int main(int argc, char **argv)
{
print_version(argv[0]);
if(argc==1)
{
cerr << "Not enough arguments \nPlease try \"" << argv[0] << " -h\" or \"" << argv[0] << " --help \" \n" << endl;
return 0;
}
if ((!strcmp(argv[1],"-h")) | (!strcmp(argv[1],"--help"))) getHelp(argv);
disp_argv(argc,argv);
// Start Chrono
cpuChrono C;
C.start();
SymMatrix HeadMat;
HeadMat.load(argv[1]);
HeadMat.invert(); // invert inplace
HeadMat.save(argv[2]);
// Stop Chrono
C.stop();
C.dispEllapsed();
return 0;
}
int main(int argc, char **argv)
{
print_version(argv[0]);
if(argc==1)
{
cerr << "Not enough arguments \nPlease try \"" << argv[0] << " -h\" or \"" << argv[0] << " --help \" \n" << endl;
return 0;
}
if ((!strcmp(argv[1],"-h")) | (!strcmp(argv[1],"--help"))) getHelp(argv);
disp_argv(argc,argv);
// Start Chrono
auto start_time = std::chrono::system_clock::now();
SymMatrix HeadMat;
HeadMat.load(argv[1]);
HeadMat.invert(); // invert inplace
HeadMat.save(argv[2]);
// Stop Chrono
auto end_time = std::chrono::system_clock::now();
dispEllapsed(end_time-start_time);
return 0;
}
typename make_triangular_matrix<SymMatrix, boost::numeric::ublas::lower>::type
cholesky_decomposition(SymMatrix const & A)
{
assert(A.size1() == A.size2());
auto L = matrix_lower_trianle(A);
for(size_t i = 0; i != A.size1(); ++ i)
{
for(size_t j = 0; j != i; ++ j)
{
for(size_t k = 0; k != j; ++ k)
{
L(i, j) -= L(i, k) * L(j, k);
}
L(i, j) /= L(j, j);
using ural::square;
L(i, i) -= square(L(i, j));
}
assert(L(i, i) >= 0);
using std::sqrt;
L(i, i) = sqrt(L(i, i));
}
return L;
}
bool isPSD (const SymMatrix &M)
/* Check a Matrix is both Symetric and Positive Semi Definite
* Creates a temporary copy
*
* Numerics of Algorithm:
* Use UdUfactor and checks reciprocal condition number >=0
* Algorithms using UdUfactor will will therefore succeed if isPSD(M) is true
* Do NOT assume because isPSD is true that M will appear to be PSD to other numerical algorithms
* Return:
* true iff M isSymetric and is PSD by the above algorithm
*/
{
RowMatrix UD(M.size1(),M.size1());
RowMatrix::value_type rcond = UdUfactor(UD, M);
return rcond >= 0.;
}
void assemble_HM(const Geometry& geo, SymMatrix& mat, const unsigned gauss_order)
{
mat = SymMatrix((geo.size()-geo.outermost_interface().nb_triangles()));
mat.set(0.0);
double K = 1.0 / (4.0 * M_PI);
// We iterate over the meshes (or pair of domains) to fill the lower half of the HeadMat (since its symmetry)
for ( Geometry::const_iterator mit1 = geo.begin(); mit1 != geo.end(); ++mit1) {
for ( Geometry::const_iterator mit2 = geo.begin(); (mit2 != (mit1+1)); ++mit2) {
// if mit1 and mit2 communicate, i.e they are used for the definition of a common domain
const int orientation = geo.oriented(*mit1, *mit2); // equals 0, if they don't have any domains in common
// equals 1, if they are both oriented toward the same domain
// equals -1, if they are not
if ( orientation != 0 ) {
double Scoeff = orientation * geo.sigma_inv(*mit1, *mit2) * K;
double Dcoeff = - orientation * geo.indicator(*mit1, *mit2) * K;
double Ncoeff;
if ( !(mit1->outermost() || mit2->outermost()) ) {
