virtual void handleEvents( bool __is_finished, bool verbose )
{
if ( __iterations == 0 )
{
//SEvent startEv( IterationStarted, new IterationStarted<value_type>( __residual, __iterations) );
//SSubject::notifyObservers( &startEv );
if ( verbose )
std::cout << "iteration " << __iterations << " : " << residual() << "\n";
}
if ( __is_finished == true )
{
//SEvent finishEv( IterationFinished, new IterationFinished<value_type>( __residual, __iterations) );
//SSubject::notifyObservers( &finishEv );
if ( verbose )
std::cout << "iteration " << __iterations << " : " << residual() << "\n";
}
else
{
if ( verbose )
std::cout << "iteration " << __iterations << " : " << residual() << "\n";
//SEvent aEvent( IterationUpdated, new IterationUpdated<value_type>( __residual, __iterations) );
//SSubject::notifyObservers( &aEvent );
}
}
int main(int argv, char *argc[]) {
info I, J;
int N;
double *u;
if (argv > 1) {sscanf(argc[1],"%d", &N);}
else {N = 100;}
// if (scanf("%d", &N)) {} else {N = 40;}
iinit(&I, N);
u = advection(I.N, I.M);
compare(u, I.N);
residual(u, &I);
iprint(I);
free(u);
N *= 2;
iinit(&J, N);
u = advection(J.N, J.M);
compare(u, J.N);
residual(u, &J);
iprint(J);
free(u);
printf("Degree of convergence = %lg\n\n",
log(I.S/J.S)/log(I.h/J.h));
return 0;
}
int main(){
double a;//created variables
double b;
double c;
double x1;
double x2;
cout<<"please input values a b c to solve the function of the form ax^2+bx+c=0\na=";//prompt for user input of variables a b and c
cin>>a;
cout<<"b=";
cin>>b;
cout<<"c=";
cin>>c;
if (a==0&b==0&c==0){//if statement that outputs that every possible number is a root
cout<<"every real and complex number is a root\n";
keep_window_open();//stops the program
return 0;}
if (a==0&b==0&c!=0){//if statement that outputs the equation is inconsistent in other words it is not of the form ax^2+bx+c and has no roots
cout<<"the equation is inconsistent\n";
keep_window_open();//stops the program
return 0;}
if (discriminant(a,b,c)<0){//if statement that outputs the possible roots are complex
cout<<"the roots are complex\n";
keep_window_open();//stops the program
return 0;}
if (a==0&b!=0){//if statement that says there is only one root
cout<<"there is one root\n";
}
if(discriminant(a,b,c)==0){//if statement that outputs there are two of the same roots
cout<<"there is one double root\n";
}
x1=(-b+sqrt((b*b)-(4*a*c)))/(2*a);//calcution of the roots
x2=(-b-sqrt((b*b)-(4*a*c)))/(2*a);
double r1=residual(a,b,c,x1);//use of the function to calculate the residual of roots x1 and x2
double r2=residual(a,b,c,x2);
cout<<"root1:"<<x1<<"\nroot2:"<<x2<<"\nresidual1:"<<r1<<"\nresidual2:"<<r2<<"\n"; //output of the roots and residuals
keep_window_open();
return 0;
}
/**
* R is initialized to be Y. When a new entry is selected in our model,
* we have to compute the residual ONLY for the corresponding response
* of R. Since we refit, we only have to touch those responses that
* have a non-zero value, which can be done by traversing X. An entry
* in X has the form (source, target, value). Here, 'source' refers to
* the column of A that has been selected, 'target' to the column of
* Y that it affects. So, for each column, we can compute the residual
* update. This can be done in parallel (unlike refitting).
*
* THIS HAS NOT YET BEEN MPI PARALLELIZED.
