void test3() {
#ifdef MAKE_TEST3
std::cout << "Multidimensional test:" << std::endl;
// Construct data for the IVP
double T = 1;
// Many dimensions
int n = 5;
// Multidimensional rhs
Eigen::VectorXd y0(2*n);
for(int i = 0; i < n; ++i) {
y0(i)=(i+1.)/n;
y0(i+n)=-1;
}
// Multidimensional rhs
auto f = [n] (Eigen::VectorXd y) {
Eigen::VectorXd fy(2*n);
Eigen::VectorXd g(n);
g(0) = y(0)*(y(1)+y(0));
g(n-1) = y(n-1)*(y(n-1)+y(n-2));
for(int i = 1; i < n-1; ++i) {
g(i) = y(i)*(y(i-1)+y(i+1));
}
Eigen::SparseMatrix<double> C(n,n);
C.reserve(3);
for(int i = 0; i < n; ++i) {
C.insert(i,i) = 2;
if(i < n-1) C.insert(i,i+1) = -1;
if(i >= 1) C.insert(i,i-1) = -1;
}
C.makeCompressed();
fy.head(n) = y.head(n);
Eigen::SparseLU< Eigen::SparseMatrix<double> > solver;
solver.analyzePattern(C);
solver.compute(C);
fy.tail(n) = solver.solve(g);
return fy;
};
// Constructor:
ode45<Eigen::VectorXd> O(f);
// Setup options
O.options.do_statistics = true;
// Solve
auto sol = O.solve(y0, T);
// Print info
O.print();
std::cout << "T = " << sol.back().second << std::endl;
std::cout << "y(T) = " << std::endl << sol.back().first << std::endl;
#endif
}
NatCSI(const std::vector<double> & t, const std::vector<double> & y)
: t(t), y(y), h(t.size()-1), c(t.size()) {
// Size check
assert( ( t.size() == y.size() ) && "Error: mismatched size of t and y!");
// m is the number of conditions (t goes from t_0 to t_n)
// WARNING: m = n+1
m = t.size();
// Vector containing increments (from the right)
for(int i = 0; i < (m - 1); ++i) {
h(i) = t[i+1] - t[i];
// Check that t is sorted
assert( ( h(i) > 0 ) && "Error: array t must be sorted!");
}
// System matrix and rhs as in 3.5.9, we remove first and last row (known by natural contition)
Eigen::SparseMatrix<double> A(m,m);
Eigen::VectorXd b(m);
// WARNING: sparse reserve space
A.reserve(3);
// Fill in natural conditions 3.5.10 for matrix
A.coeffRef(0,0) = 2 / h(0);
A.coeffRef(0,1) = 1 / h(0);
A.coeffRef(m-1,m-2) = 1 / h(m-2);
A.coeffRef(m-1,m-1) = 2 / h(m-2);
// Reuse computation for rhs
double bold = (y[1] - y[0]) / (h(0)*h(0));
b(0) = 3*bold; // Fill in natural conditions 3.5.10
// Fill matrix A and rhs b
for(int i = 1; i < m-1; ++i) {
// PRecompute b_i
double hinv = 1./h(i);
// Fill in a
A.coeffRef(i,i-1) = hinv;
A.coeffRef(i,i) = 2./h(i) + 2./h(i-1);
A.coeffRef(i,i+1) = hinv;
// Reuse computation for rhs b
double bnew = (y[i+1] - y[i]) / (h(i)*h(i));
b(i) = 3. * (bnew + bold);
bold = bnew;
}
b(m-1) = 3*bold; // Fill in natural conditions 3.5.10
// Compress the matrix
A.makeCompressed();
std::cout << A;
// Factorize A and solve system A*c(1:end) = b
Eigen::SparseLU<Eigen::SparseMatrix<double>> lu;
lu.compute(A);
c = lu.solve(b);
}
void testEigen(int m, int n, int nnz, std::vector<int>& rows, std::vector<int>& cols,
std::vector<double>& values, double* matB){
double start, stop, time_to_solve, time_to_build;
double tol=1e-9;
Eigen::SparseMatrix<double> A;
std::vector< Eigen::Triplet<double> > trips;
trips.reserve(m * n);
for (int k = 0; k < nnz; k++){
double _val = values[k];
int i = rows[k];
int j = cols[k];
if (fabs(_val) > tol){
trips.push_back(Eigen::Triplet<double>(i-1,j-1,_val));
}
}
//NOTE: setFromTriples() accumulates contributions to the same (i,j)!