// Computing S block first because it's needed for the corresponding N block
operatorS(*mit1, *mit2, mat, Scoeff, gauss_order);
Ncoeff = geo.sigma(*mit1, *mit2)/geo.sigma_inv(*mit1, *mit2);
} else {
Ncoeff = orientation * geo.sigma(*mit1, *mit2) * K;
}
if ( !mit1->outermost() ) {
// Computing D block
operatorD(*mit1, *mit2, mat, Dcoeff, gauss_order);
}
if ( ( *mit1 != *mit2 ) && ( !mit2->outermost() ) ) {
// Computing D* block
operatorD(*mit1, *mit2, mat, Dcoeff, gauss_order, true);
}
// Computing N block
operatorN(*mit1, *mit2, mat, Ncoeff, gauss_order);
}
}
}
// Deflate the diagonal block (N33) of 'mat' : (in order to have a zero-mean potential for the outermost interface)
const Interface i = geo.outermost_interface();
unsigned i_first = (*i.begin()->mesh().vertex_begin())->index();
deflat(mat, i, mat(i_first, i_first) / (geo.outermost_interface().nb_vertices()));
}
Vector<T> CholeskyDecomp<T>::operator()(const SymMatrix<T>& m,
const Vector<T>& sol) const
{
ulong rows = m.numRows();
Vector<T> retval(rows);
T ki;
LowerTriangular<T> lower(rows);
for(unsigned long k = 0; k < rows; k++)
{
for(unsigned long i = 0; i <= k; i++)
{
ki = m(k,i);
for(unsigned long j = 0; j < i; j++)
ki -= lower(i, j) * lower(k, j);
if(i == k)
ki = sqrt(ki);
else
ki /= lower(i,i);
lower.at(k,i) = ki;
}
}
Matrix<T> upper(lower);
Vector<T> temp(rows);
upper.transpose();
for(unsigned long i = 0; i < rows; i++)
{
ki = sol[i];
for(unsigned long j = 0; j < i; j++)
{
ki -= lower(i,j) * temp[j];
}
temp.at(i) = ki/lower(i,i);
}
for(unsigned long i = rows; i > 0; i--)
{
ki = temp[i-1];
for(unsigned long j = rows; j > i; j--)
{
ki -= upper(i-1, j-1) * retval[j-1];
}
retval.at(i-1) = ki/upper(i-1, i-1);
}
return retval;
}
void PDFullSpaceSolver::ComputeResiduals(
const SymMatrix& W,
const Matrix& J_c,
const Matrix& J_d,
const Matrix& Px_L,
const Matrix& Px_U,
const Matrix& Pd_L,
const Matrix& Pd_U,
const Vector& z_L,
const Vector& z_U,
const Vector& v_L,
const Vector& v_U,
const Vector& slack_x_L,
const Vector& slack_x_U,
const Vector& slack_s_L,
const Vector& slack_s_U,
const Vector& sigma_x,
const Vector& sigma_s,
Number alpha,
Number beta,
const IteratesVector& rhs,
const IteratesVector& res,
IteratesVector& resid)
{
DBG_START_METH("PDFullSpaceSolver::ComputeResiduals", dbg_verbosity);
DBG_PRINT_VECTOR(2, "res", res);
IpData().TimingStats().ComputeResiduals().Start();
// Get the current sizes of the perturbation factors
Number delta_x;
Number delta_s;
Number delta_c;
Number delta_d;
perturbHandler_->CurrentPerturbation(delta_x, delta_s, delta_c, delta_d);
SmartPtr<Vector> tmp;
// x
W.MultVector(1., *res.x(), 0., *resid.x_NonConst());
J_c.TransMultVector(1., *res.y_c(), 1., *resid.x_NonConst());
J_d.TransMultVector(1., *res.y_d(), 1., *resid.x_NonConst());
Px_L.MultVector(-1., *res.z_L(), 1., *resid.x_NonConst());
Px_U.MultVector(1., *res.z_U(), 1., *resid.x_NonConst());
resid.x_NonConst()->AddTwoVectors(delta_x, *res.x(), -1., *rhs.x(), 1.);
// s
Pd_U.MultVector(1., *res.v_U(), 0., *resid.s_NonConst());
Pd_L.MultVector(-1., *res.v_L(), 1., *resid.s_NonConst());