*/
void residual () {
#if USE_PFUNC
task root_task;
attribute root_attribute (false /*nested*/, false /*grouped*/);
#endif
space_type sel_space (0, shadow_X.size());
Residual residual(&Y, &R, &l2_norm, &set_map, &snp_map, &materializer, M);
#if USE_PFUNC
ParallelResidual root_residual (sel_space, residual, *global_taskmgr);
pfunc::spawn (*global_taskmgr, root_task, root_attribute, root_residual);
pfunc::wait (*global_taskmgr, root_task);
#else
residual (sel_space);
#endif
}
//**********************************************************************
PHX_EVALUATE_FIELDS(NeumannResidual,workset)
{
residual.deep_copy(ScalarT(0.0));
for (std::size_t cell = 0; cell < workset.num_cells; ++cell) {
for (std::size_t ip = 0; ip < num_ip; ++ip) {
normal_dot_flux(cell,ip) = ScalarT(0.0);
for (std::size_t dim = 0; dim < num_dim; ++dim) {
normal_dot_flux(cell,ip) += normal(cell,ip,dim) * flux(cell,ip,dim);
}
}
}
// const Intrepid::FieldContainer<double> & weighted_basis = workset.bases[basis_index]->weighted_basis;
const Teuchos::RCP<const BasisValues2<double> > bv = workset.bases[basis_index];
for (std::size_t cell = 0; cell < workset.num_cells; ++cell) {
for (std::size_t basis = 0; basis < residual.dimension(1); ++basis) {
for (std::size_t qp = 0; qp < num_ip; ++qp) {
residual(cell,basis) += normal_dot_flux(cell,qp)*bv->weighted_basis_scalar(cell,basis,qp);
}
}
}
if(workset.num_cells>0)
Intrepid::FunctionSpaceTools::
integrate<ScalarT>(residual, normal_dot_flux,
(workset.bases[basis_index])->weighted_basis_scalar,
Intrepid::COMP_BLAS);
}
//**********************************************************************
PHX_EVALUATE_FIELDS(InterfaceResidual,workset)
{
residual.deep_copy(ScalarT(0.0));
for (std::size_t cell = 0; cell < workset.num_cells; ++cell) {
for (std::size_t ip = 0; ip < num_ip; ++ip) {
normal_dot_flux(cell,ip) = ScalarT(0.0);
for (std::size_t dim = 0; dim < num_dim; ++dim) {
normal_dot_flux(cell,ip) += normal(cell,ip,dim) * flux(cell,ip,dim);
}
}
}
// const Kokkos::DynRankView<double,PHX::Device> & weighted_basis = this->wda(workset).bases[basis_index]->weighted_basis;
const Teuchos::RCP<const BasisValues2<double> > bv = this->wda(workset).bases[basis_index];
for (std::size_t cell = 0; cell < workset.num_cells; ++cell) {
for (std::size_t basis = 0; basis < residual.dimension(1); ++basis) {
for (std::size_t qp = 0; qp < num_ip; ++qp) {
residual(cell,basis) += normal_dot_flux(cell,qp)*bv->weighted_basis_scalar(cell,basis,qp);
}
}
}
if(workset.num_cells>0)
Intrepid2::FunctionSpaceTools::
integrate<ScalarT>(residual, normal_dot_flux,
(this->wda(workset).bases[basis_index])->weighted_basis_scalar,
Intrepid2::COMP_CPP);
}
void EBPoissonOp::
levelJacobi(LevelData<EBCellFAB>& a_phi,
const LevelData<EBCellFAB>& a_rhs)
{
CH_TIME("EBPoissonOp::levelJacobi");
// Overhauled from previous in-place Gauss-Seidel-like method
// This implementation is very inefficient in terms of memory usage,
// and could be greatly improved. It has the advantage, though,
// of being very simple and easy to verify as correct.
EBCellFactory factory(m_eblg.getEBISL());
// Note: this is hardcoded to a single variable (component),
// like some other code in this class
LevelData<EBCellFAB> resid(m_eblg.getDBL(), 1, m_ghostCellsRHS, factory);
residual(resid, a_phi, a_rhs, true);
LevelData<EBCellFAB> relaxationCoeff(m_eblg.getDBL(), 1, m_ghostCellsRHS, factory);
getJacobiRelaxCoeff(relaxationCoeff);
EBLevelDataOps::scale(resid, relaxationCoeff);
EBLevelDataOps::incr(a_phi, resid, 1.0);
}
/*!
