1 | /******************************************************* |
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2 | A simple program that demonstrates NewMat10 library. |
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3 | The program defines a random symmetric matrix |
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4 | and computes its eigendecomposition. |
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5 | For further details read the NewMat10 Reference Manual |
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6 | ********************************************************/ |
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7 | |
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8 | |
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9 | #define WANT_STREAM |
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10 | #define WANT_MATH |
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11 | #define WANT_FSTREAM |
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12 | |
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13 | |
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14 | |
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15 | #include <stdlib.h> |
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16 | #include <time.h> |
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17 | #include <string.h> |
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18 | |
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19 | // the following two are needed for printing |
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20 | #include <iostream.h> |
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21 | #include <iomanip.h> |
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22 | /************************************** |
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23 | The NewMat10 include files */ |
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24 | #include "include.h" |
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25 | #include "newmat.h" |
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26 | #include "newmatap.h" |
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27 | #include "newmatio.h" |
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28 | /***************************************/ |
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29 | |
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30 | |
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31 | #ifdef use_namespace |
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32 | using namespace RBD_LIBRARIES; |
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33 | #endif |
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34 | |
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35 | int main(int argc, char **argv) { |
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36 | |
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37 | int M = 3, N = 5; |
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38 | Matrix X(M,N); // Define an M x N general matrix |
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39 | |
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40 | // Fill X by random numbers between 0 and 9 |
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41 | // Note that indexing into matrices in NewMat is 1-based! |
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42 | srand(time(NULL)); |
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43 | for (int i = 1; i <= M; ++i) { |
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44 | for (int j = 1; j <= N; ++j) { |
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45 | X(i,j) = rand() % 10; |
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46 | } |
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47 | } |
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48 | |
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49 | SymmetricMatrix C; |
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50 | C << X * X.t(); // fill in C by X * X^t. |
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51 | // Works because we *know* that the result is symmetric |
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52 | |
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53 | cout << "The symmetrix matrix C" << endl; |
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54 | cout << setw(5) << setprecision(0) << C << endl; |
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55 | |
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56 | |
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57 | // compute eigendecomposition of C |
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58 | Matrix V(3,3); // for eigenvectors |
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59 | DiagonalMatrix D(3); // for eigenvalues |
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60 | |
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61 | // the decomposition |
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62 | Jacobi(C, D, V); |
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63 | |
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64 | // Print the result |
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65 | cout << "The eigenvalues matrix:" << endl; |
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66 | cout << setw(10) << setprecision(5) << D << endl; |
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67 | cout << "The eigenvectors matrix:" << endl; |
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68 | cout << setw(10) << setprecision(5) << V << endl; |
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69 | |
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70 | // Check that the first eigenvector indeed has the eigenvector property |
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71 | ColumnVector v1(3); |
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72 | v1(1) = V(1,1); |
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73 | v1(2) = V(2,1); |
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74 | v1(3) = V(3,1); |
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75 | |
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76 | ColumnVector Cv1 = C * v1; |
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77 | ColumnVector lambda1_v1 = D(1) * v1; |
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78 | |
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79 | cout << "The max-norm of the difference between C*v1 and lambda1*v1 is " << |
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80 | NormInfinity(Cv1 - lambda1_v1) << endl << endl; |
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81 | |
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82 | // Build the inverse and check the result |
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83 | Matrix Ci = C.i(); |
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84 | Matrix I = Ci * C; |
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85 | |
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86 | cout << "The inverse of C is" << endl; |
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87 | cout << setw(10) << setprecision(5) << Ci << endl; |
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88 | cout << "And the inverse times C is identity" << endl; |
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89 | cout << setw(10) << setprecision(5) << I << endl; |
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90 | |
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91 | // Example for multiple solves (see NewMat documentation) |
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92 | ColumnVector r1(3), r2(3); |
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93 | for (int i = 1; i <= 3; ++i) { |
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94 | r1(i) = rand() % 10; |
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95 | r2(i) = rand() % 10; |
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96 | } |
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97 | LinearEquationSolver CLU = C; // decomposes C |
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98 | ColumnVector s1 = CLU.i() * r1; |
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99 | ColumnVector s2 = CLU.i() * r2; |
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100 | |
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101 | cout << "solution for right hand side r1" << endl; |
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102 | cout << setw(10) << setprecision(5) << s1 << endl; |
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103 | cout << "solution for right hand side r2" << endl; |
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104 | cout << setw(10) << setprecision(5) << s2 << endl; |
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105 | |
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106 | return 0; |
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107 | } |
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