dim, t_T, domain, C_FD, delta,
test_num, problem_params, project_path,
results_folder):
# Define the value of the time-step
nt = problem_params["nt"]
tau = float(t_T / nt)
eps_k = problem_params['total_accuracy'] / nt
# Initialize times
t_k = 0 # t_k
t_kp1 = tau # t_{k + 1}
t_km1 = -tau # t_{k - 1}
# Initialize the time integration scheme
order = 4 # time integration order 2, 3, 4
quadrature = integrators.TimeIntegrator(order, t_k, t_kp1)
# Allocate the space for the arrays
ed_k, et_k, e_incr_k, error_k, m0_k, md_k, mf_k, maj_incr_k, majorant_k, beta_k, i_eff_maj_k = \
estimates.allocate_space_for_error_and_majorant(nt)
h_max_k = postprocess.allocate_array(nt)
h_min_k = postprocess.allocate_array(nt)
error_d_k = postprocess.allocate_array(nt)
majorant_d_k = postprocess.allocate_array(nt)
vd_k = postprocess.allocate_array(nt)
vt_k = postprocess.allocate_array(nt)
v_norm_incr_k = postprocess.allocate_array(nt)
v_norm_k = postprocess.allocate_array(nt)
rel_error_k = postprocess.allocate_array(nt)
rel_majorant_k = postprocess.allocate_array(nt)
primal_problem_time = postprocess.allocate_array(nt)
majorant_minimization_time = postprocess.allocate_array(nt)
majorant_reconstruction_time = postprocess.allocate_array(nt)
# Initialize the approximation and flux on the t = t_k
v_k = interpolate(phi, V)
y_k = interpolate(grad_phi, H_div)
v_k1 = Function(V)
# Initialize the counter
k = 0
error = 1e8
while k + 1 <= nt:
t0_problem = tic()
# Update the time integration for the quadratures
print "\n-----------------------------------------"
print "Time interval [%f, %f]:" % (t_k, t_kp1)
print "------------------------------------------\n"
quadrature.update(t_k, t_kp1)
#while error > eps_k:
if k == 0 or problem_params[
'solving_strategy_tag'] == "with-refinement":
# Define unknown function and test function
u = TrialFunction(V) # unknown function
v = TestFunction(V) # test function for the variational form
# Define stiffness and mass matrices based on the functional space
K, M = problem.construct_stiffness_and_mass_matrices(u, v, dim)
# Define BC
bc = DirichletBC(V, u_0, u0_boundary)
# Update right hand side f_{k}
f.t = t_k
f_k = interpolate(f, V)
# Update right hand side f_{k + 1}
f.t = t_kp1
f_k1 = interpolate(f, V)
# Update boundary condition
u_0.t = t_kp1
# Update the exact solution at t_{k + 1}
u_e.t = t_kp1
u_k1 = interpolate(u_e, V_exact)
v_k1 = problem.get_next_step_solution(
v_k, v_k1, K, tau, M, f_k, f_k1, bc,
problem_params['time_discretization_tag'])
# u_0.t = t_kp1
# v_k1 = interpolate(u_e, V)
'''
if (problem_params['solving_strategy_tag'] == 'without-refinement' and k == 0) or \
problem_params['solving_strategy_tag'] == 'with-refinement':
postprocess.plot_solution(v_k1, mesh, dim, project_path + results_folder + 'u-%d' % (k + 1))
'''
grad_v_k = problem.Grad(v_k, dim)
grad_v_k1 = problem.Grad(v_k1, dim)
# Define y_1 as a derivative with respect to x_i = x_0 = x[0]
y_k1 = project(grad_v_k1, H_div)