resid.s_NonConst()->AddTwoVectors(-1., *res.y_d(), -1., *rhs.s(), 1.);
if (delta_s!=0.) {
resid.s_NonConst()->Axpy(delta_s, *res.s());
}
// c
J_c.MultVector(1., *res.x(), 0., *resid.y_c_NonConst());
resid.y_c_NonConst()->AddTwoVectors(-delta_c, *res.y_c(), -1., *rhs.y_c(), 1.);
// d
J_d.MultVector(1., *res.x(), 0., *resid.y_d_NonConst());
resid.y_d_NonConst()->AddTwoVectors(-1., *res.s(), -1., *rhs.y_d(), 1.);
if (delta_d!=0.) {
resid.y_d_NonConst()->Axpy(-delta_d, *res.y_d());
}
// zL
resid.z_L_NonConst()->Copy(*res.z_L());
resid.z_L_NonConst()->ElementWiseMultiply(slack_x_L);
tmp = z_L.MakeNew();
Px_L.TransMultVector(1., *res.x(), 0., *tmp);
tmp->ElementWiseMultiply(z_L);
resid.z_L_NonConst()->AddTwoVectors(1., *tmp, -1., *rhs.z_L(), 1.);
// zU
resid.z_U_NonConst()->Copy(*res.z_U());
resid.z_U_NonConst()->ElementWiseMultiply(slack_x_U);
tmp = z_U.MakeNew();
Px_U.TransMultVector(1., *res.x(), 0., *tmp);
tmp->ElementWiseMultiply(z_U);
resid.z_U_NonConst()->AddTwoVectors(-1., *tmp, -1., *rhs.z_U(), 1.);
// vL
resid.v_L_NonConst()->Copy(*res.v_L());
resid.v_L_NonConst()->ElementWiseMultiply(slack_s_L);
tmp = v_L.MakeNew();
Pd_L.TransMultVector(1., *res.s(), 0., *tmp);
tmp->ElementWiseMultiply(v_L);
resid.v_L_NonConst()->AddTwoVectors(1., *tmp, -1., *rhs.v_L(), 1.);
// vU
resid.v_U_NonConst()->Copy(*res.v_U());
resid.v_U_NonConst()->ElementWiseMultiply(slack_s_U);
tmp = v_U.MakeNew();
Pd_U.TransMultVector(1., *res.s(), 0., *tmp);
tmp->ElementWiseMultiply(v_U);
resid.v_U_NonConst()->AddTwoVectors(-1., *tmp, -1., *rhs.v_U(), 1.);
DBG_PRINT_VECTOR(2, "resid", resid);
if (Jnlst().ProduceOutput(J_MOREVECTOR, J_LINEAR_ALGEBRA)) {
resid.Print(Jnlst(), J_MOREVECTOR, J_LINEAR_ALGEBRA, "resid");
}
if (Jnlst().ProduceOutput(J_MOREDETAILED, J_LINEAR_ALGEBRA)) {
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_x %e\n", resid.x()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_s %e\n", resid.s()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_c %e\n", resid.y_c()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_d %e\n", resid.y_d()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_zL %e\n", resid.z_L()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_zU %e\n", resid.z_U()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_vL %e\n", resid.v_L()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"max-norm resid_vU %e\n", resid.v_U()->Amax());
}
IpData().TimingStats().ComputeResiduals().End();
}
bool PDFullSpaceSolver::SolveOnce(bool resolve_with_better_quality,
bool pretend_singular,
const SymMatrix& W,
const Matrix& J_c,
const Matrix& J_d,
const Matrix& Px_L,
const Matrix& Px_U,
const Matrix& Pd_L,
const Matrix& Pd_U,
const Vector& z_L,
const Vector& z_U,
const Vector& v_L,
const Vector& v_U,
const Vector& slack_x_L,
const Vector& slack_x_U,
const Vector& slack_s_L,
const Vector& slack_s_U,
const Vector& sigma_x,
const Vector& sigma_s,
Number alpha,
Number beta,
const IteratesVector& rhs,
IteratesVector& res)
{
// TO DO LIST:
//
// 1. decide for reasonable return codes (e.g. fatal error, too
// ill-conditioned...)
