def _bcbcg_blbcg_least_square_exp_b(self):
""" figure 2"""
print("_bcbcg_blbcg_least_square_exp_b starting ... ")
m=3
self._BB = np.random.random( ( self._mat.shape[0],m) )
self._BX = np.ones ( (self._mat.shape[1],m) )
#line 1
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_a, self._final_r_blbcg_a, self._residual_hist_blbcg_a = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB, self._BX, 16, self._tol, self._maxiter, 0)
#line 2
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_b, self._final_r_blbcg_b, self._residual_hist_blbcg_b = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB, self._BX, 32, self._tol, self._maxiter, 0)
#line addition
#blbcg_solver_obj = BLBCG()
#self._final_x_blbcg_c, self._final_r_blbcg_c, self._residual_hist_blbcg_c = \
# blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 32, self._tol, self._maxiter, 0)
#line 3
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_a, self._final_r_bcbcg_a, self._residual_hist_bcbcg_a = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB, self._BX, 16, self._tol, self._maxiter, 0)
#line 4
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_b, self._final_r_bcbcg_b, self._residual_hist_bcbcg_b = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB, self._BX, 32, self._tol, self._maxiter, 0)
#line addition
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_c, self._final_r_bcbcg_c, self._residual_hist_bcbcg_c = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 32, self._tol, self._maxiter, 0)
plot_worker = Presenter()
#residual_list = [self._residual_hist_blbcg_a, self._residual_hist_blbcg_b, self._residual_hist_blbcg_c, \
# self._residual_hist_bcbcg_a, self._residual_hist_bcbcg_b, self._residual_hist_bcbcg_c ]
residual_list = [self._residual_hist_blbcg_a, self._residual_hist_blbcg_b, \
self._residual_hist_bcbcg_a, self._residual_hist_bcbcg_b ]
#legend_list = ["blbcg_m4s4", "blbcg_m4s8", "blbcg_m4s12", "bcbcg_m4s4", "bcbcg_m4s8", "bcbcg_m4s12"]
legend_list = ["blbcg_m3s16", "blbcg_m3s32", "bcbcg_m3s16", "bcbcg_m3s32"]
#color_list = ["r","k","b","y","g","m"]
color_list = ["r","k","b","g"]
#plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(residual_list, "bodyy6", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
def _db_blbcg_least_square_exp(self):
""" """
blbcg_solver_obj = BLBCG()
self._final_x_a, self._final_r_a, self._residual_hist_a = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB, self._BX, self._step_val, self._tol, self._maxiter, 0)
bcbcg_solver_obj = BCBCG()
self._final_x_b, self._final_r_b, self._residual_hist_b = \
bcbcg_solver_obj.bcbcg_solver(self._mat, self._BB, self._BX, self._step_val, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_a, self._residual_hist_b]
legend_list = ["blbcg_s2b3_lstsq","blbcg_s2b3"]
color_list = ["r","k"]
plot_worker.instant_plot_y_log10(residual_list, "crystm01", "#iteration", "$\\frac{||x_1||}{||b_1||}$", legend_list, color_list)
def _cg_bcg_bcbcg_blcg_exp(self, block_size):
""" """
print("_cg_bcg_bcbcg_least_square_exp starting, ... ")
self._BB_1 = np.random.random((self._mat.shape[0], 1))
self._BX_1 = np.ones((self._mat.shape[1], 1))
self._BB_X = np.random.random((self._mat.shape[0], block_size))
self._BX_X = np.ones((self._mat.shape[1], block_size))
#line 1
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_1,
self._BB_1, self._tol,
self._maxiter)
self._final_X_cg, self._final_R_cg, self._residual_hist_cg = bcg_solver_obj.bcg_variant_lstsq_run(
0)
##line 2
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_X,
self._BB_X, self._tol,
self._maxiter)
self._final_X_bcg_mX, self._final_R_bcg_mX, self._residual_hist_bcg_mX = bcg_solver_obj.bcg_variant_lstsq_run(
0)
#line 3
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m1sY, self._final_r_bcbcg_m1sY, self._residual_hist_bcbcg_m1sY = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, self._step_val, self._tol, self._maxiter, 0)
#line 4
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_mXsY, self._final_r_bcbcg_mXsY, self._residual_hist_bcbcg_mXsY = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_X, self._BX_X, self._step_val, self._tol, self._maxiter, 0)