# Check the exact flux (test)
# grad_phi.t = t_kp1
# y_k1 = project(grad_phi, H_div)
v_norm_incr_k, vd_k, vt_k = estimates.v_norm_nd_t(
k, v_norm_incr_k, vd_k, vt_k, v_k, v_k1, V_exact, VV_exact,
mesh, dim, tau, delta)
#v_norm_incr_k, vd_k, vt_k = estimates.v_norm_nd_t(k, v_norm_incr_k, vd_k, vt_k,
# v_k1, grad_v_k, grad_v_k1,
# V_exact, VV_exact,
# mesh, dim, tau, delta)
# Calculate error components
e_incr_k, ed_k, et_k, ed_var = estimates.error_norm_nd_t(
k, e_incr_k, ed_k, et_k, grad_u_e, sq_grad_u_e, quadrature,
u_k1, v_k1, grad_v_k, grad_v_k1, V_exact, VV_exact, mesh, dim,
tau, delta)
# Calculate majorant components
maj_incr_k, m0_k, md_k, mf_k, beta_k, y_k1, md_var, mf_var = \
estimates.majorant_nd_t(k, maj_incr_k, m0_k, md_k, mf_k, beta_k, e_incr_k[k], ed_k[k], et_k[k],
f, sq_f, phi, quadrature,
v_k, v_k1, grad_v_k, grad_v_k1, V_exact,
y_k, y_k1, H_div,
mesh, tau, delta, dim, domain, C_FD,
majorant_reconstruction_time, majorant_minimization_time)
e_distr, md_distr, maj_distr = estimates.error_majorant_distribution_nd(
mesh, dim, ed_var, md_var, mf_var, V_exact)
e_distr, md_distr, maj_distr = estimates.majorant_distribution_DG0(
mesh, ed_var, md_var, mf_var)
# Document indicator perpormance
h_max_k[k] = mesh.hmax()
h_min_k[k] = mesh.hmin()
init_data_info = postprocess.construct_result_tag(
test_num, problem_params["nx0"], problem_params["nx1"],
problem_params["nx2"], nt, k + 1)
# Update the overall error from time interval [0, t_k]
error_k, majorant_k, i_eff_maj_k, rel_error_k, rel_majorant_k = \
estimates.add_increment_of_error_and_majorant(k, e_incr_k, maj_incr_k, error_k, et_k, majorant_k,
i_eff_maj_k,
v_norm_incr_k, v_norm_k, vt_k, rel_error_k, rel_majorant_k)
postprocess.output_time_layer_result_error_and_majorant(
t_k, t_kp1, rel_error_k[k], rel_majorant_k[k], i_eff_maj_k[k])
error = error_k[k]
# Define the solving strategy (refine or not refine)
if problem_params['solving_strategy_tag'] == "with-refinement":
if dim == 2 and problem_params[
'refinement_criteria_tag'] == "majorant":
postprocess.plot_carpet_2d(
mesh, project(ed_var, V_exact), project_path +
results_folder + 'carpet-error' + init_data_info)
postprocess.plot_carpet_2d(
mesh, project(md_var, V_exact), project_path +
results_folder + 'carpet-majorant' + init_data_info)
# Refine mesh
mesh = self.execute_refinement_strategy(
problem_params, mesh, e_distr, md_distr, maj_distr,
error_k, majorant_k, i_eff_maj_k, error_d_k, majorant_d_k,
h_max_k, h_min_k, t_T, k, nt, project_path, results_folder,
init_data_info)
# num_cells = mesh.num_cells()
# num_vertices = mesh.num_vertices()
# results_info = postprocess.construct_result_tag(test_num, nt, nx0, nx1, nx2, k+1, num_cells, num_vertices, dim)
# Update functional spaces, BC, and stiffness/mass matrices
V, VV, V_exact, VV_exact, H_div = problem.functional_spaces(
mesh, problem_params, dim)
# Update functions
#v_k1.set_allow_extrapolation(True)
#y_k1.set_allow_extrapolation(True)
v_k.assign(interpolate(v_k1, V))
