eigen库支持稀疏矩阵

#include <Eigen/Sparse>
#include <vector>
#include <iostream>
typedef Eigen::SparseMatrix<double> SpMat; // declares a column-major sparse matrix type of double
typedef Eigen::Triplet<double> T;
int main(int argc, char** argv)
{
int n = 300; // size of the image
int m = n*n; // number of unknows (=number of pixels)
// Assembly:
std::vector<T> coefficients; // list of non-zeros coefficients
Eigen::Vector3d b; // the right hand side-vector resulting from the constraints
//buildProblem(coefficients, b, n);
//coefficients
coefficients.push_back(T(0,0,1));
coefficients.push_back(T(0,0,1));
coefficients.push_back(T(0,0,1));
coefficients.push_back(T(0,2,1));
coefficients.push_back(T(1,1,1));
coefficients.push_back(T(1,2,1));
coefficients.push_back(T(2,0,1));
coefficients.push_back(T(2,1,4));
//coefficients END
//b
b = Eigen::Vector3d(17,40,29);
//b END
//buildProblem(coefficients, b, n); END
std::cout << b << std::endl;
SpMat A(3,3);
A.setFromTriplets(coefficients.begin(), coefficients.end());
std::cout << A << std::endl;
// Solving:
Eigen::SimplicialCholesky<SpMat> chol; // performs a Cholesky factorization of A
chol.compute(A);
Eigen::VectorXd x = chol.solve(b); // use the factorization to solve for the given right hand side
// Export the result to a file:
std::cout << x << std::endl;


return 0;
}