#line 5
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_mXsY, self._final_r_blbcg_mXsY, self._residual_hist_blbcg_mXsY = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_X, self._BX_X, self._step_val, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_mX, self._residual_hist_bcbcg_m1sY, \
self._residual_hist_bcbcg_mXsY, self._residual_hist_blbcg_mXsY]
legend_list = ["cg","bcg_m"+str(block_size), "bcbcg_m1_s"+str(self._step_val), \
"bcbcg_m"+str(block_size)+"s"+str(self._step_val), "blbcg_m"+str(block_size)+"s"+str(self._step_val)]
color_list = ["r", "k", "b", "g", "m"]
plot_worker.instant_plot_y_log10(
residual_list, "MG", "#iteration",
"$\\mathbf{log_{10}\\frac{||r_1||}{||b_1||}}$", legend_list,
color_list)
def _db_blbcg_exp(self):
""" """
lbcg_solver_obj = LBCG()
self._final_x_a, self._final_r_a, self._residual_hist_a = \
lbcg_solver_obj.lbcg_solver(self._mat, self._SB, self._SX, 8, self._tol, self._maxiter)
blbcg_solver_obj = BLBCG()
self._final_x_b, self._final_r_b, self._residual_hist_b = \
blbcg_solver_obj.blbcg_solver(self._mat, self._BB, self._BX, 8, self._tol, self._maxiter, 0)
bcbcg_solver_obj = BCBCG()
self._final_x_c, self._final_r_c, self._residual_hist_c = \
bcbcg_solver_obj.bcbcg_solver(self._mat, self._BB, self._BX, 8, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_a, self._residual_hist_b, self._residual_hist_c]
legend_list = ["lbcg_s8","blbcg_s8b10", "bcbcg_s8b10"]
color_list = ["r","k", "b"]
plot_worker.instant_plot_y_log10(residual_list, "bodyy6", "#iteration", "$\\frac{||x_1||}{||b_1||}$", legend_list, color_list)
def _db_blbcg_exp(self):
""" """
lbcg_solver_obj = LBCG()
self._final_x_a, self._final_r_a, self._residual_hist_a = \
lbcg_solver_obj.lbcg_solver(self._mat, self._SB, self._SX, 8, self._tol, self._maxiter)
blbcg_solver_obj = BLBCG()
self._final_x_b, self._final_r_b, self._residual_hist_b = \
blbcg_solver_obj.blbcg_solver(self._mat, self._BB, self._BX, 8, self._tol, self._maxiter, 0)
bcbcg_solver_obj = BCBCG()
self._final_x_c, self._final_r_c, self._residual_hist_c = \
bcbcg_solver_obj.bcbcg_solver(self._mat, self._BB, self._BX, 8, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_a, self._residual_hist_b, self._residual_hist_c]
legend_list = ["lbcg_s8","blbcg_s8b10", "bcbcg_s8b10"]
color_list = ["r","k", "b"]
plot_worker.instant_plot_y_log10(residual_list, "bodyy6", "#iteration", "$\\frac{||x_1||}{||b_1||}$", legend_list, color_list)
def _cg_bcg_bcbcg_blcg_exp(self, block_size):
""" """
print("_cg_bcg_bcbcg_least_square_exp starting, ... ")
self._BB_1 = np.random.random( ( self._mat.shape[0],1) )
self._BX_1 = np.ones ( (self._mat.shape[1],1) )
self._BB_X = np.random.random( ( self._mat.shape[0],block_size) )
self._BX_X = np.ones ( (self._mat.shape[1],block_size) )
#line 1
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_1, self._BB_1, self._tol, self._maxiter)
self._final_X_cg, self._final_R_cg, self._residual_hist_cg = bcg_solver_obj.bcg_variant_lstsq_run(0)
##line 2
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_X, self._BB_X, self._tol, self._maxiter)
self._final_X_bcg_mX, self._final_R_bcg_mX, self._residual_hist_bcg_mX = bcg_solver_obj.bcg_variant_lstsq_run(0)
#line 3
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m1sY, self._final_r_bcbcg_m1sY, self._residual_hist_bcbcg_m1sY = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, self._step_val, self._tol, self._maxiter, 0)
#line 4
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_mXsY, self._final_r_bcbcg_mXsY, self._residual_hist_bcbcg_mXsY = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_X, self._BX_X, self._step_val, self._tol, self._maxiter, 0)
#line 5
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_mXsY, self._final_r_blbcg_mXsY, self._residual_hist_blbcg_mXsY = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_X, self._BX_X, self._step_val, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_mX, self._residual_hist_bcbcg_m1sY, \
self._residual_hist_bcbcg_mXsY, self._residual_hist_blbcg_mXsY]
legend_list = ["cg","bcg_m"+str(block_size), "bcbcg_m1_s"+str(self._step_val), \
