Measurement::Measurement(MatrixXcd observable)
{
ComplexEigenSolver<MatrixXcd> solver(observable);
MatrixXcd vectors = solver.eigenvectors();
for (int i = 0; i < vectors.cols(); ++i)
addOperator(vectors.col(i) * vectors.col(i).transpose(), i_to_string(i));
_checkOperatorsAreValid();
}
Rcpp::List EigsGen::extract()
{
int nconv = iparam[5 - 1];
int niter = iparam[9 - 1];
// Sometimes there are nconv = nev + 1 converged eigenvalues,
// mainly due to pairs of complex eigenvalues.
// We will truncate at nev.
int truenconv = nconv > nev ? nev : nconv;
// Converged eigenvalues from aupd()
VectorXcd evalsConverged(nconv);
evalsConverged.real() = MapVec(workl + ncv * ncv, nconv);
evalsConverged.imag() = MapVec(workl + ncv * ncv + ncv, nconv);
// If only eigenvalues are requested
if(!retvec)
{
if(nconv < nev)
::Rf_warning("only %d eigenvalues converged, less than k", nconv);
sortDesc(evalsConverged);
if(evalsConverged.size() > truenconv)
evalsConverged.conservativeResize(truenconv);
return returnResult(returnRealIfPossible(evalsConverged),
R_NilValue, wrap(truenconv), wrap(niter));
}
// Recompute the Hessenburg matrix, since occasionally
// aupd() will give us the incorrect one
recomputeH();
MapMat Hm(workl, ncv, ncv);
MapMat Vm(V, n, ncv);
RealSchur<MatrixXd> schur(Hm);
MatrixXd Qm = schur.matrixU();
MatrixXd Rm = schur.matrixT();
VectorXcd evalsRm(ncv);
VectorXi selectInd(nconv);
eigenvalueSchur(Rm, evalsRm);
findMatchedIndex(evalsConverged.head(nconv), evalsRm, selectInd);
//Rcpp::Rcout << evalsRm << "\n\n";
//Rcpp::Rcout << evalsConverged << "\n\n";
truenconv = selectInd.size();
if(truenconv < 1)
{
::Rf_warning("no converged eigenvalues found");
return returnResult(R_NilValue, R_NilValue, wrap(0L),
wrap(niter));
}
// Shrink Qm and Rm to the dimension given by the largest value
// in selectInd. Since selectInd is strictly increasing,
// we can just use its last value.
int lastInd = selectInd[selectInd.size() - 1];
Qm.conservativeResize(Eigen::NoChange, lastInd + 1);
Rm.conservativeResize(lastInd + 1, lastInd + 1);
// Eigen decomposition of Rm
EigenSolver<MatrixXd> es(Rm);
evalsRm = es.eigenvalues();
MatrixXcd evecsA = Vm * (Qm * es.eigenvectors());
// Order and select eigenvalues/eigenvectors
for(int i = 0; i < truenconv; i++)
{
// Since selectInd[i] >= i for all i, it is safe to
// overwrite the elements and columns.