* \brief Solve.
*/
void JacobiSolver::iterate( const int max_iters, const double tolerance )
{
// Extract the linear problem.
Epetra_CrsMatrix *A =
dynamic_cast<Epetra_CrsMatrix*>( d_linear_problem->GetMatrix() );
Epetra_Vector *x =
dynamic_cast<Epetra_Vector*>( d_linear_problem->GetLHS() );
const Epetra_Vector *b =
dynamic_cast<Epetra_Vector*>( d_linear_problem->GetRHS() );
// Setup the residual.
Epetra_Map row_map = A->RowMap();
Epetra_Vector residual( row_map );
// Iterate.
Epetra_CrsMatrix H = buildH( A );
Epetra_Vector temp_vec( row_map );
d_num_iters = 0;
double residual_norm = 1.0;
double b_norm;
b->NormInf( &b_norm );
double conv_crit = b_norm*tolerance;
while ( residual_norm > conv_crit && d_num_iters < max_iters )
{
H.Apply( *x, temp_vec );
x->Update( 1.0, temp_vec, 1.0, *b, 0.0 );
A->Apply( *x, temp_vec );
residual.Update( 1.0, *b, -1.0, temp_vec, 0.0 );
residual.NormInf( &residual_norm );
++d_num_iters;
}
}
bool LOCA::Epetra::Interface::MultiPoint::
computeF(const Epetra_Vector& x, Epetra_Vector& F, const FillType fillFlag)
{
bool stat = true;
// Copy owned parts of vector from vector with global map
// to one with split map
solution->Epetra_Vector::operator=(x);
solutionOverlap->Import(*solution, *overlapImporter, Insert);
EpetraExt::BlockVector residual(*solution);
for (int i=0; i < timeStepsOnTimeDomain; i++) {
solution->ExtractBlockValues(splitVec, (*rowIndex)[i]);
splitRes.PutScalar(0.0);
// Pass step index so application can adjust parameters
iReq->setMultiPointParameter((*rowIndex)[i]);
stat = iReq->computeF(splitVec, splitRes, fillFlag);
residual.LoadBlockValues(splitRes, (*rowIndex)[i]);
}
// F does not know it is a clone of a block vector,
// so must copy values from residual
// -- can be fixed? Maybe make residual a view of F instad of copying.
F = residual;
return stat;
}
Residual Analyzer::translation(const ConstImageAdapter<double>&image,
const ImagePoint& where, int _patchsize) const {
debug(LOG_DEBUG, DEBUG_LOG, 0, "get translation at %s",
std::string(where).c_str());
// create the subwindow we want to lock at
int xoffset = where.x() - _patchsize / 2;
int yoffset = where.y() - _patchsize / 2;
ImagePoint patchcorner(xoffset, yoffset);
ImageRectangle window(patchcorner,
ImageSize(patchsize, _patchsize));
debug(LOG_DEBUG, DEBUG_LOG, 0, "window: %s",
window.toString().c_str());
// we need a phase correlator to measure the transform
transform::PhaseCorrelator pc(false);
// compute the translation between the windows
WindowAdapter<double> frompatch(image, window);
WindowAdapter<double> topatch(baseimage, window);
std::pair<Point, double> delta
= pc(frompatch, topatch);
Point translation = delta.first;
double weight = delta.second;
debug(LOG_DEBUG, DEBUG_LOG, 0, "%s -> %s",
where.toString().c_str(),
translation.toString().c_str());
// add the residual to the result set
Residual residual(where, translation, weight);
return residual;
}
std::vector< Eigen::VectorXd > NormalVarianceMixtureBaseError::simulate(std::vector< Eigen::VectorXd > Y)
{
std::vector< Eigen::VectorXd > residual( Y.size());
for(int i = 0; i < Y.size(); i++){
residual[i] = sigma * (Rcpp::as< Eigen::VectorXd >(Rcpp::rnorm( Y[i].size()) ));