// 2. Make constants parameters that can be set from the outside
// 3. Get Information out of Ipopt structures
// 4. add heuristic for structurally singular problems
// 5. see if it makes sense to distinguish delta_x and delta_s,
// or delta_c and delta_d
// 6. increase pivot tolerance if number of get evals so too small
DBG_START_METH("PDFullSpaceSolver::SolveOnce",dbg_verbosity);
IpData().TimingStats().PDSystemSolverSolveOnce().Start();
// Compute the right hand side for the augmented system formulation
SmartPtr<Vector> augRhs_x = rhs.x()->MakeNewCopy();
Px_L.AddMSinvZ(1.0, slack_x_L, *rhs.z_L(), *augRhs_x);
Px_U.AddMSinvZ(-1.0, slack_x_U, *rhs.z_U(), *augRhs_x);
SmartPtr<Vector> augRhs_s = rhs.s()->MakeNewCopy();
Pd_L.AddMSinvZ(1.0, slack_s_L, *rhs.v_L(), *augRhs_s);
Pd_U.AddMSinvZ(-1.0, slack_s_U, *rhs.v_U(), *augRhs_s);
// Get space into which we can put the solution of the augmented system
SmartPtr<IteratesVector> sol = res.MakeNewIteratesVector(true);
// Now check whether any data has changed
std::vector<const TaggedObject*> deps(13);
deps[0] = &W;
deps[1] = &J_c;
deps[2] = &J_d;
deps[3] = &z_L;
deps[4] = &z_U;
deps[5] = &v_L;
deps[6] = &v_U;
deps[7] = &slack_x_L;
deps[8] = &slack_x_U;
deps[9] = &slack_s_L;
deps[10] = &slack_s_U;
deps[11] = &sigma_x;
deps[12] = &sigma_s;
void* dummy;
bool uptodate = dummy_cache_.GetCachedResult(dummy, deps);
if (!uptodate) {
dummy_cache_.AddCachedResult(dummy, deps);
augsys_improved_ = false;
}
// improve_current_solution can only be true, if that system has
// been solved before
DBG_ASSERT((!resolve_with_better_quality && !pretend_singular) || uptodate);
ESymSolverStatus retval;
if (uptodate && !pretend_singular) {
// Get the perturbation values
Number delta_x;
Number delta_s;
Number delta_c;
Number delta_d;
perturbHandler_->CurrentPerturbation(delta_x, delta_s, delta_c, delta_d);
// No need to go through the pain of finding the appropriate
// values for the deltas, because the matrix hasn't changed since
// the last call. So, just call the Solve Method
//
// Note: resolve_with_better_quality is true, then the Solve
// method has already asked the augSysSolver to increase the
// quality at the end solve, and we are now getting the solution
// with that better quality
retval = augSysSolver_->Solve(&W, 1.0, &sigma_x, delta_x,
&sigma_s, delta_s, &J_c, NULL,
delta_c, &J_d, NULL, delta_d,
*augRhs_x, *augRhs_s, *rhs.y_c(), *rhs.y_d(),
*sol->x_NonConst(), *sol->s_NonConst(),
*sol->y_c_NonConst(), *sol->y_d_NonConst(),
false, 0);
if (retval!=SYMSOLVER_SUCCESS) {
IpData().TimingStats().PDSystemSolverSolveOnce().End();
return false;
}
}
else {
const Index numberOfEVals=rhs.y_c()->Dim()+rhs.y_d()->Dim();
// counter for the number of trial evaluations
// (ToDo is not at the correct place)
Index count = 0;
// Get the very first perturbation values from the perturbation
// Handler
Number delta_x;
Number delta_s;
Number delta_c;
Number delta_d;
perturbHandler_->ConsiderNewSystem(delta_x, delta_s, delta_c, delta_d);
retval = SYMSOLVER_SINGULAR;
bool fail = false;
while (retval!= SYMSOLVER_SUCCESS && !fail) {
if (pretend_singular) {
retval = SYMSOLVER_SINGULAR;
pretend_singular = false;
}
else {
count++;
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Solving system with delta_x=%e delta_s=%e\n delta_c=%e delta_d=%e\n",
delta_x, delta_s, delta_c, delta_d);
bool check_inertia = true;
if (neg_curv_test_tol_ > 0.) {
check_inertia = false;
}