evalsRm[i] = evalsRm[selectInd[i]];
}
if(evalsRm.size() > truenconv)
evalsRm.conservativeResize(truenconv);
transformEigenvalues(evalsRm);
// Now (evalsRm, selectInd) gives the pair of (value, location)
sortDescPair(evalsRm, selectInd);
if(truenconv > nev)
{
truenconv = nev;
evalsRm.conservativeResize(truenconv);
}
MatrixXcd evecsConverged(n, truenconv);
for(int i = 0; i < truenconv; i++)
{
evecsConverged.col(i) = evecsA.col(selectInd[i]);
}
if(truenconv < nev)
::Rf_warning("only %d eigenvalues converged, less than k", truenconv);
return returnResult(returnRealIfPossible(evalsRm),
returnRealIfPossible(evecsConverged),
wrap(truenconv),
wrap(niter));
}
bool Ellipsoid::overlapsWith(Ellipsoid ellipsoid, bool &ellipsoidMatrixDecompositionIsSuccessful)
{
// Construct translation matrix
MatrixXd T1 = MatrixXd::Identity(Ndimensions+1,Ndimensions+1);
MatrixXd T2 = MatrixXd::Identity(Ndimensions+1,Ndimensions+1);
T1.bottomLeftCorner(1,Ndimensions) = (-1.0) * centerCoordinates.transpose();
T2.bottomLeftCorner(1,Ndimensions) = (-1.0) * ellipsoid.getCenterCoordinates().transpose();
// Construct ellipsoid matrix in homogeneous coordinates
MatrixXd A = MatrixXd::Zero(Ndimensions+1,Ndimensions+1);
MatrixXd B = A;
A(Ndimensions,Ndimensions) = -1;
B(Ndimensions,Ndimensions) = -1;
A.topLeftCorner(Ndimensions,Ndimensions) = covarianceMatrix.matrix().inverse();
B.topLeftCorner(Ndimensions,Ndimensions) = ellipsoid.getCovarianceMatrix().matrix().inverse();
MatrixXd AT = T1*A*T1.transpose(); // Translating to ellipsoid center
MatrixXd BT = T2*B*T2.transpose(); // Translating to ellipsoid center
// Compute Hyper Quadric Matrix generating from the two ellipsoids
// and derive its eigenvalues decomposition
MatrixXd C = AT.inverse() * BT;
MatrixXcd CC(Ndimensions+1,Ndimensions+1);
CC.imag() = MatrixXd::Zero(Ndimensions+1,Ndimensions+1);
CC.real() = C;
ComplexEigenSolver<MatrixXcd> eigenSolver(CC);
// If eigenvalue decomposition fails, set control flag to false
// to stop the nested sampling and print the results
if (eigenSolver.info() != Success)
{
ellipsoidMatrixDecompositionIsSuccessful = false;
}
MatrixXcd E = eigenSolver.eigenvalues();
MatrixXcd V = eigenSolver.eigenvectors();
bool ellipsoidsDoOverlap = false; // Assume no overlap in the beginning
double pointA; // Point laying in this ellipsoid
double pointB; // Point laying in the other ellipsoid
// Loop over all eigenvectors
for (int i = 0; i < Ndimensions+1; i++)
{
// Skip inadmissible eigenvectors
if (V(Ndimensions,i).real() == 0)
{
continue;
}
else if (E(i).imag() != 0)
{
V.col(i) = V.col(i).array() * (V.conjugate())(Ndimensions,i); // Multiply eigenvector by complex conjugate of last element
V.col(i) = V.col(i).array() / V(Ndimensions,i).real(); // Normalize eigenvector to last component value
pointA = V.col(i).transpose().real() * AT * V.col(i).real(); // Evaluate point from this ellipsoid
pointB = V.col(i).transpose().real() * BT * V.col(i).real(); // Evaluate point from the other ellipsoid
// Accept only if point belongs to both ellipsoids
if ((pointA <= 0) && (pointB <= 0))
{
ellipsoidsDoOverlap = true; // Exit if ellipsoidsDoOverlap is found
break;
}
}
}
return ellipsoidsDoOverlap;
}
/*!
\brief Partial pivoted LU to construct low-rank.
*/
void partial_Piv_LU(const int start_Row, const int start_Col, const int n_Rows, const int n_Cols, const double tolerance, int& computed_Rank, MatrixXcd& U, MatrixXcd& V) {
/********************************/
/* PURPOSE OF EXISTENCE */
/********************************/
/*!
Obtains the low-rank decomposition of the matrix to a desired tolerance using the partial pivoting LU algorithm, i.e., given a sub-matrix 'A' and tolerance 'epsilon', computes matrices 'U' and 'V' such that ||A-UV||_F < epsilon. The norm is Frobenius norm.
*/
/************************/
/* INPUTS */
/************************/
/// start_Row - Starting row of the sub-matrix.
/// start_Col - Starting column of the sub-matrix.
/// n_Rows - Number of rows of the sub-matrix.
/// n_Cols - Number of columns of the sub-matrix.
/// tolerance - Tolerance of low-rank approximation.
/************************/
/* OUTPUTS */
/************************/
/// computed_Rank - Rank obtained for the given tolerance.
/// U - Matrix forming the column basis.
/// V - Matrix forming the row basis.