#v_k.assign(project(v_k1, V))
#v_k.assign(v_k1)
y_k.assign(interpolate(y_k1, H_div))
v_k1 = Function(V)
else:
# Document results
postprocess.document_results_without_refinement(
mesh, e_distr, md_distr, error_k, majorant_k, i_eff_maj_k,
error_d_k, majorant_d_k, h_max_k, h_min_k, t_T, k, nt,
project_path, results_folder, init_data_info)
# Update functions
v_k.assign(v_k1)
y_k.assign(y_k1)
# Update time layer
t_kp1 += tau
t_km1 += tau
t_k += tau
k += 1
else:
for jj in xrange(0, N+1):
phi_list.append(Expression("cos(pi*i*x[0]) * cos(pi*j*x[1])", i=ii, j=jj))
return phi_list
# Import functionality from the folder 'lib'
import os, sys
lib_path = os.path.abspath('lib')
sys.path.append(lib_path)
import postprocess
# ---------------------------------------------------------------------------------#
# Calculate reference triangle constant C_hat_G and C_hat_P
tic()
h = 1
def f1_tan(x):
return x*cos(x)/sin(x) + 1
def f2_tan(x):
return tan(x) + tanh(x)
zeta_1 = scipy.optimize.newton(f1_tan, DOLFIN_PI/2)
zeta_2 = scipy.optimize.newton(f2_tan, DOLFIN_PI)
sigma_1 = abs(zeta_2 * tan(zeta_2))
C_hat_P = h / zeta_1
def majorant_nd_t(k, m_incr_k, m0_k, md_k, mf_k, beta_k, e_incr_k, ed_k, et_k,
f, sq_f, phi, quadr, v_k, v_k1, grad_v_k, grad_v_k1, V_exact,
y_k, y_k1, H_div, mesh, tau, delta, dim, domain, C_FD,
majorant_reconstruction_time, majorant_minimization_time):
tic()
rf_k = problem.Div(y_k, dim) - (v_k1 - v_k) / tau
rf_k1 = problem.Div(y_k1, dim) - (v_k1 - v_k) / tau
rd_k = y_k - grad_v_k
rd_k1 = y_k1 - grad_v_k1
sq_F_ = quadr.f(sq_f, V_exact)
F_t_tk_ = quadr.f_t_tk(f, V_exact)
F_tk1_t_ = quadr.f_tk1_t(f, V_exact)
# Components of mf = || f + div y - v_t ||
mf_y_k = (sq_F_ + Constant(2.0) / tau * inner(F_tk1_t_, rf_k) +
tau / Constant(3.0) * inner(rf_k, rf_k)) * dx(domain=mesh)
mf_y_k1 = (Constant(2.0) / tau * inner(F_t_tk_, rf_k1) +
tau / Constant(3.0) *
(inner(rf_k, rf_k1) + inner(rf_k1, rf_k1))) * dx(domain=mesh)
# Components of md = || y - grad v ||
md_y_k = (tau / Constant(3.0) * inner(rd_k, rd_k)) * dx(domain=mesh)
md_y_k1 = (tau / Constant(3.0) *
(inner(rd_k, rd_k1) + inner(rd_k1, rd_k1))) * dx(domain=mesh)
m0 = assemble(((phi - v_k) * (phi - v_k)) * dx(domain=mesh))
# Assemble components of dual and balance terms of majorant
# which stays the same in optimization process
Mf_y_k = assemble(mf_y_k)
Md_y_k = assemble(md_y_k)
Mf_y_k1, Md_y_k1 = update_majorant_on_k1_time_level(mf_y_k1, md_y_k1)
m_d, m_f = update_majorant_components(Md_y_k, Md_y_k1, Mf_y_k, Mf_y_k1)
maj, beta = calculate_majorant(m_d, m_f, C_FD, delta)
i_eff = sqrt(maj / e_incr_k)
majorant_reconstruction_time[k] = toc()
output_optimization_results(-1, maj, m_d, m_f, i_eff, beta, e_incr_k, ed_k,
et_k)
#----------------------------------------------------------------------------#
# Optimization part
#----------------------------------------------------------------------------#
tic()
S, K, z, q = get_matrices_of_optimization_problem(H_div, F_t_tk_, v_k,