retval = augSysSolver_->Solve(&W, 1.0, &sigma_x, delta_x,
&sigma_s, delta_s, &J_c, NULL,
delta_c, &J_d, NULL, delta_d,
*augRhs_x, *augRhs_s, *rhs.y_c(), *rhs.y_d(),
*sol->x_NonConst(), *sol->s_NonConst(),
*sol->y_c_NonConst(), *sol->y_d_NonConst(), check_inertia, numberOfEVals);
}
if (retval==SYMSOLVER_FATAL_ERROR) return false;
if (retval==SYMSOLVER_SINGULAR &&
(rhs.y_c()->Dim()+rhs.y_d()->Dim() > 0) ) {
// Get new perturbation factors from the perturbation
// handlers for the singular case
bool pert_return = perturbHandler_->PerturbForSingularity(delta_x, delta_s,
delta_c, delta_d);
if (!pert_return) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"PerturbForSingularity can't be done\n");
IpData().TimingStats().PDSystemSolverSolveOnce().End();
return false;
}
}
else if (retval==SYMSOLVER_WRONG_INERTIA &&
augSysSolver_->NumberOfNegEVals() < numberOfEVals) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of negative eigenvalues too small!\n");
// If the number of negative eigenvalues is too small, then
// we first try to remedy this by asking for better quality
// solution (e.g. increasing pivot tolerance), and if that
// doesn't help, we assume that the system is singular
bool assume_singular = true;
if (!augsys_improved_) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Asking augmented system solver to improve quality of its solutions.\n");
augsys_improved_ = augSysSolver_->IncreaseQuality();
if (augsys_improved_) {
IpData().Append_info_string("q");
assume_singular = false;
}
else {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Quality could not be improved\n");
}
}
if (assume_singular) {
bool pert_return =
perturbHandler_->PerturbForSingularity(delta_x, delta_s,
delta_c, delta_d);
if (!pert_return) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"PerturbForSingularity can't be done for assume singular.\n");
IpData().TimingStats().PDSystemSolverSolveOnce().End();
return false;
}
IpData().Append_info_string("a");
}
}
else if (retval==SYMSOLVER_WRONG_INERTIA ||
retval==SYMSOLVER_SINGULAR) {
// Get new perturbation factors from the perturbation
// handlers for the case of wrong inertia
bool pert_return = perturbHandler_->PerturbForWrongInertia(delta_x, delta_s,
delta_c, delta_d);
if (!pert_return) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"PerturbForWrongInertia can't be done for wrong interia or singular.\n");
IpData().TimingStats().PDSystemSolverSolveOnce().End();
return false;
}
}
else if (neg_curv_test_tol_ > 0.) {
DBG_ASSERT(augSysSolver_->ProvidesInertia());
// we now check if the inertia is possible wrong
Index neg_values = augSysSolver_->NumberOfNegEVals();
if (neg_values != numberOfEVals) {
// check if we have a direction of sufficient positive curvature
SmartPtr<Vector> x_tmp = sol->x()->MakeNew();
W.MultVector(1., *sol->x(), 0., *x_tmp);
Number xWx = x_tmp->Dot(*sol->x());
x_tmp->Copy(*sol->x());
x_tmp->ElementWiseMultiply(sigma_x);
xWx += x_tmp->Dot(*sol->x());
SmartPtr<Vector> s_tmp = sol->s()->MakeNewCopy();
s_tmp->ElementWiseMultiply(sigma_s);
xWx += s_tmp->Dot(*sol->s());
if (neg_curv_test_reg_) {
x_tmp->Copy(*sol->x());
x_tmp->Scal(delta_x);
xWx += x_tmp->Dot(*sol->x());
s_tmp->Copy(*sol->s());
s_tmp->Scal(delta_s);
xWx += s_tmp->Dot(*sol->s());
}
Number xs_nrmsq = pow(sol->x()->Nrm2(),2) + pow(sol->s()->Nrm2(),2);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"In inertia heuristic: xWx = %e xx = %e\n",
xWx, xs_nrmsq);
if (xWx < neg_curv_test_tol_*xs_nrmsq) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