/// If the matrix is small enough, do not do anything
int tolerable_Rank = 5;
if (n_Cols <= tolerable_Rank){
kernel->get_Matrix(start_Row, start_Col, n_Rows, n_Cols, U);
V = MatrixXcd::Identity(n_Cols, n_Cols);
computed_Rank = n_Cols;
return;
}
else if (n_Rows <= tolerable_Rank){
U = MatrixXcd::Identity(n_Rows, n_Rows);
kernel->get_Matrix(start_Row, start_Col, n_Rows, n_Cols, V);
computed_Rank = n_Rows;
return;
}
vector<int> rowIndex; /// This stores the row indices, which have already been used.
vector<int> colIndex; /// This stores the column indices, which have already been used.
vector<VectorXcd> u; /// Stores the column basis.
vector<VectorXcd> v; /// Stores the row basis.
srand (time(NULL));
complex<double> max, unused_max, Gamma;
/* INITIALIZATION */
/// Initialize the matrix norm and the the first row index
double matrix_Norm = 0;
rowIndex.push_back(0);
int pivot;
computed_Rank = 0;
VectorXcd a, row, col;
double row_Squared_Norm, row_Norm, col_Squared_Norm, col_Norm;
/// Repeat till the desired tolerance is obtained
do {
/// Generation of the row
kernel->get_Matrix_Row(start_Col, n_Cols, start_Row+rowIndex.back(), a);
/// Row of the residuum and the pivot column
row = a;
for (int l=0; l<computed_Rank; ++l) {
row = row-u[l](rowIndex.back())*v[l];
}
pivot = kernel->max_Abs_Vector(row, colIndex, max);
int max_tries = 100;
int count = 0;
int count1 = 0;
/// This randomization is needed if in the middle of the algorithm the row happens to be exactly the linear combination of the previous rows.
while (abs(max)<tolerance && count < max_tries) {
int new_rowIndex;
rowIndex.pop_back();
do {
new_rowIndex = rand()%n_Rows;
++count1;
} while (find(rowIndex.begin(),rowIndex.end(),new_rowIndex)!=rowIndex.end() && count1 < max_tries);
count1 = 0;
rowIndex.push_back(new_rowIndex);
/// Generation of the row
kernel->get_Matrix_Row(start_Col, n_Cols, start_Row+rowIndex.back(), a);
/// Row of the residuum and the pivot column
row = a;
for (int l=0; l<computed_Rank; ++l) {
row = row-u[l](rowIndex.back())*v[l];
}
pivot = kernel->max_Abs_Vector(row, colIndex, max);
++count;
}
if (count == max_tries) break;
count = 0;
colIndex.push_back(pivot);
/// Normalizing constant
Gamma = 1.0/(max);
/// Generation of the column
kernel->get_Matrix_Col(start_Row, n_Rows, start_Col+colIndex.back(), a);
/// Column of the residuum and the pivot row
col = a;
for (int l=0; l<computed_Rank; ++l) {
col = col-v[l](colIndex.back())*u[l];
}
pivot = kernel->max_Abs_Vector(col, rowIndex, unused_max);
/// This randomization is needed if in the middle of the algorithm the columns happens to be exactly the linear combination of the previous columns.