v_k1, grad_v_k,
grad_v_k1, mesh, tau,
dim)
OPT_ITER = 4
for opt_iter in range(1, OPT_ITER):
# Solve system with respect to Y
A = (C_FD**2) * S + beta * K
b = -0.5 * A * y_k.vector() - C_FD**2 * 3.0 / (tau**2) * z + beta * q
#solve(A, y_k1.vector(), b, "gmres", 'ilu')
solve(A, y_k1.vector(), b)
mf_y_k1 = (
Constant(2.0) / tau * inner(F_t_tk_, rf_k1) + tau / Constant(3.0) *
(inner(rf_k, rf_k1) + inner(rf_k1, rf_k1))) * dx(domain=mesh)
md_y_k1 = (tau / Constant(3.0) *
(inner(rd_k, rd_k1) + inner(rd_k1, rd_k1))) * dx(
domain=mesh)
Mf_y_k1, Md_y_k1 = update_majorant_on_k1_time_level(mf_y_k1, md_y_k1)
m_d, m_f = update_majorant_components(Md_y_k, Md_y_k1, Mf_y_k, Mf_y_k1)
maj, beta = calculate_majorant(m_d, m_f, C_FD, delta)
i_eff = sqrt(maj / e_incr_k)
output_optimization_results(opt_iter, maj, m_d, m_f, i_eff, beta,
e_incr_k, ed_k, et_k)
majorant_minimization_time[k] = toc()
md_k[k] = m_d
mf_k[k] = m_f
beta_k[k] = beta
m_incr_k[k] = maj
m0_k[k] = m0
rf_k = problem.Div(y_k, dim) - (v_k1 - v_k) / tau
rf_k1 = problem.Div(y_k1, dim) - (v_k1 - v_k) / tau
rd_k = y_k - grad_v_k
rd_k1 = y_k1 - grad_v_k1
md_var = tau / Constant(3.0) * (inner(rd_k, rd_k) + inner(rd_k, rd_k1) +
inner(rd_k1, rd_k1))
mf_var = 1.0 / delta * (
(1.0 + beta) * md_var + (1.0 + 1.0 / beta) * (C_FD**2) *
(sq_F_ + Constant(2.0) / tau *
(inner(F_tk1_t_, rf_k) + inner(F_t_tk_, rf_k1)) +
tau / Constant(3.0) *
(inner(rf_k, rf_k) + inner(rf_k, rf_k1) + inner(rf_k1, rf_k1))))
return m_incr_k, m0_k, md_k, mf_k, beta_k, y_k1, md_var, mf_var
def majorant_nd(u, Ve, y, H_div, f, A, invA, min_eig_A, c_H, eps, lmbd, a, u0,
error, mesh, C_FD, dim, test_params):
tic()
u0_0, O_mesh = get_2d_slice_of_3d_function_on_Oz(
mesh,
interpolate(u0, Ve),
0,
dim,
test_params["v_approx_order"],
)
u_0, O_mesh = get_2d_slice_of_3d_function_on_Oz(
mesh,
interpolate(u, Ve),
0,
dim,
test_params["v_approx_order"],
)
r_b = abs(u0_0 - u_0)
m_b = assemble(inner(r_b, r_b) * dx(domain=O_mesh))
# Define residuals
beta = 1.0
f_bar = f - c_H * D_t(u, dim) - lmbd * u - inner(a, NablaGrad(u, dim))
maj, m_d, m_f, beta, var_m_d, var_m_f_w_opt = calculate_majorant_bar_mu_opt(
u, y, beta, f_bar, A, invA, min_eig_A, lmbd, mesh, dim, C_FD)
i_eff = sqrt(maj / error)
print " "
print '%------------------------------------------------------------------------------------%'
print "% Majorant before optimization"
print '%------------------------------------------------------------------------------------%'
print " "
majorant_reconstruction_time = toc()
output_optimization_results(-1, maj, m_d, m_f, i_eff, beta, error)
if test_params['MAJORANT_OPTIMIZE']:
#----------------------------------------------------------------------------#
# Optimization algorithm
#----------------------------------------------------------------------------#
print " "
print "%-----------------------"
print "% optimization "
print "%-----------------------"
print " "
tic()
#S, K, z, g = get_matrices_of_optimization_problem(H_div, u, f, c_H, mesh, dim)