" -> Redo with modified matrix.\n");
bool pert_return = perturbHandler_->PerturbForWrongInertia(delta_x, delta_s,
delta_c, delta_d);
if (!pert_return) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"PerturbForWrongInertia can't be done for inertia heuristic.\n");
IpData().TimingStats().PDSystemSolverSolveOnce().End();
return false;
}
retval = SYMSOLVER_WRONG_INERTIA;
}
}
}
} // while (retval!=SYMSOLVER_SUCCESS && !fail) {
// Some output
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of trial factorizations performed: %d\n",
count);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Perturbation parameters: delta_x=%e delta_s=%e\n delta_c=%e delta_d=%e\n",
delta_x, delta_s, delta_c, delta_d);
// Set the perturbation values in the Data object
IpData().setPDPert(delta_x, delta_s, delta_c, delta_d);
}
// Compute the remaining sol Vectors
Px_L.SinvBlrmZMTdBr(-1., slack_x_L, *rhs.z_L(), z_L, *sol->x(), *sol->z_L_NonConst());
Px_U.SinvBlrmZMTdBr(1., slack_x_U, *rhs.z_U(), z_U, *sol->x(), *sol->z_U_NonConst());
Pd_L.SinvBlrmZMTdBr(-1., slack_s_L, *rhs.v_L(), v_L, *sol->s(), *sol->v_L_NonConst());
Pd_U.SinvBlrmZMTdBr(1., slack_s_U, *rhs.v_U(), v_U, *sol->s(), *sol->v_U_NonConst());
// Finally let's assemble the res result vectors
res.AddOneVector(alpha, *sol, beta);
IpData().TimingStats().PDSystemSolverSolveOnce().End();
return true;
}
int main( int argc, char **argv)
{
print_version(argv[0]);
//TODO doesn't say txt, if you don't specify it
command_usage("Provides informations on a Matrix generated with OpenMEEG");
const char *filename = command_option("-i",(const char *) NULL,"Matrix file");
const char *txt = command_option("-txt",(const char *) 0,"Force reading data stored in ascii format");
const char *sym = command_option("-sym",(const char *) 0,"Data are symmetric matrices");
const char *sparse = command_option("-sparse",(const char *) 0,"Data are sparse matrices");
const char *mat = command_option("-mat",(const char *) 0,"Data are matlab format");
const char *bin = command_option("-bin",(const char *) 0,"Data are binary format");
if (command_option("-h",(const char *)0,0)) return 0;
if (!filename) {
cerr << "Please set Matrix File" << endl;
exit(1);
}
cout << "Loading : " << filename << endl;
if (sym) {
if (txt) {
SymMatrix M;
M.load(filename);
cout << "Format : ASCII" << endl;
print_infos(M);
} else if (mat) {
cerr << "Unsupported Format : MAT for symmetric matrices" << endl;
exit(1);
} else if (bin) {
SymMatrix M;
M.load(filename);
cout << "Format : BINARY" << endl;
print_infos(M);
} else {
SymMatrix M;
M.load(filename);
print_infos(M);
}
} else if (sparse) {
if (txt) {
SparseMatrix M;
M.load(filename);
cout << "Format : ASCII" << endl;
print_infos(M);
} else if (mat) {
SparseMatrix M;
M.load(filename);
cout << "Format : MAT" << endl;
print_infos(M);
} else if (bin) {
SparseMatrix M;
M.load(filename);
cout << "Format : BINARY" << endl;
print_infos(M);
} else {
SparseMatrix M;
M.load(filename);
print_infos(M);
}
} else {
if (txt) {
Matrix M;
M.load(filename);
cout << "Format : ASCII" << endl;
print_infos(M);
} else if (mat) {
Matrix M;
M.load(filename);
cout << "Format : MAT" << endl;
print_infos(M);
} else if (bin) {
Matrix M;
M.load(filename);
cout << "Format : BINARY" << endl;
print_infos(M);
} else {
Matrix M;
M.load(filename);
print_infos(M);
}
}
return 0;
}
void QpGenData::putCIntoAt( SymMatrix& M, int row, int col )
{