A.resize(m, n);
start = second();
A.setFromTriplets(trips.begin(), trips.end());
stop = second();
time_to_build = stop - start;
Eigen::SparseLU< Eigen::SparseMatrix<double>, Eigen::COLAMDOrdering<int> > solverLU;
Eigen::VectorXd b; b.resize(m);
for (int i = 0; i < m; i++ ) b(i) = matB[i];
printf("\nProcessing in Eigen using LU...\n");
start = second();
solverLU.compute(A);
Eigen::VectorXd X = solverLU.solve(b);
stop = second();
time_to_solve = stop - start;
Eigen::VectorXd ax = A * X;
Eigen::VectorXd bMinusAx = b - ax;
double h_r[m];
for (int i=0; i<m; i++) h_r[i]=bMinusAx(i);
double r_inf = vec_norminf(m, h_r);
printf("(Eigen) |b - A*x| = %E \n", r_inf);
printf("(Eigen) Time to build(sec): %f\n", time_to_build);
printf("(Eigen) Time (sec): %f\n", time_to_solve);
}
//************************************
// Method: SolvePFC
// FullName: PFCal::PFC::SolvePFC
// Access: public
// Returns: PF_RESULT
// Qualifier: 潮流计算模板函数，构造各种方法求解线性方程组
//************************************
PFVoid PFC::Solve(){
PFDouble opml(0.0);
Eigen::SparseLU<PFCore::PFSMatrixXD, PFCore::PFCOLAMDOrdering> solver;
PFCIter = 0;
//0. 初始化计算矩阵及向量大小
this->_InitPFCalMatrix();
do {
//1. 求解功率失配量
cout << "QG DISPATCH..." << endl;
_MakeDPQVector();
//2. 如果达到收敛条件，退出
cout << "MAXDISPATCH\t" << PFCIter << "\t" << dPFCPQ.cwiseAbs().maxCoeff() << endl;
if (dPFCPQ.cwiseAbs().maxCoeff() < PFCEpsm){
this->PFCResult = PF_RESULT_CONVERG;
return;
}
//3. 计算雅可比阵
_MakeJacoMatrix();
//4. 计算电压偏差
solver.compute(PFCJaco);
if (solver.info() != Eigen::Success){
cout << "Solving<stage_1> Failed!" << endl;
this->PFCResult = PF_RESULT_DIVERGE_FAILURE;
return;
}
dPFCVA = solver.solve(dPFCPQ);
if (solver.info() != Eigen::Success){
cout << "Solving<stage_2> Failed!" << endl;
this->PFCResult = PF_RESULT_DIVERGE_FAILURE;
return;
}
//5. 检测是否出现NAN
if(dPFCVA.hasNaN()){
this->PFCResult = PF_RESULT_DIVERGE_NAN;
return;
}
//6. 更新状态量
cout << "VOL DISPATCH..." << endl;
opml = _CalOptimalMultiplier();
dPFCVA *= opml;
_UpdateSysState();
//7. 迭代次数增加
++PFCIter;
} while (PFCIter <= PFCMaxIter);
//迭代次数越限
if (PFCIter > PFCMaxIter){
this->PFCResult = PF_RESULT_DIVERGE_OVER_ITER;
return;
}
this->PFCResult = PF_RESULT_DIVERGE;
return;
}
Tvector<T> Tsparse_matrix<T>::solve( const Tvector<T>& b ) const
{
assert( ROWS == b.size() ); // Check dimensions are compatible
// Convert Tvector to an Eigen matrix
Eigen::Matrix<T, -1, 1> B;
B.resize(b.size(),1);
for (std::size_t i=0; i<b.size(); ++i)
{
B(i,0) = b.CONTAINER[i];
}
// Convert Tsparse_matrix to an Eigen::SparseMatrix
Eigen::SparseMatrix<T> A(ROWS,COLS); // Declare Eigen sparse matrix
std::vector<Eigen::Triplet<T>> triplet_list; // Declare list of triplets
for ( std::size_t i=0; i<ROWS; ++i )
{
if ( !S_MATRIX[i].isempty() ) // Check that the row is not empty
{
std::vector<std::size_t> index;
std::vector<T> element;
index = S_MATRIX[i].index_list();
element = S_MATRIX[i].element_list();
std::size_t J = index.size();
for ( std::size_t j=0; j<J; ++j )
{
triplet_list.push_back( Eigen::Triplet<T>( i, index[j], element[j] ));
}
}
}
A.setFromTriplets(triplet_list.begin(), triplet_list.end());
