/* * This example shows how to use the class RWGenFact<T> * to do linear algebra with double precision matrices. */ // Include the RWGenFact<T> class header file: #include <rw/math/genfact.h> // Include the double precision matrix header file: #include <rw/math/genmat.h> #include <iostream> // Initial data for the vectors: const double adata[] = {-3.0, 2.0, 1.0, 8.0, -7.0, 9.0, 5.0, 4.0, -6.0}; const double arhs[] = {6.0, 9.0, 1.0}; int main() { // Construct a test matrix and print it out: RWGenMat<double> testmat(adata,3,3); std::cout << "test matrix:\n" << testmat << "\n"; /* * Calculate and print the inverse. * Note that a type conversion occurs: * testmat is converted to type RWGenFact<double> * before the inverse is computed. */ std::cout << "inverse:\n" << inverse(testmat); // Now construct an RWGenFact<double> from the matrix: RWGenFact<double> LUtest(testmat); // Once constructed, LUtest may be reused as required. // Find the determinant: std::cout << "\ndeterminant\n" << determinant(LUtest) << std::endl; // Solve the linear system testmat*x = rhs: RWMathVec<double> rhs(arhs, 3); RWMathVec<double> x = solve(LUtest, rhs); std::cout << "solution:\n" << x << "\n"; }

Sample Input:

None required.

Sample Output (note that the exact numbers depend on machine precision):

test matrix: 3X3 [ -3 8 5 2 -7 4 1 9 -6 ] inverse: 3X3 [ 0.025532 0.395745 0.285106 0.068085 0.055319 0.093617 0.106383 0.148936 0.021277 ] determinant: 235 solution: [ 4 1 2 ]