27-Sep-2022 15:59:24 web_matrix_test(): MATLAB/Octave version 4.2.2 Test web_matrix() Process the "tiny" web matrix power_rank(): Given an NxN incidence matrix A, compute the transition matrix T, Then start with a vector of N values 1/N, and repeatedly compute x <= T*x After many steps, compare last three iterates. If they are close, we are probably at an eigenvector associated with the eigenvalue 1. x, T*x, T*T*x 1.00000 1.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Process the "moler" web matrix power_rank(): Given an NxN incidence matrix A, compute the transition matrix T, Then start with a vector of N values 1/N, and repeatedly compute x <= T*x After many steps, compare last three iterates. If they are close, we are probably at an eigenvector associated with the eigenvalue 1. x, T*x, T*T*x 0.0109619 0.0106074 0.0102642 0.0056642 0.0054810 0.0053037 0.0029268 0.0028321 0.0027405 0.0039350 0.0038077 0.0036845 0.9698397 0.9708153 0.9717594 0.0066724 0.0064566 0.0062477 Process the "sauer" web matrix power_rank(): Given an NxN incidence matrix A, compute the transition matrix T, Then start with a vector of N values 1/N, and repeatedly compute x <= T*x After many steps, compare last three iterates. If they are close, we are probably at an eigenvector associated with the eigenvalue 1. x, T*x, T*T*x 0.015444 0.015444 0.015444 0.011583 0.011583 0.011583 0.011583 0.011583 0.011583 0.015444 0.015444 0.015444 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.030888 0.081081 0.081081 0.081081 0.110039 0.110039 0.110039 0.110039 0.110039 0.110039 0.081081 0.081081 0.081081 0.146718 0.146718 0.146718 0.146718 0.146718 0.146718 0.146718 0.146718 0.146718 web_matrix_test(): Normal end of execution. 27-Sep-2022 15:59:24