// Setup and solve the system
Eigen::SparseLU< Eigen::SparseMatrix<T> > solverA;
//Eigen::BiCGSTAB<Eigen::SparseMatrix<T>> solverA;
//solverA.analyzePattern(A);
//solverA.factorize(A);
solverA.compute(A);
Eigen::Matrix<T, -1, 1> X;
X.resize(b.size(),1);
X = solverA.solve(B);
// Convert back to a Tvector
Tvector<T> x(COLS,0.0);
for (std::size_t i=0; i<COLS; ++i)
{
x.CONTAINER[i] = X(i,0);
}
return x;
}
int main() {
// Construct data for RK order 4
MatrixXd A = MatrixXd::Zero(4,4);
A(1,0) = .5;
A(2,1) = .5;
A(3,2) = 1;
VectorXd b(4);
b << 1./6, 1./3, 1./3, 1./6;
// Construct data for the IVP
double T = 1;
int n = 5;
VectorXd y0(2*n);
for(int i = 0; i < n; ++i) {
y0(i)=(i+1.)/n;
y0(i+n)=-1;
}
auto f = [n] (VectorXd y) {
VectorXd fy(2*n);
VectorXd g(n);
g(0) = y(0)*(y(1)+y(0));
g(n-1) = y(n-1)*(y(n-1)+y(n-2));
for(int i = 1; i < n-1; ++i) {
g(i) = y(i)*(y(i-1)+y(i+1));
}
Eigen::SparseMatrix<double> C(n,n);
C.reserve(3);
for(int i = 0; i < n; ++i) {
C.insert(i,i) = 2;
if(i < n-1) C.insert(i,i+1) = -1;
if(i >= 1) C.insert(i,i-1) = -1;
}
C.makeCompressed();
fy.head(n) = y.head(n);
Eigen::SparseLU< Eigen::SparseMatrix<double> > solver;
solver.analyzePattern(C);
solver.compute(C);
fy.tail(n) = solver.solve(g);
return fy;
};
errors(f, T, y0, A, b);
}
void ReliefGeneration::CompressHeightField(std::vector<double>& heightField, int resX, int resY)
{
double bbScale = 2;
double alpha = bbScale * 300.0;
double threthold = bbScale * 0.05;
int vertNum = (resX + 1) * (resY + 1);
std::vector<MagicMath::Vector3> DeltaVector(vertNum);
for (int xid = 0; xid < resX; xid++)
{
for (int yid = 0; yid < resY; yid++)
{
int index = xid * (resY + 1) + yid;
MagicMath::Vector3 deltaT(0, 0, 0);
deltaT[0] = heightField.at(index + resY + 1) - heightField.at(index);
deltaT[1] = heightField.at(index + 1) - heightField.at(index);
double deltaMag = deltaT.Normalise();
if (deltaMag > threthold)
{
deltaMag = 0;
}
else
{
deltaMag = log(1 + alpha * deltaMag) / alpha;
}
DeltaVector.at(index) = deltaT * deltaMag;
}
}
std::vector<double> LaplaceField(vertNum);
for (int xid = 1; xid < resX; xid++)
{
for (int yid = 1; yid < resY; yid++)
{
int index = xid * (resY + 1) + yid;
LaplaceField.at(index) = DeltaVector.at(index)[0] - DeltaVector.at(index - resY - 1)[0] +
DeltaVector.at(index)[1] - DeltaVector.at(index - 1)[1];
}
}
DebugLog << "Relief: Construct Matrix" << std::endl;
std::vector< Eigen::Triplet<double> > tripletList;
Eigen::VectorXd b(vertNum, 1);
for (int xid = 0; xid < resX + 1; xid++)
{
for (int yid = 0; yid < resY + 1; yid++)
{
int index = xid * (resY + 1) + yid;
if (xid == 0 || xid == resX || yid == 0 || yid == resY)
{
tripletList.push_back( Eigen::Triplet<double>(index, index, 1) );
b(index) = 0;
}
else
{
tripletList.push_back( Eigen::Triplet<double>(index, index, -4.0) );
tripletList.push_back( Eigen::Triplet<double>(index, index + 1, 1.0) );
tripletList.push_back( Eigen::Triplet<double>(index, index - 1, 1.0) );
tripletList.push_back( Eigen::Triplet<double>(index, index + resY + 1, 1.0) );
tripletList.push_back( Eigen::Triplet<double>(index, index - resY - 1, 1.0) );
b(index) = LaplaceField.at(index);