M.symAtPutSubmatrix( row, col, *C, 0, 0, mz, nx );
}
void QpGenData::putQIntoAt( SymMatrix& M, int row, int col )
{
M.symAtPutSubmatrix( row, col, *Q, 0, 0, nx, nx );
}
Vector::Vector(SymMatrix& A) {
nlin()=A.nlin()*(A.nlin()+1)/2;
value = A.value;
}
// Initialize the local copy of the positions of the nonzero
// elements
ESymSolverStatus
TSymLinearSolver::InitializeStructure(const SymMatrix& sym_A)
{
DBG_START_METH("TSymLinearSolver::InitializeStructure",
dbg_verbosity);
DBG_ASSERT(!initialized_);
ESymSolverStatus retval;
// have_structure_ is already true if this is a warm start for a
// problem with identical structure
if (!have_structure_) {
dim_ = sym_A.Dim();
nonzeros_triplet_ = TripletHelper::GetNumberEntries(sym_A);
delete [] airn_;
delete [] ajcn_;
airn_ = new Index[nonzeros_triplet_];
ajcn_ = new Index[nonzeros_triplet_];
TripletHelper::FillRowCol(nonzeros_triplet_, sym_A, airn_, ajcn_);
// If the solver wants the compressed format, the converter has to
// be initialized
const Index *ia;
const Index *ja;
Index nonzeros;
if (matrix_format_ == SparseSymLinearSolverInterface::Triplet_Format) {
ia = airn_;
ja = ajcn_;
nonzeros = nonzeros_triplet_;
}
else {
if (HaveIpData()) {
IpData().TimingStats().LinearSystemStructureConverter().Start();
IpData().TimingStats().LinearSystemStructureConverterInit().Start();
}
nonzeros_compressed_ =
triplet_to_csr_converter_->InitializeConverter(dim_, nonzeros_triplet_,
airn_, ajcn_);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemStructureConverterInit().End();
}
ia = triplet_to_csr_converter_->IA();
ja = triplet_to_csr_converter_->JA();
if (HaveIpData()) {
IpData().TimingStats().LinearSystemStructureConverter().End();
}
nonzeros = nonzeros_compressed_;
}
retval = solver_interface_->InitializeStructure(dim_, nonzeros, ia, ja);
if (retval != SYMSOLVER_SUCCESS) {
return retval;
}
// Get space for the scaling factors
delete [] scaling_factors_;
if (IsValid(scaling_method_)) {
if (HaveIpData()) {
IpData().TimingStats().LinearSystemScaling().Start();
}
scaling_factors_ = new double[dim_];
if (HaveIpData()) {
IpData().TimingStats().LinearSystemScaling().End();
}
}
have_structure_ = true;
}
else {
ASSERT_EXCEPTION(dim_==sym_A.Dim(), INVALID_WARMSTART,
"TSymLinearSolver called with warm_start_same_structure, but the problem is solved for the first time.");
// This is a warm start for identical structure, so we don't need to
// recompute the nonzeros location arrays
const Index *ia;
const Index *ja;
Index nonzeros;
if (matrix_format_ == SparseSymLinearSolverInterface::Triplet_Format) {
ia = airn_;
ja = ajcn_;
nonzeros = nonzeros_triplet_;
}
else {
IpData().TimingStats().LinearSystemStructureConverter().Start();
ia = triplet_to_csr_converter_->IA();
ja = triplet_to_csr_converter_->JA();
IpData().TimingStats().LinearSystemStructureConverter().End();
nonzeros = nonzeros_compressed_;
}
retval = solver_interface_->InitializeStructure(dim_, nonzeros, ia, ja);
}
initialized_=true;
return retval;
}
ESymSolverStatus
TSymLinearSolver::MultiSolve(const SymMatrix& sym_A,
std::vector<SmartPtr<const Vector> >& rhsV,
std::vector<SmartPtr<Vector> >& solV,
bool check_NegEVals,
Index numberOfNegEVals)
{
DBG_START_METH("TSymLinearSolver::MultiSolve",dbg_verbosity);
DBG_ASSERT(!check_NegEVals || ProvidesInertia());
// Check if this object has ever seen a matrix If not,
// allocate memory of the matrix structure and copy the nonzeros
// structure (it is assumed that this will never change).