"bcbcg_m"+str(block_size)+"s"+str(self._step_val), "blbcg_m"+str(block_size)+"s"+str(self._step_val)]
color_list = ["r","k","b","g","m"]
plot_worker.instant_plot_y_log10(residual_list, "MG", "#iteration", "$\\mathbf{log_{10}\\frac{||r_1||}{||b_1||}}$", legend_list, color_list)
def _bcbcg_blbcg_least_square_exp_b(self):
""" """
self._BB_6 = np.random.random( ( self._mat.shape[0],6) )
self._BX_6 = np.ones ( (self._mat.shape[1],6) )
#line 1
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_m6s6, self._final_r_blbcg_m6s6, self._residual_hist_blbcg_m6s6 = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 6, self._tol, self._maxiter, 0)
#line 2
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_m6s12, self._final_r_blbcg_m6s12, self._residual_hist_blbcg_m6s12 = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 12, self._tol, self._maxiter, 0)
#line 3
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m6s6, self._final_r_bcbcg_m6s6, self._residual_hist_bcbcg_m6s6 = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 6, self._tol, self._maxiter, 0)
#line 4
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m6s12, self._final_r_bcbcg_m6s12, self._residual_hist_bcbcg_m6s12 = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 12, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_blbcg_m6s6, self._residual_hist_blbcg_m6s12, \
self._residual_hist_bcbcg_m6s6, self._residual_hist_bcbcg_m6s12 ]
legend_list = ["blbcg_m6s6", "blbcg_m6s12", "bcbcg_m6s6", "bcbcg_m6s12"]
color_list = ["r","k","b","y"]
#plot_worker.instant_plot_y_log10(residual_list, "Chem97ZtZ", "#iteration", "$\\frac{||x_1||}{||b_1||}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
def _bcbcg_blbcg_least_square_exp_b(self):
""" figure 2"""
print("_bcbcg_blbcg_least_square_exp_b starting ... ")
m = 3
self._BB = np.random.random((self._mat.shape[0], m))
self._BX = np.ones((self._mat.shape[1], m))
#line 1
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_a, self._final_r_blbcg_a, self._residual_hist_blbcg_a = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB, self._BX, 16, self._tol, self._maxiter, 0)
#line 2
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_b, self._final_r_blbcg_b, self._residual_hist_blbcg_b = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB, self._BX, 32, self._tol, self._maxiter, 0)
#line addition
#blbcg_solver_obj = BLBCG()
#self._final_x_blbcg_c, self._final_r_blbcg_c, self._residual_hist_blbcg_c = \
# blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 32, self._tol, self._maxiter, 0)
#line 3
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_a, self._final_r_bcbcg_a, self._residual_hist_bcbcg_a = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB, self._BX, 16, self._tol, self._maxiter, 0)
#line 4
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_b, self._final_r_bcbcg_b, self._residual_hist_bcbcg_b = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB, self._BX, 32, self._tol, self._maxiter, 0)
#line addition
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_c, self._final_r_bcbcg_c, self._residual_hist_bcbcg_c = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 32, self._tol, self._maxiter, 0)
plot_worker = Presenter()
#residual_list = [self._residual_hist_blbcg_a, self._residual_hist_blbcg_b, self._residual_hist_blbcg_c, \
# self._residual_hist_bcbcg_a, self._residual_hist_bcbcg_b, self._residual_hist_bcbcg_c ]
residual_list = [self._residual_hist_blbcg_a, self._residual_hist_blbcg_b, \
self._residual_hist_bcbcg_a, self._residual_hist_bcbcg_b ]
#legend_list = ["blbcg_m4s4", "blbcg_m4s8", "blbcg_m4s12", "bcbcg_m4s4", "bcbcg_m4s8", "bcbcg_m4s12"]
legend_list = [
"blbcg_m3s16", "blbcg_m3s32", "bcbcg_m3s16", "bcbcg_m3s32"
]
#color_list = ["r","k","b","y","g","m"]
color_list = ["r", "k", "b", "g"]
#plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(
residual_list, "bodyy6", "#iteration",
"$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list,
color_list)
def _diff_eigen_estimation_test_c(self):
""" """
print("_diff_eigen_estimation_test_c starting ...")