}
}
}
DebugLog << "Relief: Solve Matrix" << std::endl;
Eigen::SparseMatrix<double, Eigen::ColMajor> matA(vertNum,vertNum);
matA.setFromTriplets(tripletList.begin(), tripletList.end());
Eigen::SparseLU<Eigen::SparseMatrix<double, Eigen::ColMajor> > solver;
solver.compute(matA);
if(solver.info()!= Eigen::Success)
{
DebugLog << "Relief: SuperLU Failed" << std::endl;
}
Eigen::VectorXd res = solver.solve(b);
//Copy results
for (int i = 0; i < vertNum; i++)
{
heightField.at(i) = res(i);
}
}
IGL_INLINE void igl::PolyVectorFieldFinder<DerivedV, DerivedF>::
minQuadWithKnownMini(const Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar> > &Q,
const Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar> > &f,
const Eigen::VectorXi isConstrained,
const Eigen::Matrix<std::complex<typename DerivedV::Scalar>, Eigen::Dynamic, 1> &xknown,
Eigen::Matrix<std::complex<typename DerivedV::Scalar>, Eigen::Dynamic, 1> &x)
{
int N = Q.rows();
int nc = xknown.rows();
Eigen::VectorXi known; known.setZero(nc,1);
Eigen::VectorXi unknown; unknown.setZero(N-nc,1);
int indk = 0, indu = 0;
for (int i = 0; i<N; ++i)
if (isConstrained[i])
{
known[indk] = i;
indk++;
}
else
{
unknown[indu] = i;
indu++;
}
Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar>> Quu, Quk;
igl::slice(Q,unknown, unknown, Quu);
igl::slice(Q,unknown, known, Quk);
std::vector<typename Eigen::Triplet<std::complex<typename DerivedV::Scalar> > > tripletList;
Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar> > fu(N-nc,1);
igl::slice(f,unknown, Eigen::VectorXi::Zero(1,1), fu);
Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar> > rhs = (Quk*xknown).sparseView()+.5*fu;
Eigen::SparseLU< Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar>>> solver;
solver.compute(-Quu);
if(solver.info()!=Eigen::Success)
{
std::cerr<<"Decomposition failed!"<<std::endl;
return;
}
Eigen::SparseMatrix<std::complex<typename DerivedV::Scalar>> b = solver.solve(rhs);
if(solver.info()!=Eigen::Success)
{
std::cerr<<"Solving failed!"<<std::endl;
return;
}
indk = 0, indu = 0;
x.setZero(N,1);
for (int i = 0; i<N; ++i)
if (isConstrained[i])
x[i] = xknown[indk++];
else
x[i] = b.coeff(indu++,0);
}
void
poisson_blend(mve::FloatImage::ConstPtr src, mve::ByteImage::ConstPtr mask,
mve::FloatImage::Ptr dest, float alpha) {
assert(src->width() == mask->width() && mask->width() == dest->width());
assert(src->height() == mask->height() && mask->height() == dest->height());
assert(src->channels() == 3 && dest->channels() == 3);
assert(mask->channels() == 1);
assert(valid_mask(mask));
const int n = dest->get_pixel_amount();
const int width = dest->width();
const int height = dest->height();
const int channels = dest->channels();
mve::Image<int>::Ptr indices = mve::Image<int>::create(width, height, 1);
indices->fill(-1);
int index = 0;
for (int i = 0; i < n; ++i) {
if (mask->at(i) != 0) {
indices->at(i) = index;
index++;
}
}
const int nnz = index;
std::vector<math::Vec3f> coefficients_b;
coefficients_b.resize(nnz);
std::vector<Eigen::Triplet<float, int> > coefficients_A;
coefficients_A.reserve(nnz); //TODO better estimate...