if(common_V == 0){
for(int ii = 0; ii < residual[i].size(); ii++)
{
double V = simulate_V();
Vs[i][ii] = V;
residual[i][ii] *= sqrt(V);
}
} else {
double V = simulate_V();
for(int ii = 0; ii < residual[i].size(); ii++)
{
Vs[i][ii] = V;
residual[i][ii] *= sqrt(V);
}
}
}
return(residual);
}
bool VxlSelfCalib::calibIdenticalK(const vcl_vector< vnl_matrix_fixed<double, 3, 4> > & projections,
const vnl_matrix_fixed<double, 3, 3> & initK,
const vnl_vector_fixed<double, 3> & init_p,
vnl_matrix_fixed<double, 3, 3> & finalK,
vnl_vector_fixed<double, 3> & final_p)
{
assert(projections.size() >= 3);
vgl_point_2d<double> pp(initK[0][2], initK[1][2]);
calibIdenticalKEqu195_Residual residual(projections, pp);
vnl_vector<double> x(4, 0.0);
x[0] = initK[0][0];
x[1] = init_p[0];
x[2] = init_p[1];
x[3] = init_p[2];
vnl_levenberg_marquardt lmq(residual);
bool isMinized = lmq.minimize(x);
if (!isMinized) {
vcl_cerr<<"Error: minimization failed.\n";
vcl_cerr<<"x = "<<x<<vcl_endl;
lmq.diagnose_outcome();
return false;
}
lmq.diagnose_outcome();
vcl_cerr<<"x = "<<x<<vcl_endl;
return true;
}
/**
In order to do so, first a reasonable dt for stability is calculated and F,G,RHS are evaluated.
Afterwards, the Poisson pressure equation is solved and the velocitys are updated.
\param[in] printInfo boolean if additional informations on the fields and rediduum of p are printed
\param[in] verbose boolean if debbuging information should be printed (standard: false)
*/
void Compute::TimeStep(bool printInfo, bool verbose=false)
{
// TODO: test
// compute dt
if (verbose) std::cout << "Computing the timestep width..." << std::flush; // only for debugging issues
real_t dt = compute_dt();
if (verbose) std::cout << "Done.\n" << std::flush; // only for debugging issues
// compute F, G
MomentumEqu(dt);
update_boundary_values(); //update boundary values
// compute rhs
RHS(dt);
// solve Poisson equation
real_t residual(_epslimit + 1.0);
index_t iteration(0);
//while (iteration <= _param->IterMax() && residual > _epslimit){
while (true){
// one solver cycle is done here
residual = _solver->Cycle(_p, _rhs);
iteration++;
if (iteration > _param->IterMax()){
//if (printInfo) {
std::cout << "Warning: Solver did not converge! Residual: " << residual << "\n";
//}
break;
} else if (residual < _epslimit){
//if (printInfo) {
std::cout << "Solver converged after " << iteration << " timesteps. Residual: " << residual << "\n";
//}
break;
}
}
// compute new velocitys u, v
NewVelocities(dt);
update_boundary_values();
//update total time
_t += dt;
// print information
if (printInfo){
std::cout << "============================================================\n";
// total simulated time
std::cout << "Total simulated time: t = " << _t << "\n";
// timestep
std::cout << "Last timestep: dt = " << dt << "\n";
// magnitudes of the fields
std::cout << "max(F) = " << _F->AbsMax() << ", max(G) = " << _G->AbsMax() << ", max(rhs) = " << _rhs->AbsMax() << "\n";
std::cout << "max(u) = " << _u->AbsMax() << ", max(v) = " << _v->AbsMax() << ", max(p) = " << _p->AbsMax() << "\n";
//std::cout << "Average value of rhs: " << _rhs->average_value() << "\n";
