Wed Sep 12 21:11:22 2018 CG_TEST Python version: 3.6.5 Test the CG library. ORTH_RANDOM_TEST Python version: 3.6.5 ORTH_RANDOM computes a random orthogal matrix. ORTH_RANDOM matrix: Col: 0 1 2 3 4 Row 0 : -0.559743 -0.371903 -0.0337166 0.68224 0.285986 1 : 0.188708 -0.906112 0.0432361 -0.368531 0.0752686 2 : -0.404357 -0.0795868 -0.732356 -0.200174 -0.503733 3 : -0.423174 0.184571 -0.114406 -0.542494 0.692437 4 : 0.555425 0.0157436 -0.668999 0.253702 0.423476 ORTH_RANDOM_TEST: Normal end of execution. PDS_RANDOM_TEST Python version: 3.6.5 PDS_RANDOM computes the PDS_RANDOM matrix. PDS_RANDOM matrix: Col: 0 1 2 3 4 Row 0 : 0.497055 0.1657 -0.0383139 -0.136355 0.0927247 1 : 0.1657 0.873143 0.0517221 -0.0475388 -0.0540219 2 : -0.0383139 0.0517221 0.614563 0.00896361 0.239044 3 : -0.136355 -0.0475388 0.00896361 0.446983 0.0594034 4 : 0.0927247 -0.0540219 0.239044 0.0594034 0.549504 PDS_RANDOM_TEST Normal end of execution. R8_NORMAL_01_TEST Python version: 3.6.5 R8_NORMAL_01 generates normally distributed random values. Using initial random number seed = 123456789 1.679040 -0.566060 1.212934 1.269381 -1.666087 -2.242464 0.039675 0.673068 -0.275127 2.164005 0.297785 2.044536 1.398819 -1.242985 -0.067084 -0.794396 -0.523768 -0.350567 0.131700 0.537380 R8_NORMAL_01_TEST Normal end of execution. R8_UNIFORM_01_TEST Python version: 3.6.5 R8_UNIFORM_01 produces a sequence of random values. Using random seed 123456789 SEED R8_UNIFORM_01(SEED) 469049721 0.218418 2053676357 0.956318 1781357515 0.829509 1206231778 0.561695 891865166 0.415307 141988902 0.066119 553144097 0.257578 236130416 0.109957 94122056 0.043829 1361431000 0.633966 Verify that the sequence can be restarted. Set the seed back to its original value, and see that we generate the same sequence. SEED R8_UNIFORM_01(SEED) 469049721 0.218418 2053676357 0.956318 1781357515 0.829509 1206231778 0.561695 891865166 0.415307 141988902 0.066119 553144097 0.257578 236130416 0.109957 94122056 0.043829 1361431000 0.633966 R8_UNIFORM_01_TEST Normal end of execution. R8MAT_HOUSE_AXH_TEST Python version: 3.6.5 R8MAT_HOUSE_AXH multiplies a matrix A times a compact Householder matrix. Matrix A: Col: 0 1 2 3 4 Row 0 : -2.81582 -4.33881 -4.38273 -4.98162 3.59097 1 : 4.56318 -2.42422 -0.50461 3.97504 3.40847 2 : 3.29509 -3.90043 -0.986937 -1.49248 -3.76896 3 : 0.616954 -4.56171 2.54673 -4.05455 -4.92488 4 : -0.846929 1.33966 2.97287 -4.86383 -2.39697 Compact vector V so column 3 of H*A is packed: 0: 0 1: 0 2: -0.788819 3: 0.399863 4: 0.466771 Householder matrix H: Col: 0 1 2 3 4 Row 0 : 1 0 0 0 0 1 : 0 1 0 0 0 2 : 0 0 -0.244469 0.630839 0.736395 3 : 0 0 0.630839 0.680219 -0.373289 4 : 0 0 0.736395 -0.373289 0.56425 Indirect product A*H: Col: 0 1 2 3 4 Row 0 : -2.81582 -4.33881 0.573215 -7.49385 0.65837 1 : 4.56318 -2.42422 5.14095 1.11322 0.0678026 2 : 3.29509 -3.90043 -3.47568 -0.230898 -2.29629 3 : 0.616954 -4.56171 -6.80702 0.686997 0.610057 4 : -0.846929 1.33966 -5.56019 -0.538306 2.65233 Direct product A*H: Col: 0 1 2 3 4 Row 0 : -2.81582 -4.33881 0.573215 -7.49385 