S, K, z, g = get_matrices_of_optimization_problem(
H_div, u, f_bar, invA, mesh, dim)
y = Function(H_div)
Y = y.vector()
# Execute iterative process to optimize majorant with respect to beta and flux
for k in range(1, test_params['majorant_optimization_iterations']):
#list_linear_solver_methods()
#list_krylov_solver_preconditioners()
# Solve system with respect to Y
solve((C_FD**2) / min_eig_A * S + beta * K, Y,
(C_FD**2) / min_eig_A * z + beta * g) # lu 3.4 - 3.5
#solve((C_FD ** 2) * S + beta * K, Y, (C_FD ** 2) * z + beta * g, "gmres", "jacobi") # 3.4
#solve((C_FD ** 2) * S + beta * K, Y, (C_FD ** 2) * z + beta * g, "cg", "ilu") # 3.4
#solve(C_FD * S + beta * K, Y, C_FD * z + beta * g, "cg", "jacobi")
#solve(C_FD * S + beta * K, Y, C_FD * z + beta * g, "gmres", "hypre_amg") # 3.4 - 3.5
y.vector = Y
# Update majorant
maj, m_d, m_f, beta, var_m_d, var_m_f_w_opt = calculate_majorant_bar_mu_opt(
u, y, beta, f_bar, A, invA, min_eig_A, lmbd, mesh, dim, C_FD)
i_eff = sqrt(maj / error)
output_optimization_results(k, maj, m_d, m_f, i_eff, beta, error)
majorant_minimization_time = toc()
else:
majorant_minimization_time = 0.0
return maj, y, beta, m_d, m_f, var_m_d, var_m_f_w_opt, majorant_reconstruction_time, majorant_minimization_time
def majorant_nd(v, y, H_div, f, A, invA, min_eig_A, eps, a, lmbd, error, mesh,
dim, C_FD, C_Ntr, problem_params):
"""
:param v: approximate solution
:param y: flux
:param H_div: funtional space for the flux function
:param f: right-hand side function (modified RHS)
:param A: diffusion matrix
:param min_eig_A: minimal eigenvalue of diffusion matrix
:param a: convection function
:param lmbd: reaction function
:param error: error value
:param mesh: mesh
:param dim: geometrical problem dimension
:param C_FD: Freidrichs constant of the computational domain
:param MAJORANT_OPTIMIZE: test_parameter on weather to optimize majorant of not
:return maj: majorant value
m_d, m_f_one_minus_mu_opt: majorant components
beta: optimal parameter
var_m_d, var_m_f_w_opt: variational form of the majorant (to use them later in construction of error estimators)
maj, y, beta, m_d, m_f, var_m_d, var_m_f_w_opt, majorant_reconstruction_time, majorant_minimization_time
"""
tic()
# Initialize value
beta = 1.0
#for i in range(2):
f_bar = f - lmbd * v - inner(a, Grad(v, dim))
#f_bar = f
maj, m_d, m_f, beta, var_m_d, var_m_f_w_opt = calculate_majorant_bar_mu_opt(
v, y, beta, f_bar, A, invA, min_eig_A, lmbd, mesh, dim, C_FD)
i_eff = sqrt(maj / error)
print " "
print '%------------------------------------------------------------------------------------%'
print "% Majorant optimization"
print '%------------------------------------------------------------------------------------%'
print " "
info(parameters, verbose=True)
print(parameters.linear_algebra_backend)
list_linear_solver_methods()
list_krylov_solver_preconditioners()
solver = KrylovSolver('gmres', 'ilu')
prm = solver.parameters