std::cout << "============================================================\n";
}
}
void Domain1D::
eval(size_t jg, doublereal* xg, doublereal* rg,
integer* mask, doublereal rdt)
{
if (jg != npos && (jg + 1 < firstPoint() || jg > lastPoint() + 1)) {
return;
}
// if evaluating a Jacobian, compute the steady-state residual
if (jg != npos) {
rdt = 0.0;
}
// start of local part of global arrays
doublereal* x = xg + loc();
doublereal* rsd = rg + loc();
integer* diag = mask + loc();
size_t jmin, jmax, jpt, j, i;
jpt = jg - firstPoint();
if (jg == npos) { // evaluate all points
jmin = 0;
jmax = m_points - 1;
} else { // evaluate points for Jacobian
jmin = std::max<size_t>(jpt, 1) - 1;
jmax = std::min(jpt+1,m_points-1);
}
for (j = jmin; j <= jmax; j++) {
if (j == 0 || j == m_points - 1) {
for (i = 0; i < m_nv; i++) {
rsd[index(i,j)] = residual(x,i,j);
diag[index(i,j)] = 0;
}
} else {
for (i = 0; i < m_nv; i++) {
rsd[index(i,j)] = residual(x,i,j)
- timeDerivativeFlag(i)*rdt*(value(x,i,j) - prevSoln(i,j));
diag[index(i,j)] = timeDerivativeFlag(i);
}
}
}
}
static void
KBDWindow ( float* window, unsigned int size, float alpha )
{
double sumvalue = 0.;
double scale;
int i;
scale = 0.25 / sqrt (size);
for ( i = 0; i < (int)size/2; i++ )
window [i] = sumvalue += Bessel_I_0 ( M_PI * alpha * residual (4.*i/size - 1.) );
// need to add one more value to the nomalization factor at size/2:
sumvalue += Bessel_I_0 ( M_PI * alpha * residual (4.*(size/2)/size-1.) );
// normalize the window and fill in the righthand side of the window:
for ( i = 0; i < (int)size/2; i++ )
window [size-1-i] = window [i] = /*sqrt*/ ( window [i] / sumvalue ) * scale;
}
//----------------------------------------------------------------------
Bart::LogitResidualData *
LogitBartPosteriorSampler::create_and_store_residual(int i) {
Ptr<BinomialRegressionData> data_point(model_->dat()[i]);
double original_prediction = model_->predict(data_point->x());
boost::shared_ptr<Bart::LogitResidualData> residual(
new Bart::LogitResidualData(data_point, original_prediction));
residuals_.push_back(residual);
return residual.get();
}
void lasso2(const std::vector<float>& y,const std::vector<float>& x,std::vector<float>& w,unsigned int max_fiber)
{
unsigned int y_dim = y.size();
std::vector<float> residual(y);
std::vector<float> tmp(y_dim);
std::vector<char> fib_map(y_dim);
w.resize(y_dim);
float step_size = decomposition_fraction;
unsigned int max_iter = ((float)max_fiber/step_size);
unsigned char total_fiber = 0;
for(int fib_index = 0;fib_index < max_iter;++fib_index)
{
// calculate the correlation with each SFO
image::matrix::vector_product(&*x.begin(),&*residual.begin(),&*tmp.begin(),
image::dyndim(y_dim,y_dim));
// get the most correlated orientation
int dir = std::max_element(tmp.begin(),tmp.end())-tmp.begin();
float corr = tmp[dir];
if(corr < 0.0)
break;
if(!fib_map[dir])
{
total_fiber++;
if(total_fiber > max_fiber)
break;
fib_map[dir] = 1;
}
std::vector<float>::const_iterator xi = x.begin()+dir*y_dim;
if(fib_index == 0)
{
std::vector<float>::const_iterator xj = x.begin();
float min_step_value = std::numeric_limits<float>::max();
int min_step_dir = 0;
for(int index = 0;index < y_dim;++index,xj += y_dim)