for (int i = 0; i < n; ++i) {
const int row = indices->at(i);
if (mask->at(i) == 126 || mask->at(i) == 128) {
Eigen::Triplet<float, int> t(row, row, 1.0f);
coefficients_A.push_back(t);
coefficients_b[row] = math::Vec3f(&dest->at(i, 0));
}
if (mask->at(i) == 255) {
const int i01 = indices->at(i - width);
const int i10 = indices->at(i - 1);
const int i11 = indices->at(i);
const int i12 = indices->at(i + 1);
const int i21 = indices->at(i + width);
/* All neighbours should be eighter border conditions or part of the optimization. */
assert(i01 != -1 && i10 != -1 && i11 != -1 && i12 != -1 && i21 != -1);
Eigen::Triplet<float, int> t01(row, i01, 1.0f);
Eigen::Triplet<float, int> t10(row, i10, 1.0f);
Eigen::Triplet<float, int> t11(row, i11, -4.0f);
Eigen::Triplet<float, int> t12(row, i12, 1.0f);
Eigen::Triplet<float, int> t21(row, i21, 1.0f);
Eigen::Triplet<float, int> triplets[] = {t01, t10, t11, t12, t21};
coefficients_A.insert(coefficients_A.end(), triplets, triplets + 5);
math::Vec3f l_d = simple_laplacian(i, dest);
math::Vec3f l_s = simple_laplacian(i, src);
coefficients_b[row] = (alpha * l_s + (1.0f - alpha) * l_d);
}
}
SpMat A(nnz, nnz);
A.setFromTriplets(coefficients_A.begin(), coefficients_A.end());
Eigen::SparseLU<SpMat, Eigen::COLAMDOrdering<int> > solver;
solver.compute(A);
for (int channel = 0; channel < channels; ++channel) {
Eigen::VectorXf b(nnz);
for (std::size_t i = 0; i < coefficients_b.size(); ++i)
b[i] = coefficients_b[i][channel];
Eigen::VectorXf x(n);
x = solver.solve(b);
for (int i = 0; i < n; ++i) {
int index = indices->at(i);
if (index != -1) dest->at(i, channel) = x[index];
}
}
}
int main()
{
cout << "----- TESTING SparseMatrix -----" << endl;
/* ----- TESTING sparse linear system solver ----- */
/* initialize random seed: */
srand (time(NULL));
size_t N = 2000;
TSL::SparseMatrix<double> A_matrix(N,N);
/*for (size_t i=0; i<N; ++i)
{
A_matrix( i, i ) = rand() * (i + rand());
A_matrix( i, 10 ) = rand() * (i + rand());
A_matrix( i, 57 ) = rand() * (i + rand());
}*/
for (size_t i = 0; i < N; ++i)
{
for (size_t j = 0; j < N; ++j)
{
A_matrix( i, j ) = rand() * i + rand() * j;
}
}
//cout << "A_matrix.rows() = " << A_matrix.rows() << endl;
//cout << "A_matrix.cols() = " << A_matrix.cols() << endl;
//cout << "A_matrix.size() = " << A_matrix.size() << endl;
//cout << "A_matrix.numel() = " << A_matrix.numel() << endl;
//A_matrix(33, 100) = 2.0;
//cout << "A_matrix.numel_row( 33 ) = " << A_matrix.numel_row( 33 ) << endl;
// Test copy constructor
//TSL::SparseMatrix<double> A_copy( A_matrix );
//cout << "A_copy.numel() = " << A_copy.numel() << endl;
TSL::Vector<double> B_vector(N,0.435);
TSL::Vector<double> X_vector(N,0.0);
// Test SparseLU solve method
cout << " * Solving using Eigen and SparseLU " << endl;
TSL::Timer timer;
timer.start();
X_vector = A_matrix.solve( B_vector ); // SparseLU solve
//cout << "B_vector = " << endl << B_vector << endl;
//cout << "X_vector = " << endl << X_vector << endl;
//cout << "X[N-1] = " << X_vector[ N - 1 ] << endl;
//cout << "1 / N = " << 1.0 / N << endl;