0.65837 1 : 4.56318 -2.42422 5.14095 1.11322 0.0678026 2 : 3.29509 -3.90043 -3.47568 -0.230898 -2.29629 3 : 0.616954 -4.56171 -6.80702 0.686997 0.610057 4 : -0.846929 1.33966 -5.56019 -0.538306 2.65233 H*A should pack column 3: Col: 0 1 2 3 4 Row 0 : -2.81582 -4.33881 -4.38273 -4.98162 3.59097 1 : 4.56318 -2.42422 -0.50461 3.97504 3.40847 2 : -1.04002 -0.937652 4.03706 -5.7746 -3.95052 3 : 2.81449 -6.06358 -4.44089e-16 -1.88388 -4.83284 4 : 1.71831 -0.41352 -4.44089e-16 -2.32995 -2.28953 R8MAT_HOUSE_AXH_TEST Normal end of execution. R8MAT_HOUSE_FORM_TEST Python version: 3.6.5 R8MAT_HOUSE_FORM forms a Householder matrix from its compact form. Compact vector form V: 0: 0 1: 0 2: 1 3: 2 4: 3 Householder matrix H: Col: 0 1 2 3 4 Row 0 : 1 0 0 0 0 1 : 0 1 0 0 0 2 : 0 0 0.857143 -0.285714 -0.428571 3 : 0 0 -0.285714 0.428571 -0.857143 4 : 0 0 -0.428571 -0.857143 -0.285714 R8MAT_HOUSE_FORM_TEST Normal end of execution. R8MAT_MM_TEST Python version: 3.6.5 R8MAT_MM computes a matrix-matrix product C = A * B; A: Col: 0 1 2 Row 0 : 1 0 0 1 : 1 1 0 2 : 1 2 1 3 : 1 3 3 B: Col: 0 1 2 3 Row 0 : 1 1 1 1 1 : 0 1 2 3 2 : 0 0 1 3 C = A*B: Col: 0 1 2 3 Row 0 : 1 1 1 1 1 : 1 2 3 4 2 : 1 3 6 10 3 : 1 4 10 19 R8MAT_MM_TEST Normal end of execution. R8MAT_PRINT_TEST Python version: 3.6.5 R8MAT_PRINT prints an R8MAT. Here is an R8MAT: Col: 0 1 2 3 4 Row 0 : 11 12 13 14 15 1 : 21 22 23 24 25 2 : 31 32 33 34 35 3 : 41 42 43 44 45 Col: 5 Row 0 : 16 1 : 26 2 : 36 3 : 46 R8MAT_PRINT_TEST: Normal end of execution. R8MAT_PRINT_SOME_TEST Python version: 3.6.5 R8MAT_PRINT_SOME prints some of an R8MAT. Here is an R8MAT: Col: 3 4 5 Row 0 : 14 15 16 1 : 24 25 26 2 : 34 35 36 R8MAT_PRINT_SOME_TEST: Normal end of execution. R8MAT_UNIFORM_AB_TEST Python version: 3.6.5 R8MAT_UNIFORM_AB computes a random R8MAT. -1 <= X <= 5 Initial seed is 123456789 Random R8MAT: Col: 0 1 2 3 Row 0 : 0.31051 -0.603288 -0.629637 -0.98897 1 : 4.73791 0.545467 1.69723 4.38502 2 : 3.97706 -0.340259 1.40784 1.10451 3 : 2.37017 -0.737026 3.52804 -0.432731 4 : 1.49184 2.80379 3.78372 -0.918299 R8MAT_UNIFORM_AB_TEST: Normal end of execution. R8VEC_HOUSE_COLUMN_TEST Python version: 3.6.5 R8VEC_HOUSE_COLUMN returns the compact form of a Householder matrix that "packs" a column of a matrix. Matrix A: Col: 0 1 2 3 Row 0 : 1.09209 2.07654 0.219145 2.00653 1 : 4.78159 0.330594 3.16983 3.77337 2 : 4.14755 1.28789 0.308636 3.98643 3 : 2.80848 0.549784 2.24769 0.00919186 Working on column K = 0 Householder matrix H: Col: 0 1 2 3 Row 0 : -0.155781 -0.682069 -0.591626 -0.400615 1 : -0.682069 0.597486 -0.34914 -0.236418 2 : -0.591626 -0.34914 0.697156 -0.205068 3 : -0.400615 -0.236418 -0.205068 0.86114 Product H*A: Col: 0 1 2 3 Row 0 : -7.01042 -1.53117 -3.27924 -5.24844 1 :-8.88178e-16 -1.79845 1.1053 -0.508058 2 :-1.77636e-15 -0.558841 -1.48213 0.272729 3 :-8.88178e-16 -0.700714 1.03509 -2.50551 Working on column K = 1 Householder matrix H: Col: 0 1 2 3 Row 0 : 1 0 0 0 1 : 0 -0.895014 -0.278112 -0.348717 2 : 0 -0.278112 0.959184 -0.0511776 3 : 0 -0.348717 -0.0511776 0.93583 Product