{
if(index == dir)
continue;
float value = lar_get_step(xi,xi,xj,residual.begin(),y_dim);
if(value < min_step_value)
{
min_step_dir = index;
min_step_value = value;
}
}
w[dir] += min_step_value;
image::vec::axpy(residual.begin(),residual.end(),-min_step_value,xi);
}
else
{
w[dir] += corr*step_size;
image::vec::axpy(residual.begin(),residual.end(),-corr*step_size,xi);
}
}
}
void Foam::amgPrecon::precondition
(
scalarField& x,
const scalarField& b,
const direction cmpt
) const
{
// Execute preconditioning
residual(x, b, cmpt);
cycle(x, b, cmpt);
}
void slae::gaussSeidel( myVector & oX, int iMaxIter, int & oIterCount, double iEps )
{
int k;
double res = 1;
for( k = 0; k < iMaxIter && res > iEps; ++k )
{
iteration();
res = residual();
}
oX = m_AprX;
oIterCount = k;
}
void run_solver(MatrixType const & matrix, VectorType const & rhs, VectorType const & ref_result, SolverTag const & solver, PrecondTag const & precond)
{
VectorType result(rhs);
VectorType residual(rhs);
result = viennacl::linalg::solve(matrix, rhs, solver, precond);
residual -= viennacl::linalg::prod(matrix, result);
std::cout << " > Relative residual: " << viennacl::linalg::norm_2(residual) / viennacl::linalg::norm_2(rhs) << std::endl;
std::cout << " > Iterations: " << solver.iters() << std::endl;
result -= ref_result;
std::cout << " > Relative deviation from result: " << viennacl::linalg::norm_2(result) / viennacl::linalg::norm_2(ref_result) << std::endl;
}
bool SSiPPSolver::checkSolved(State* s)
{
std::list<State*> open, closed;
State* tmp = s;
if (!tmp->checkBits(mdplib::SOLVED_SSiPP)) {
open.push_front(s);
s->setBits(mdplib::CLOSED_SSiPP);
}
bool rv = true;
while (!open.empty()) {
tmp = open.front();
open.pop_front();
closed.push_front(tmp);
Action* a = greedyAction(problem_, tmp);
tmp->setBestAction(a);
if (problem_->goal(tmp))
continue;
if (tmp->deadEnd()) {
rv = false;
continue;
}
if (residual(problem_, tmp) > epsilon_) {
rv = false;
}
// Return if it ran out of time
if (ranOutOfTime()) {
return false;
}
for (Successor su : problem_->transition(tmp, a)) {
State* next = su.su_state;
if (!next->checkBits(mdplib::SOLVED_SSiPP) &&
!next->checkBits(mdplib::CLOSED_SSiPP)) {
open.push_front(next);
next->setBits(mdplib::CLOSED_SSiPP);
}
}
}
if (rv) {
for (State* sc : closed) {
sc->setBits(mdplib::SOLVED_SSiPP);
}
} else {
while (!closed.empty()) {
tmp = closed.front();
closed.pop_front();
tmp->clearBits(mdplib::CLOSED_SSiPP);
bellmanUpdate(problem_, tmp);
}
}
return rv;
}
Foam::tmp<Foam::scalarField> Foam::lduMatrix::residual
(
const scalarField& psi,
const scalarField& source,
const FieldField<Field, scalar>& interfaceBouCoeffs,
const lduInterfaceFieldPtrsList& interfaces,
const direction cmpt
) const
{
tmp<scalarField> trA(new scalarField(psi.size()));
residual(trA.ref(), psi, source, interfaceBouCoeffs, interfaces, cmpt);
return trA;
}
Real
IsotropicPlasticity::computeResidual(unsigned qp, Real effectiveTrialStress, Real scalar)
{
Real residual(0);
_hardening_slope = 0;
if (_yield_condition > 0)
{
_hardening_slope = computeHardening( qp, scalar );
// The order here is important. The final term can be small, and we don't want it lost to roundoff.