timer.print();
timer.stop();
// Test PETSc solve
cout << " * Solving using PETSc " << endl;
PetscInitialize(NULL,NULL,(char*)0,(char*)0);
#ifdef PETSC_Z
// Convert to a complex matrix
TSL::SparseMatrix< std::complex<double> > A_cmplx( N, N );
for (size_t i = 0; i < N; ++i)
{
for (size_t j = 0; j < N; ++j)
{
A_cmplx( i, j ) = A_matrix( i, j );
}
}
TSL::Vector< std::complex<double> > B_cmplx( N, 0.435 );
TSL::PETScSparseLinearSystem< std::complex<double> > petsc_system( &A_cmplx, &B_cmplx );
#endif
#ifdef PETSC_D
TSL::PETScSparseLinearSystem<double> petsc_system( &A_matrix, &B_vector );
#endif
timer.start();
try
{
petsc_system.solve();
}
catch ( std::runtime_error )
{
cout << " \033[1;31;48m * FAILED THROUGH EXCEPTION BEING RAISED \033[0m\n";
return 1;
}
timer.print();
timer.stop();
TSL::Vector<double> diff( N, 0.0 );
for (size_t i = 0; i < N; ++i)
{
#ifdef PETSC_Z
diff[i] = X_vector[i] - B_cmplx[i].real();
#endif
#ifdef PETSC_D
diff[i] = X_vector[i] - B_vector[i];
#endif
}
cout << "diff.norm_inf() = " << diff.norm_inf() << endl;
//cout << "A_matrix( 10, 10 ) = " << A_matrix( 10, 10 ) << endl;
//A_matrix.clear();
//cout << "A_matrix( 10, 10 ) = " << A_matrix( 10, 10 ) << endl;
//A_matrix.eye();
//cout << "A_matrix( 10, 10 ) = " << A_matrix( 10, 10 ) << endl;
//A_matrix.scale( 2.0 );
//cout << "A_matrix( 10, 10 ) = " << A_matrix( 10, 10 ) << endl;
//A_matrix.print();
TSL::SparseMatrix<double> B_matrix(N,N);
B_matrix = A_matrix;
B_matrix = A_matrix * 2.0;
B_matrix = 2.0 * A_matrix;
//B_matrix.print();
TSL::SparseMatrix<double> C_matrix(3,3);
C_matrix(0,0) = 1.0; C_matrix(0,1) = -2.0; //C_matrix(0,2) = 3.0;
C_matrix(1,0) = 5.0; C_matrix(1,1) = 8.0; C_matrix(1,2) = -1.0;
//C_matrix(2,0) = 2.0;
C_matrix(2,1) = 1.0; C_matrix(2,2) = 1.0;
//C_matrix.print();
//C_matrix.output( "./C_matrix" );
//B_matrix = C_matrix;
//cout << "B_matrix = ";
//B_matrix.print();
TSL::SparseMatrix<double> D_matrix(N,N);
Eigen::SparseMatrix<double, Eigen::ColMajor, long long> C_matrix_Eigen(3,3);
C_matrix_Eigen = C_matrix.convert_to_Eigen();
/*for (int k = 0; k < C_matrix_Eigen.outerSize(); ++k){
for (Eigen::SparseMatrix<double, Eigen::ColMajor, long long>::InnerIterator it(C_matrix_Eigen, k); it; ++it){
std::cout << it.row() <<"\t";
std::cout << it.col() << "\t";
std::cout << it.value() << std::endl;
}
}*/
Eigen::SparseLU< Eigen::SparseMatrix<double, Eigen::ColMajor, long long> > solver;
solver.compute( C_matrix_Eigen );
// Test complex sparse matrices
TSL::SparseMatrix<std::complex<double>> E_matrix( 2, 2 );
TSL::Vector<std::complex<double>> E_vector( 2, 0.0 );
E_matrix( 0, 0 ) = std::complex<double>(1.0,1.0);
E_matrix( 0, 1 ) = std::complex<double>(-1.0,0.0);
E_matrix( 1, 0 ) = std::complex<double>(1.0,-1.0);
E_matrix( 1, 1 ) = std::complex<double>(1.0,1.0);
E_vector[ 0 ] = std::complex<double>(0.0,1.0);
E_vector[ 1 ] = std::complex<double>(1.0,0.0);
//E_matrix.print();
TSL::Vector<std::complex<double>> E_sol( 2, 0.0 );
E_sol = E_matrix.solve( E_vector );
//cout << " E_sol = " << endl << E_sol << endl;
cout << "FINISHED" << endl;
}