gerschgorin_estimator = GerschgorinCircleTheoremEigenvalueEstimator()
gerschgorin_max_eigenvalue, gerschgorin_min_eigenvalue = gerschgorin_estimator.csr_mat_extreme_eigenvalue_estimation(self._mat)
print("################Gershchogrin theorem", "min:",gerschgorin_max_eigenvalue, " , old min:", gerschgorin_min_eigenvalue)
if gerschgorin_min_eigenvalue< 0.:
gerschgorin_min_eigenvalue = 0.
print("################Gershchogrin theorem", "min:",gerschgorin_max_eigenvalue, " , new min:", gerschgorin_min_eigenvalue)
eigs_vals, eigs_vecs = eigs(self._mat, k=6, which="LM")
print("eigs_vals max", eigs_vals)
#eigs_vals, eigs_vecs = eigs(self._mat, k=6, tol=1e-3,which="SR")
#print("eigs_vals min", eigs_vals)
power_method_solver = PowerIteration()
self._init_eigen_vec = np.random.random( ( self._mat.shape[0],1) )
pm_maxiters = 500
pm_tol = 1e-6
pm_max_eigen_vec, pm_max_eigen_val, pm_max_eigen_list = power_method_solver.naive_power_iteration (self._mat, self._init_eigen_vec, pm_maxiters, pm_tol)
print("################Power method max:",pm_max_eigen_val, " , iteration:", len(pm_max_eigen_list))
#self._init_eigen_vec = np.random.random( ( self._mat.shape[0],1) )
#pm_min_eigen_vec, pm_min_eigen_val, pm_min_eigen_list = power_method_solver.power_iteration_with_shifting_acc1 (self._mat, self._init_eigen_vec, pm_max_eigen_val, pm_maxiters, pm_tol)
#pm_min_eigen_val = pm_min_eigen_val + pm_max_eigen_val
#print("################Power method min:",pm_min_eigen_val, " , iteration:", len(pm_min_eigen_list))
##
eigen_repo = {"gerschgorin":(gerschgorin_max_eigenvalue, gerschgorin_min_eigenvalue),\
"mix_method":(pm_max_eigen_val,gerschgorin_min_eigenvalue) \
}
print(eigen_repo["gerschgorin"], " , ", eigen_repo["mix_method"])
self._BB = np.random.random( ( self._mat.shape[0],3) )
self._BX = np.ones ( (self._mat.shape[1],3) )
step_val = 32
#line 2
bcbcg_solver_obj = BCBCG()
self._final_gerschgorin_x_bcbcg_a, self._final_gerschgorin_r_bcbcg_a, self._gerschgorin_residual_hist_bcbcg_a = \
bcbcg_solver_obj.bcbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["gerschgorin"][0], eigen_repo["gerschgorin"][1])
#line 4
bcbcg_solver_obj = BCBCG()
self._final_mix_x_bcbcg_b, self._final_mix_r_bcbcg_b, self._mix_residual_hist_bcbcg_b = \
bcbcg_solver_obj.bcbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["mix_method"][0], eigen_repo["mix_method"][1])
#addition
blbcg_solver_obj = BLBCG()
self._final_gerschgorin_x_blbcg_a, self._final_gerschgorin_r_blbcg_a, self._gerschgorin_residual_hist_blbcg_a = \
blbcg_solver_obj.blbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["gerschgorin"][0], eigen_repo["gerschgorin"][1])
blbcg_solver_obj = BLBCG()
self._final_mix_x_blbcg_b, self._final_mix_r_blbcg_b, self._mix_residual_hist_blbcg_b = \
blbcg_solver_obj.blbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["mix_method"][0], eigen_repo["mix_method"][1])
plot_worker = Presenter()
residual_list = [self._gerschgorin_residual_hist_bcbcg_a, self._mix_residual_hist_bcbcg_b , \
self._gerschgorin_residual_hist_blbcg_a, self._mix_residual_hist_blbcg_b ]
