def _debug_bcg(self):
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._X,
self._B, self._tol,
self._maxiter)
self._final_X, self._final_R, self._residual_hist = bcg_solver_obj.bcg_variant_one_run(
0)
print(self._residual_hist)
def _cg_bcg_bcbcg_least_square_exp(self):
""" """
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_4 = np.random.random( ( self._mat.shape[0],4) )
self._BX_4 = np.ones ( (self._mat.shape[1],4) )
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
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m1s2, self._final_r_bcbcg_m1s2, self._residual_hist_bcbcg_m1s2 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 2, self._tol, self._maxiter, 0)
##line 4
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m1s8, self._final_r_bcbcg_m1s8, self._residual_hist_bcbcg_m1s8 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 8, self._tol, self._maxiter, 0)
##line 5
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m4s2, self._final_r_bcbcg_m4s2, self._residual_hist_bcbcg_m4s2 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 2, self._tol, self._maxiter, 0)
##line 6
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m4s8, self._final_r_bcbcg_m4s8, self._residual_hist_bcbcg_m4s8 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 8, self._tol, self._maxiter, 0)
plot_worker = Presenter()
#residual_list = [self._residual_hist_cg, self._residual_hist_bcg_m12, \
# self._residual_hist_bcbcg_m1s2, self._residual_hist_bcbcg_m1s8, \
# self._residual_hist_bcbcg_m4s2, self._residual_hist_bcbcg_m4s8 ]
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_m12]
#legend_list = ["cg","bcg_m12", "bcbcg_m1s2", "bcbcg_m1s8", "bcbcg_m4s2", "bcbcg_m4s8"]
legend_list = ["cg","bcg_m12"]
#color_list = ["r","k","b","y","m","g"]
color_list = ["r","k"]
#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(dim=30,401, nnz=471,601, cond=5816.01 )", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(residual_list, "MG", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
def _db_bcg_least_square (self):
""" """
m = 32
self._BB_m = np.random.random( ( self._mat.shape[0],m) )
self._BX_m = np.ones ( (self._mat.shape[1],m) )
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_m, self._BB_m, self._tol, self._maxiter)
self._final_X_bcg, self._final_R_bcg, self._residual_hist_bcg = bcg_solver_obj.bcg_variant_one_run(0)
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._BX_m, self._BB_m, self._tol, self._maxiter)
self._final_X_bcg_lstsq, self._final_R_bcg_lstsq, self._residual_hist_bcg_lstsq = bcg_solver_obj.bcg_variant_lstsq_run(0)
plot_worker = Presenter()
residual_list = [self._residual_hist_bcg, self._residual_hist_bcg_lstsq]
legend_list = ["bcg","bcg_lstsq"]
color_list = ["r","k"]
plot_worker.instant_plot_y_log10(residual_list, "test", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", 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 _cg_bcg_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)
plot_worker = Presenter()
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_mX]
legend_list = ["cg", "bcg_m" + str(block_size)]
color_list = ["r", "k"]
plot_worker.instant_plot_y_log10(
residual_list, "MG", "#iteration",
"$\\mathbf{log_{10}\\frac{||r_1||}{||b_1||}}$", legend_list,
color_list)
def _cg_bcg_bcbcg_least_square_exp(self):
""" """
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_4 = np.random.random( ( self._mat.shape[0],4) )
self._BX_4 = np.ones ( (self._mat.shape[1],4) )
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
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m1s2, self._final_r_bcbcg_m1s2, self._residual_hist_bcbcg_m1s2 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 2, self._tol, self._maxiter, 0)
##line 4
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m1s8, self._final_r_bcbcg_m1s8, self._residual_hist_bcbcg_m1s8 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 8, self._tol, self._maxiter, 0)
##line 5
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m4s2, self._final_r_bcbcg_m4s2, self._residual_hist_bcbcg_m4s2 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 2, self._tol, self._maxiter, 0)
##line 6
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m4s8, self._final_r_bcbcg_m4s8, self._residual_hist_bcbcg_m4s8 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_4, self._BX_4, 8, self._tol, self._maxiter, 0)
plot_worker = Presenter()
#residual_list = [self._residual_hist_cg, self._residual_hist_bcg_m12, \
# self._residual_hist_bcbcg_m1s2, self._residual_hist_bcbcg_m1s8, \
# self._residual_hist_bcbcg_m4s2, self._residual_hist_bcbcg_m4s8 ]
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_m12]
#legend_list = ["cg","bcg_m12", "bcbcg_m1s2", "bcbcg_m1s8", "bcbcg_m4s2", "bcbcg_m4s8"]
legend_list = ["cg","bcg_m12"]
#color_list = ["r","k","b","y","m","g"]
color_list = ["r","k"]
#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(dim=30,401, nnz=471,601, cond=5816.01 )", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
plot_worker.instant_plot_y_log10(residual_list, "MG", "#iteration", "$\\mathbf{log_{10}\\frac{||x_1||}{||b_1||}}$", legend_list, color_list)
def _cg_bcg_bcbcg_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
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m1s2, self._final_r_bcbcg_m1s2, self._residual_hist_bcbcg_m1s2 = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 2, self._tol, self._maxiter, 0)
#line 4
#bcbcg_solver_obj = BCBCG()
#self._final_x_bcbcg_m1s6, self._final_r_bcbcg_m1s6, self._residual_hist_bcbcg_m1s6 = \
# bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 6, self._tol, self._maxiter, 0)
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m1s6, self._final_r_bcbcg_m1s6, self._residual_hist_bcbcg_m1s6 = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_1, self._BX_1, 8, self._tol, self._maxiter, 0)
#line 5
bcbcg_solver_obj = BCBCG()
self._final_x_bcbcg_m6s2, self._final_r_bcbcg_m6s2, self._residual_hist_bcbcg_m6s2 = \
bcbcg_solver_obj.bcbcg_solver_least_square(self._mat, self._BB_6, self._BX_6, 2, self._tol, self._maxiter, 0)
#line 6
#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)
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, 8, self._tol, self._maxiter, 0)
plot_worker = Presenter()
residual_list = [self._residual_hist_cg, self._residual_hist_bcg_m12, \
self._residual_hist_bcbcg_m1s2, self._residual_hist_bcbcg_m1s6, \
self._residual_hist_bcbcg_m6s2, self._residual_hist_bcbcg_m6s6 ]
legend_list = ["cg","bcg_m12", "bcbcg_m1s2", "bcbcg_m1s6", "bcbcg_m6s2", "bcbcg_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)
def _debug_bcg(self):
bcg_solver_obj = NativeBlockConjugateGradient(self._mat, self._X, self._B, self._tol, self._maxiter)
self._final_X, self._final_R, self._residual_hist = bcg_solver_obj.bcg_variant_one_run(0)
print(self._residual_hist)