H*A: Col: 0 1 2 3 Row 0 : -7.01042 -1.53117 -3.27924 -5.24844 1 : 1.59868e-15 2.00941 -0.938018 1.25258 2 :-1.41139e-15 7.63278e-17 -1.78201 0.531121 3 :-4.30552e-16 0 0.659083 -2.18152 Working on column K = 2 Householder matrix H: Col: 0 1 2 3 Row 0 : 1 0 0 0 1 : 0 1 0 0 2 : 0 0 -0.937906 0.346889 3 : 0 0 0.346889 0.937906 Product H*A: Col: 0 1 2 3 Row 0 : -7.01042 -1.53117 -3.27924 -5.24844 1 : 1.59868e-15 2.00941 -0.938018 1.25258 2 : 1.17439e-15 -7.15884e-17 1.89999 -1.25489 3 : -8.9341e-16 2.64773e-17 1.11022e-16 -1.86183 R8VEC_HOUSE_COLUMN_TEST Normal end of execution. R8VEC_NORM_TEST Python version: 3.6.5 R8VEC_NORM computes the L2 norm of an R8VEC. Input vector: 0: 0.218418 1: 0.956318 2: 0.829509 3: 0.561695 4: 0.415307 5: 0.0661187 6: 0.257578 7: 0.109957 8: 0.043829 9: 0.633966 L2 norm = 1.62017 R8VEC_NORM_TEST: Normal end of execution. R8VEC_NORM_AFFINE_TEST Python version: 3.6.5 R8VEC_NORM_AFFINE computes the L2 norm of the difference of two R8VECs. R8VEC_NORM_AFFINE(X,Y) = 1.22756 R8VEC_NORM (X-Y): 1.22756 R8VEC_NORM_AFFINE_TEST: Normal end of execution. R8VEC_PRINT_TEST Python version: 3.6.5 R8VEC_PRINT prints an R8VEC. Here is an R8VEC: 0: 123.456 1: 5e-06 2: -1e+06 3: 3.14159 R8VEC_PRINT_TEST: Normal end of execution. R8VEC_UNIFORM_01_TEST Python version: 3.6.5 R8VEC_UNIFORM_01 computes a random R8VEC. Initial seed is 123456789 Random R8VEC: 0: 0.218418 1: 0.956318 2: 0.829509 3: 0.561695 4: 0.415307 5: 0.0661187 6: 0.257578 7: 0.109957 8: 0.043829 9: 0.633966 R8VEC_UNIFORM_01_TEST: Normal end of execution. R8VEC_UNIFORM_AB_TEST Python version: 3.6.5 R8VEC_UNIFORM_AB computes a random R8VEC. -1 <= X <= 5 Initial seed is 123456789 Random R8VEC: 0: 0.31051 1: 4.73791 2: 3.97706 3: 2.37017 4: 1.49184 5: -0.603288 6: 0.545467 7: -0.340259 8: -0.737026 9: 2.80379 R8VEC_UNIFORM_AB_TEST: Normal end of execution. R83_CG_TEST Python version: 3.6.5 R83_CG applies CG to an R83 matrix. Number of variables N = 10 Norm of residual ||Ax-b|| = 1.32821e-15 Norm of error ||x1-x2|| = 6.30791e-16 R83_CG_TEST Normal end of execution. R83S_CG_TEST Python version: 3.6.5 R83S_CG applies CG to an R83S matrix. Number of variables N = 10 Norm of residual ||Ax-b|| = 1.32821e-15 Norm of error ||x1-x2|| = 6.30791e-16 R83T_CG_TEST Python version: 3.6.5 R83T_CG applies CG to an R83T matrix. Number of variables N = 10 Norm of residual ||Ax-b|| = 1.32821e-15 Norm of error ||x1-x2|| = 6.30791e-16 R8GE_CG_TEST Python version: 3.6.5 R8GE_CG applies CG to an R8GE matrix. Number of variables N = 10 Norm of residual ||Ax-b|| = 1.72262e-14 Norm of error ||x1-x2|| = 1.84883e-14 R8GE_CG_TEST Normal end of execution. R8PBU_CG_TEST Python version: 3.6.5 R8PBU_CG applies CG to an R8PBU matrix. Number of variables N = 10 Norm of residual ||Ax-b|| = 1.32821e-15 Norm of error ||x1-x2|| = 6.30791e-16 R8SD_CG_TEST Python version: 3.6.5 R8SD_CG applies CG to an R8SD matrix. Number of variables N = 10 Norm of residual ||Ax-b|| = 1.32821e-15 Norm of error ||x1-x2|| = 6.30791e-16 CG_TEST: Normal end of execution. Wed Sep 12 21:11:22 2018