if (!initialized_) {
ESymSolverStatus retval = InitializeStructure(sym_A);
if (retval != SYMSOLVER_SUCCESS) {
return retval;
}
}
DBG_ASSERT(nonzeros_triplet_== TripletHelper::GetNumberEntries(sym_A));
// Check if the matrix has been changed
DBG_PRINT((1,"atag_=%d sym_A->GetTag()=%d\n",atag_,sym_A.GetTag()));
bool new_matrix = sym_A.HasChanged(atag_);
atag_ = sym_A.GetTag();
// If a new matrix is encountered, get the array for storing the
// entries from the linear solver interface, fill in the new
// values, compute the new scaling factors (if required), and
// scale the matrix
if (new_matrix || just_switched_on_scaling_) {
GiveMatrixToSolver(true, sym_A);
new_matrix = true;
}
// Retrieve the right hand sides and scale if required
Index nrhs = (Index)rhsV.size();
double* rhs_vals = new double[dim_*nrhs];
for (Index irhs=0; irhs<nrhs; irhs++) {
TripletHelper::FillValuesFromVector(dim_, *rhsV[irhs],
&rhs_vals[irhs*(dim_)]);
if (Jnlst().ProduceOutput(J_MOREMATRIX, J_LINEAR_ALGEBRA)) {
Jnlst().Printf(J_MOREMATRIX, J_LINEAR_ALGEBRA,
"Right hand side %d in TSymLinearSolver:\n", irhs);
for (Index i=0; i<dim_; i++) {
Jnlst().Printf(J_MOREMATRIX, J_LINEAR_ALGEBRA,
"Trhs[%5d,%5d] = %23.16e\n", irhs, i,
rhs_vals[irhs*(dim_)+i]);
}
}
if (use_scaling_) {
if (HaveIpData()) {
IpData().TimingStats().LinearSystemScaling().Start();
}
for (Index i=0; i<dim_; i++) {
rhs_vals[irhs*(dim_)+i] *= scaling_factors_[i];
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemScaling().End();
}
}
}
bool done = false;
// Call the linear solver through the interface to solve the
// system. This is repeated, if the return values is S_CALL_AGAIN
// after the values have been restored (this might be necessary
// for MA27 if the size of the work space arrays was not large
// enough).
ESymSolverStatus retval;
while (!done) {
const Index* ia;
const Index* ja;
if (matrix_format_==SparseSymLinearSolverInterface::Triplet_Format) {
ia = airn_;
ja = ajcn_;
}
else {
if (HaveIpData()) {
IpData().TimingStats().LinearSystemStructureConverter().Start();
}
ia = triplet_to_csr_converter_->IA();
ja = triplet_to_csr_converter_->JA();
if (HaveIpData()) {
IpData().TimingStats().LinearSystemStructureConverter().End();
}
}
retval = solver_interface_->MultiSolve(new_matrix, ia, ja,
nrhs, rhs_vals, check_NegEVals,
numberOfNegEVals);
if (retval==SYMSOLVER_CALL_AGAIN) {
DBG_PRINT((1, "Solver interface asks to be called again.\n"));
GiveMatrixToSolver(false, sym_A);
}
else {
done = true;
}
}
// If the solve was successful, unscale the solution (if required)
// and transfer the result into the Vectors
if (retval==SYMSOLVER_SUCCESS) {
for (Index irhs=0; irhs<nrhs; irhs++) {
if (use_scaling_) {
if (HaveIpData()) {
IpData().TimingStats().LinearSystemScaling().Start();
}
for (Index i=0; i<dim_; i++) {
rhs_vals[irhs*(dim_)+i] *= scaling_factors_[i];
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemScaling().End();
}
}
if (Jnlst().ProduceOutput(J_MOREMATRIX, J_LINEAR_ALGEBRA)) {
Jnlst().Printf(J_MOREMATRIX, J_LINEAR_ALGEBRA,
"Solution %d in TSymLinearSolver:\n", irhs);
for (Index i=0; i<dim_; i++) {
Jnlst().Printf(J_MOREMATRIX, J_LINEAR_ALGEBRA,
"Tsol[%5d,%5d] = %23.16e\n", irhs, i,
rhs_vals[irhs*(dim_)+i]);
}
}
TripletHelper::PutValuesInVector(dim_, &rhs_vals[irhs*(dim_)],
*solV[irhs]);
}
}
delete[] rhs_vals;
return retval;
}