while (abs(max)<tolerance && count < max_tries) {
colIndex.pop_back();
int new_colIndex;
do {
new_colIndex = rand()%n_Cols;
} while (find(colIndex.begin(),colIndex.end(),new_colIndex)!=colIndex.end() && count1 < max_tries);
count1 = 0;
colIndex.push_back(new_colIndex);
/// Generation of the column
kernel->get_Matrix_Col(start_Row, n_Rows, start_Col+colIndex.back(), a);
/// Column of the residuum and the pivot row
col = a;
for (int l=0; l<computed_Rank; ++l) {
col = col-u[l](colIndex.back())*v[l];
}
pivot = kernel->max_Abs_Vector(col, rowIndex, unused_max);
++count;
}
if (count == max_tries) break;
count = 0;
rowIndex.push_back(pivot);
/// New vectors
u.push_back(Gamma*col);
v.push_back(row);
/// New approximation of matrix norm
row_Squared_Norm = row.squaredNorm();
row_Norm = sqrt(row_Squared_Norm);
col_Squared_Norm = col.squaredNorm();
col_Norm = sqrt(col_Squared_Norm);
matrix_Norm = matrix_Norm + abs(Gamma*Gamma*row_Squared_Norm*col_Squared_Norm);
for (int j=0; j<computed_Rank; ++j) {
matrix_Norm = matrix_Norm + 2.0*abs(u[j].dot(u.back()))*abs(v[j].dot(v.back()));
}
++computed_Rank;
} while (row_Norm*col_Norm > abs(max)*tolerance*matrix_Norm && computed_Rank <= fmin(n_Rows, n_Cols));
/// If the computed_Rank is close to full-rank then return the trivial full-rank decomposition
if (computed_Rank>=fmin(n_Rows, n_Cols)) {
if (n_Rows < n_Cols) {
U = MatrixXcd::Identity(n_Rows,n_Rows);
kernel->get_Matrix(start_Row, start_Col, n_Rows, n_Cols, V);
computed_Rank = n_Rows;
return;
}
else {
kernel->get_Matrix(start_Row, start_Col, n_Rows, n_Cols, U);
V = MatrixXcd::Identity(n_Cols,n_Cols);
computed_Rank = n_Cols;
return;
}
}
U = MatrixXcd(n_Rows,computed_Rank);
V = MatrixXcd(computed_Rank,n_Cols);
for (int j=0; j<computed_Rank; ++j) {
U.col(j) = u[j];
V.row(j) = v[j];
}
};
int main() {
// The eigen approach
ArrayXd n = ArrayXd::LinSpaced(N+1,0,N);
double multiplier = M_PI/N;
Array<double, 1, N+1> nT = n.transpose();
ArrayXd x = - cos(multiplier*n);
ArrayXd xsub = x.middleRows(1, N-1);
ArrayXd ysub = (x1-x0)/2*xsub + (x1+x0)/2;
ArrayXXd T = cos((acos(x).matrix()*nT.matrix()).array());
ArrayXXd Tsub = cos((acos(xsub).matrix()*nT.matrix()).array());
ArrayXd sqinx = 1/sqrt(1-xsub*xsub);
MatrixXd inv1x2 = (sqinx).matrix().asDiagonal();
// Can't use the following to test elements of inv1x2
// std::cout << inv1x2(0,0) << "\n";
MatrixXd Usub = inv1x2 * sin(((acos(xsub).matrix())*nT.matrix()).array()).matrix();
MatrixXd dTsub = Usub*(n.matrix().asDiagonal());
MatrixXd d2Tsub = ((sqinx*sqinx).matrix().asDiagonal())*((xsub.matrix().asDiagonal()) * (dTsub.matrix()) - (Tsub.matrix()) * ((n*n).matrix().asDiagonal()));
MatrixXd d2T(N+1, N+1);
RowVectorXd a = (pow((-1),nT))*(nT*nT+1)*(nT*nT)/3;
RowVectorXd b = (nT*nT+1)*(nT*nT)/3;
d2T.middleRows(1,N-1) = d2Tsub;
d2T.row(0) = a;
d2T.row(N) = b;
MatrixXd D2 = d2T.matrix() * ((T.matrix()).inverse());
MatrixXd E2 = D2.middleRows(1,N-1).middleCols(1,N-1);
MatrixXd Y = ysub.matrix().asDiagonal();
MatrixXd H = - (4 / ((x1-x0)*(x1-x0))) * E2 + k*Y;
Eigen::EigenSolver<Eigen::MatrixXd> HE(H);
VectorXcd D = HE.eigenvalues();
MatrixXcd V = HE.eigenvectors();
std::cout << HE.info() << std::endl;
// Open ofstream
ofstream Dfile;
Dfile.open("D-output.txt");
ofstream Vfile;
Vfile.open("V-output.txt");
ofstream V544file;
V544file.open("V544-output.txt");
Dfile.precision(15);
Dfile << D.real() << "\n";
Vfile.precision(15);
Vfile << V.real() << "\n";
V544file.precision(15);
for(int i = 1; i<N-1; i++)
{
V544file << ysub[i-1];
V544file << " " << V.col(544).row(i-1).real() << "\n";
}
Dfile.close();
Vfile.close();
V544file.close();
system("gnuplot -p plot.gp");
system("rsvg-convert -w 2000 -o V544-plot.png V544-plot.svg");
}