residual = (effectiveTrialStress - _hardening_variable[qp] - _yield_stress) - (3 * _shear_modulus * scalar);
_hardening_variable[qp] = _hardening_variable_old[qp] + (_hardening_slope * scalar);
}
return residual;
}
DoubleVector RowDoubleMatrix::conjugateGradient(const DoubleVector &B, double epsilon, unsigned int niter, bool printMessages, unsigned int messageStep) const
{
DoubleVector X(size_, 0.0); // начальное приближение - вектор нулей
DoubleVector resid(size_); // невязка
DoubleVector direction; // направление поиска
DoubleVector temp(size_); // ременное хранилище для обмена данными
double resid_norm; // норма невязки
double alpha;
double beta;
double resid_resid, resid_resid_new;
residual(X, B, resid);
direction = resid;
resid_norm = resid.norm_2();
if (printMessages) std::cout << "Начальная невязка: " << resid_norm << std::endl;
if (resid_norm > epsilon)
{
resid_resid = resid * resid;
for (unsigned int i = 0; i < niter; i++)
{
product(direction, temp);
// std::cout << direction.norm_2() << " " << temp.norm_2() << std::endl;
alpha = (resid_resid) / (direction * temp);
X += alpha * direction;
resid -= alpha * temp;
resid_resid_new = resid * resid;
resid_norm = sqrt(resid_resid_new);
if (resid_norm <= epsilon)
{
if (printMessages)
std::cout << "Решение найдено. Итераций: " << i << ", невязка: " << resid_norm << std::endl;
break;
}
if (printMessages && (i % messageStep == 0))
std::cout << i << ", невязка: " << resid_norm << std::endl;
beta = (resid_resid_new) / (resid_resid);
// d = r + d*beta
direction.scale(beta);
direction += resid;
//
resid_resid = resid_resid_new;
}
}
return X;
}
// Generate expected return of next n periods
vec ARMA11::predict(int n, vec y)
{
int m = y.n_elem-1;
vec x = residual(y);
vec er(n);
er[0] = mu-phi*(y[m]-mu)+psi*x[m];
if (n>1) {
for (int i=1; i<n; i++) {
er[i] = mu-phi*er[i-1];
}
}
return er;
}
bool LRTDPSolver::checkSolved(mlcore::State* s)
{
std::list<mlcore::State*> open, closed;
mlcore::State* tmp = s;
if (!tmp->checkBits(mdplib::SOLVED)) {
open.push_front(s);
}
bool rv = true;
while (!open.empty()) {
tmp = open.front();
open.pop_front();
mlcore::Action* a = greedyAction(problem_, tmp);
if (problem_->goal(tmp) || tmp->deadEnd())
continue;
closed.push_front(tmp);
tmp->setBits(mdplib::CLOSED);
if (residual(problem_, tmp) > epsilon_)
rv = false;
for (mlcore::Successor su : problem_->transition(tmp, a)) {
mlcore::State* next = su.su_state;
if (!next->checkBits(mdplib::SOLVED) &&
!next->checkBits(mdplib::CLOSED)) {
open.push_front(next);
}
}
}
if (rv) {
for (mlcore::State* sc : closed) {
sc->setBits(mdplib::SOLVED);
sc->clearBits(mdplib::CLOSED);
}
} else {
while (!closed.empty()) {
tmp = closed.front();
closed.pop_front();
tmp->clearBits(mdplib::CLOSED);
bellmanUpdate(problem_, tmp);
}
}
return rv;
}
void
GlobalStrainUserObject::finalize()
{
std::vector<Real> residual(9);
std::vector<Real> jacobian(81);
std::copy(&_residual(0, 0), &_residual(0, 0) + 9, residual.begin());
std::copy(&_jacobian(0, 0, 0, 0), &_jacobian(0, 0, 0, 0) + 81, jacobian.begin());
gatherSum(residual);
gatherSum(jacobian);
std::copy(residual.begin(), residual.end(), &_residual(0, 0));
std::copy(jacobian.begin(), jacobian.end(), &_jacobian(0, 0, 0, 0));
}
void operator()() {
/* Set up MPI */
setup ();
