/** * \file * \brief [LU decomposition](https://en.wikipedia.org/wiki/LU_decompositon) of a * square matrix * \author [Krishna Vedala](https://github.com/kvedala) */ #include #include #include #include #ifdef _OPENMP #include #endif /** Perform LU decomposition on matrix * \param[in] A matrix to decompose * \param[out] L output L matrix * \param[out] U output U matrix * \returns 0 if no errors * \returns negative if error occurred */ int lu_decomposition(const std::vector> &A, std::vector> *L, std::vector> *U) { int row, col, j; int mat_size = A.size(); if (mat_size != A[0].size()) { // check matrix is a square matrix std::cerr << "Not a square matrix!\n"; return -1; } // regularize each row for (row = 0; row < mat_size; row++) { // Upper triangular matrix #ifdef _OPENMP #pragma omp for #endif for (col = row; col < mat_size; col++) { // Summation of L[i,j] * U[j,k] double lu_sum = 0.; for (j = 0; j < row; j++) lu_sum += L[0][row][j] * U[0][j][col]; // Evaluate U[i,k] U[0][row][col] = A[row][col] - lu_sum; } // Lower triangular matrix #ifdef _OPENMP #pragma omp for #endif for (col = row; col < mat_size; col++) { if (row == col) { L[0][row][col] = 1.; continue; } // Summation of L[i,j] * U[j,k] double lu_sum = 0.; for (j = 0; j < row; j++) lu_sum += L[0][col][j] * U[0][j][row]; // Evaluate U[i,k] L[0][col][row] = (A[col][row] - lu_sum) / U[0][row][row]; } } return 0; } /** * operator to print a matrix */ template std::ostream &operator<<(std::ostream &out, std::vector> const &v) { const int width = 10; const char separator = ' '; for (size_t row = 0; row < v.size(); row++) { for (size_t col = 0; col < v[row].size(); col++) out << std::left << std::setw(width) << std::setfill(separator) << v[row][col]; out << std::endl; } return out; } /** Main function */ int main(int argc, char **argv) { int mat_size = 3; // default matrix size const int range = 50; const int range2 = range >> 1; if (argc == 2) mat_size = atoi(argv[1]); std::srand(std::time(NULL)); // random number initializer /* Create a square matrix with random values */ std::vector> A(mat_size); std::vector> L(mat_size); // output std::vector> U(mat_size); // output for (int i = 0; i < mat_size; i++) { // calloc so that all valeus are '0' by default A[i] = std::vector(mat_size); L[i] = std::vector(mat_size); U[i] = std::vector(mat_size); for (int j = 0; j < mat_size; j++) /* create random values in the limits [-range2, range-1] */ A[i][j] = static_cast(std::rand() % range - range2); } std::clock_t start_t = std::clock(); lu_decomposition(A, &L, &U); std::clock_t end_t = std::clock(); std::cout << "Time taken: " << static_cast(end_t - start_t) / CLOCKS_PER_SEC << "\n"; std::cout << "A = \n" << A << "\n"; std::cout << "L = \n" << L << "\n"; std::cout << "U = \n" << U << "\n"; return 0; }