prm.absolute_tolerance = 1e-10
prm.relative_tolerance = 1e-6
prm.maximum_iterations = 1000
output_optimization_results(0, maj, m_d, m_f, i_eff, beta, error)
majorant_reconstruction_time = toc()
if problem_params["MAJORANT_OPTIMIZE"]:
#----------------------------------------------------------------------------#
# Optimization algorithm
#----------------------------------------------------------------------------#
tic()
S, K, z, g = get_matrices_of_optimization_problem_bar(
H_div, v, f_bar, invA, mesh, dim)
y = Function(H_div)
Y = y.vector()
OPT_ITER = problem_params["majorant_optimization_iterations"]
i_eff = 1e8
k = 1
m_d = 1.0
m_f = 10.0
# Execute iterative process to optimize majorant with respect to beta and flux
while k in range(0, OPT_ITER): # m_d * min_eig_A /m_f <= 1: #or
# Solve system with respect to Y
#yMatrix = (C_FD ** 2) / min_eig_A * S + beta * K
#yRhs = sum((C_FD ** 2) / min_eig_A * z, beta * g)
#solve(yMatrix, Y, yRhs)
solve((C_FD**2) / min_eig_A * S + beta * K, Y,
(C_FD**2) / min_eig_A * z + beta * g)
#if len(Y) <= 1e4:
# solve((C_FD ** 2) / min_eig_A * S + beta * K, Y, (C_FD ** 2) / min_eig_A * z + beta * g)
#else:
# solver.solve((C_FD ** 2) / min_eig_A * S + beta * K, Y, (C_FD ** 2) / min_eig_A * z + beta * g,
# "gmres", "ilu")
y.vector = Y
"""
YtSY = assemble(inner(Div(y, dim), Div(y, dim)) * dx(domain=mesh))
YtSz2 = assemble(2 * inner(Div(y, dim), f) * dx(domain=mesh))
FF = assemble(inner(f, f) * dx(domain=mesh))
print "Y^T*S*Y", YtSY
print "2*Y^T*z", YtSz2
print "FF", FF
print "\| div y + f \|^2", YtSY + YtSz2 + FF
YtY = assemble(inner(y, y) * dx(domain=mesh))
YtSz2 = assemble(2 * inner(Grad(v, dim), y) * dx(domain=mesh))
GradVGradV = assemble(inner(Grad(v, dim), Grad(v, dim)) * dx(domain=mesh))
"""
# Calculate majorant
maj, m_d, m_f, beta, var_m_d, var_m_f_w_opt = calculate_majorant_bar_mu_opt(
v, y, beta, f_bar, A, invA, min_eig_A, lmbd, mesh, dim, C_FD)
i_eff = sqrt(maj / error)
output_optimization_results(k, maj, m_d, m_f, i_eff, beta, error)
k = k + 1
majorant_minimization_time = toc()
else:
majorant_minimization_time = 0.0
return maj, y, beta, m_d, m_f, var_m_d, var_m_f_w_opt, majorant_reconstruction_time, majorant_minimization_time
def majorant_nd(v, y, H_div, V, VV, f, A, invA, lambda_1, a, lmbd, error, mesh,
dim, C_FD, C_Ntr, problem_params):
"""
:param v: approximate solution
:param y: flux
:param H_div: funtional space for the flux function
:param f: right-hand side function (modified RHS)
:param A: diffusion matrix
:param lambda_1: minimal eigenvalue of diffusion matrix
:param a: convection function
:param lmbd: reaction function
:param error: error value
:param mesh: mesh
:param dim: geometrical problem dimension
:param C_FD: Freidrichs constant of the computational domain
:param MAJORANT_OPTIMIZE: test_parameter on weather to optimize majorant of not
:return maj: majorant value
m_d, m_f_one_minus_mu_opt: majorant components
beta: optimal parameter