legend_list = ["G_bcbcg_m3s32","M_bcbcg_m3s32" , "G_blbcg_m3s32","M_blbcg_m3s32"]
#legend_list = ["G_bcbcg_m3s8","M_bcbcg_m3s8" , "G_blbcg_m3s8","M_blbcg_m3s8"]
color_list = ["r","k","b","g"]
#plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
#plot_worker.instant_plot_y_log10(residual_list, "wathen100", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(residual_list, "bodyy6", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
def _diff_eigen_estimation_test_c(self):
""" """
print("_diff_eigen_estimation_test_c starting ...")
gerschgorin_estimator = GerschgorinCircleTheoremEigenvalueEstimator()
gerschgorin_max_eigenvalue, gerschgorin_min_eigenvalue = gerschgorin_estimator.csr_mat_extreme_eigenvalue_estimation(
self._mat)
print("################Gershchogrin theorem", "min:",
gerschgorin_max_eigenvalue, " , old min:",
gerschgorin_min_eigenvalue)
if gerschgorin_min_eigenvalue < 0.:
gerschgorin_min_eigenvalue = 0.
print("################Gershchogrin theorem", "min:",
gerschgorin_max_eigenvalue, " , new min:",
gerschgorin_min_eigenvalue)
eigs_vals, eigs_vecs = eigs(self._mat, k=6, which="LM")
print("eigs_vals max", eigs_vals)
#eigs_vals, eigs_vecs = eigs(self._mat, k=6, tol=1e-3,which="SR")
#print("eigs_vals min", eigs_vals)
power_method_solver = PowerIteration()
self._init_eigen_vec = np.random.random((self._mat.shape[0], 1))
pm_maxiters = 500
pm_tol = 1e-6
pm_max_eigen_vec, pm_max_eigen_val, pm_max_eigen_list = power_method_solver.naive_power_iteration(
self._mat, self._init_eigen_vec, pm_maxiters, pm_tol)
print("################Power method max:", pm_max_eigen_val,
" , iteration:", len(pm_max_eigen_list))
#self._init_eigen_vec = np.random.random( ( self._mat.shape[0],1) )
#pm_min_eigen_vec, pm_min_eigen_val, pm_min_eigen_list = power_method_solver.power_iteration_with_shifting_acc1 (self._mat, self._init_eigen_vec, pm_max_eigen_val, pm_maxiters, pm_tol)
#pm_min_eigen_val = pm_min_eigen_val + pm_max_eigen_val
#print("################Power method min:",pm_min_eigen_val, " , iteration:", len(pm_min_eigen_list))
##
eigen_repo = {"gerschgorin":(gerschgorin_max_eigenvalue, gerschgorin_min_eigenvalue),\
"mix_method":(pm_max_eigen_val,gerschgorin_min_eigenvalue) \
}
print(eigen_repo["gerschgorin"], " , ", eigen_repo["mix_method"])
self._BB = np.random.random((self._mat.shape[0], 3))
self._BX = np.ones((self._mat.shape[1], 3))
step_val = 32
#line 2
bcbcg_solver_obj = BCBCG()
self._final_gerschgorin_x_bcbcg_a, self._final_gerschgorin_r_bcbcg_a, self._gerschgorin_residual_hist_bcbcg_a = \
bcbcg_solver_obj.bcbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["gerschgorin"][0], eigen_repo["gerschgorin"][1])
#line 4
bcbcg_solver_obj = BCBCG()
self._final_mix_x_bcbcg_b, self._final_mix_r_bcbcg_b, self._mix_residual_hist_bcbcg_b = \
bcbcg_solver_obj.bcbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["mix_method"][0], eigen_repo["mix_method"][1])
#addition
blbcg_solver_obj = BLBCG()
self._final_gerschgorin_x_blbcg_a, self._final_gerschgorin_r_blbcg_a, self._gerschgorin_residual_hist_blbcg_a = \
