1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Sparse> 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <vector> 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtypedef Eigen::SparseMatrix<double> SpMat; // declares a column-major sparse matrix type of double 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtypedef Eigen::Triplet<double> T; 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid buildProblem(std::vector<T>& coefficients, Eigen::VectorXd& b, int n); 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid saveAsBitmap(const Eigen::VectorXd& x, int n, const char* filename); 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathint main(int argc, char** argv) 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int n = 300; // size of the image 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int m = n*n; // number of unknows (=number of pixels) 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Assembly: 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<T> coefficients; // list of non-zeros coefficients 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::VectorXd b(m); // the right hand side-vector resulting from the constraints 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath buildProblem(coefficients, b, n); 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SpMat A(m,m); 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath A.setFromTriplets(coefficients.begin(), coefficients.end()); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Solving: 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::SimplicialCholesky<SpMat> chol(A); // performs a Cholesky factorization of A 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::VectorXd x = chol.solve(b); // use the factorization to solve for the given right hand side 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Export the result to a file: 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath saveAsBitmap(x, n, argv[1]); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return 0; 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33