var_m_d, var_m_f_w_opt: variational form of the majorant (to use them later in construction of error estimators)
maj, y, beta, m_d, m_f, var_m_d, var_m_f_w_opt, majorant_reconstruction_time, majorant_minimization_time
"""
tic()
# Initialize value
beta = 1.0
#for i in range(2):
f_bar = f - lmbd * v - inner(a, Grad(v, dim))
#f_bar = f
maj, m_d, m_f, beta, var_m_d, var_m_f_w_opt = calculate_majorant_bar_mu_opt(
v, y, beta, f_bar, A, invA, lambda_1, lmbd, mesh, dim, C_FD)
i_eff = sqrt(maj / error)
print " "
print '%------------------------------------------------------------------------------------%'
print "% Majorant before optimization"
print '%------------------------------------------------------------------------------------%'
print " "
output_optimization_results(0, maj, m_d, m_f, i_eff, beta, error)
majorant_reconstruction_time = toc()
if problem_params["MAJORANT_OPTIMIZE"]:
#----------------------------------------------------------------------------#
# Optimization algorithm
#----------------------------------------------------------------------------#
print " "
print "%-----------------------"
print "% optimization "
print "%-----------------------"
print " "
tic()
S, K, z, g = get_matrices_of_optimization_problem_bar(
H_div, v, f_bar, invA, mesh, dim)
y = Function(H_div)
Y = y.vector()
OPT_ITER = problem_params["majorant_optimization_iterations"]
# Execute iterative process to optimize majorant with respect to beta and flux
for k in range(0, problem_params["majorant_optimization_iterations"]):
# Solve system with respect to Y
#yMatrix = (C_FD ** 2) / lambda_1 * S + beta * K
#yRhs = sum((C_FD ** 2) / lambda_1 * z, beta * g)
#solve(yMatrix, Y, yRhs)
solve((C_FD**2) / lambda_1 * S + beta * K, Y,
(C_FD**2) / lambda_1 * z + beta * g)
y.vector = Y
'''
YtSY = assemble(inner(Div(y, dim), Div(y, dim)) * dx(domain=mesh))
YtSz2 = assemble(2 * inner(Div(y, dim), f) * dx(domain=mesh))
FF = assemble(inner(f, f) * dx(domain=mesh))
print "Y^T*S*Y", YtSY
print "2*Y^T*z", YtSz2
print "FF", FF
print "\| div y + f \|^2", YtSY + YtSz2 + FF
YtY = assemble(inner(y, y) * dx(domain=mesh))
YtSz2 = assemble(2 * inner(Grad(v, dim), y) * dx(domain=mesh))
GradVGradV = assemble(inner(Grad(v, dim), Grad(v, dim)) * dx(domain=mesh))
print "YtY", YtY
print "YtSz2", YtSz2
print "GradVGradV", GradVGradV
print "\| y - grad v \|^2", YtY - YtSz2 + GradVGradV
print "\| v \|", (norm(v, 'L2'))**2
print "\| grad v \|", (norm(v, 'H1'))**2
print "\| div y \|", (norm(project(div(y), V), 'L2'))**2
print "\| f \|", (norm(project(f, V), 'L2'))**2
'''
# Calculate majorant
maj, m_d, m_f, beta, var_m_d, var_m_f_w_opt = calculate_majorant_bar_mu_opt(
v, y, beta, f_bar, A, invA, lambda_1, lmbd, mesh, dim, C_FD)
i_eff = sqrt(maj / error)
output_optimization_results(k + 1, maj, m_d, m_f, i_eff, beta,
error)
majorant_minimization_time = toc()
else:
majorant_minimization_time = 0.0
return maj, y, beta, m_d, m_f, var_m_d, var_m_f_w_opt, majorant_reconstruction_time, majorant_minimization_time