blbcg_solver_obj.blbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["gerschgorin"][0], eigen_repo["gerschgorin"][1])
blbcg_solver_obj = BLBCG()
self._final_mix_x_blbcg_b, self._final_mix_r_blbcg_b, self._mix_residual_hist_blbcg_b = \
blbcg_solver_obj.blbcg_solver_least_square_eigen_param(self._mat, self._BB, self._BX, step_val, self._tol, self._maxiter, 0, eigen_repo["mix_method"][0], eigen_repo["mix_method"][1])
plot_worker = Presenter()
residual_list = [self._gerschgorin_residual_hist_bcbcg_a, self._mix_residual_hist_bcbcg_b , \
self._gerschgorin_residual_hist_blbcg_a, self._mix_residual_hist_blbcg_b ]
legend_list = [
"G_bcbcg_m3s32", "M_bcbcg_m3s32", "G_blbcg_m3s32", "M_blbcg_m3s32"
]
#legend_list = ["G_bcbcg_m3s8","M_bcbcg_m3s8" , "G_blbcg_m3s8","M_blbcg_m3s8"]
color_list = ["r", "k", "b", "g"]
#plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
#plot_worker.instant_plot_y_log10(residual_list, "wathen100", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(
residual_list, "bodyy6", "#iteration",
"$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list,
color_list)
def _cg_bcg_blbcg_least_square_exp(self):
""" """
self._BB_1 = np.random.random( ( self._mat.shape[0],1) )
self._BX_1 = np.ones ( (self._mat.shape[1],1) )
self._BB_6 = np.random.random( ( self._mat.shape[0],6) )
self._BX_6 = np.ones ( (self._mat.shape[1],6) )
self._BB_12 = np.random.random( ( self._mat.shape[0],12) )
self._BX_12 = np.ones ( (self._mat.shape[1],12) )
#line 1
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_1, self._BB_1, self._tol, self._maxiter)
self._final_X_cg, self._final_R_cg, self._residual_hist_cg = bcg_solver_obj.bcg_variant_lstsq_run(0)
#line 2
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_12, self._BB_12, self._tol, self._maxiter)
self._final_X_bcg_m12, self._final_R_bcg_m12, self._residual_hist_bcg_m12 = bcg_solver_obj.bcg_variant_lstsq_run(0)
#line 3
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_m1s2, self._final_r_blbcg_m1s2, self._residual_hist_blbcg_m1s2 = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 2, self._tol, self._maxiter, 0)
#line 4
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_m1s6, self._final_r_blbcg_m1s6, self._residual_hist_blbcg_m1s6 = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 6, self._tol, self._maxiter, 0)
#line 5
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_m6s2, self._final_r_blbcg_m6s2, self._residual_hist_blbcg_m6s2 = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 2, self._tol, self._maxiter, 0)
#line 6
blbcg_solver_obj = BLBCG()
self._final_x_blbcg_m6s6, self._final_r_blbcg_m6s6, self._residual_hist_blbcg_m6s6 = \
blbcg_solver_obj.blbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 6, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_m12, \
self._residual_hist_blbcg_m1s2, self._residual_hist_blbcg_m1s6, \
self._residual_hist_blbcg_m6s2, self._residual_hist_blbcg_m6s6 ]
legend_list = ["cg","bcg_m12", "blbcg_m1s2", "blbcg_m1s6", "blbcg_m6s2", "blbcg_m6s6"]
color_list = ["r","k","b","y","m","g"]
plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)