/* Set up norm */
compute_l2_norm ();
#if USE_PFUNC
task root_task;
attribute root_attribute (false /*nested*/, false /*grouped*/);
#endif
/* Iterate until one of the stopping conditions is triggered */
int num_selected = 0;
while (true) {
/* Create a projector and execute the loop for local result */
space_type my_space =
partitioner_t::create (0, N, mpi_rank, mpi_size);
Projector projector
(&R,&snp_map,&materializer,&filter,block_size,M,N,K);
#if USE_PFUNC
ProjectReduceType root_project (my_space, projector, *global_taskmgr);
pfunc::spawn (*global_taskmgr, root_task, root_attribute, root_project);
pfunc::wait (*global_taskmgr, root_task);
#else
projector (my_space);
#endif
/* Reduce globally to get the result */
edge_type selected = find_global_max (projector.get_result ());
X.insert (selected);
add_to_shadow (selected);
++num_selected;
/* Re-estimate the coefficients given the new information */
refit ();
/* Compute the residual again */
residual ();
/* See if we have reached termination conditions */
if (MAX_SELECT<num_selected || EPSILON>=fro_norm()) break;
}
/* Clean up MPI */
cleanup ();
}
DG_INLINE void FactorizeLCP(const dgJointInfo* const jointInfoArray, const dgJacobian* const internalForces, dgJacobianMatrixElement* const matrixRow, dgForcePair& accel)
{
dgAssert((dgUnsigned64(&m_bodyMass) & 0x0f) == 0);
m_bodyMass.SetZero();
if (m_body->GetInvMass().m_w != dgFloat32(0.0f)) {
GetInertia();
}
if (m_joint) {
dgAssert(m_parent);
const dgJointInfo* const jointInfo = &jointInfoArray[m_joint->m_index];
dgAssert(jointInfo->m_joint == m_joint);
dgAssert(jointInfo->m_joint->GetBody0() == m_body);
dgAssert(jointInfo->m_joint->GetBody1() == m_parent->m_body);
const dgInt32 m0 = jointInfo->m_m0;
const dgInt32 m1 = jointInfo->m_m1;
const dgJacobian& y0 = internalForces[m0];
const dgJacobian& y1 = internalForces[m1];
const dgInt32 first = jointInfo->m_pairStart;
dgInt32 clampedValue = m_dof - 1;
//dgSpatialVector& accel = force.m_joint;
for (dgInt32 i = 0; i < m_dof; i++) {
dgInt32 k = m_sourceJacobianIndex[i];
const dgJacobianMatrixElement* const row = &matrixRow[first + k];
//dgFloat32 force = row->m_force + row->m_invJMinvJt * residual.GetScalar();
dgFloat32 force = row->m_force;
if ((force >= row->m_lowerBoundFrictionCoefficent) && (force <= row->m_upperBoundFrictionCoefficent)) {
dgVector residual(row->m_JMinv.m_jacobianM0.m_linear.CompProduct4(y0.m_linear) + row->m_JMinv.m_jacobianM0.m_angular.CompProduct4(y0.m_angular) +
row->m_JMinv.m_jacobianM1.m_linear.CompProduct4(y1.m_linear) + row->m_JMinv.m_jacobianM1.m_angular.CompProduct4(y1.m_angular));
residual = dgVector(row->m_coordenateAccel) - residual.AddHorizontal();
accel.m_joint[i] = -residual.GetScalar();
} else {
dgAssert (0);
dgSwap (m_sourceJacobianIndex[i], m_sourceJacobianIndex[clampedValue]);
i --;
m_dof --;
clampedValue --;
}
}
GetJacobians(jointInfo, matrixRow);
}
Factorize();
}
static int linreg(double *X,double *Y,double *S,double *U,double *T,double *B,double *R,int m,int n) {
// RS REM int i;
mat_store(X,T,m,n);
if (mat_gram_schmidt(S,U,T,m,n,TOLERANCE)<0) {
sur_print("\nUnable to solve!");
// showmatrix(X,m,n);
WAIT; return(-1);
}
mat_transp(T,S,m,n);
mat_mlt(S,T,Y,n,m,1);
solve_upper(B,U,S,n,1,TOLERANCE);
fixmat(B,n);
return(residual(R,B,Y,X,m,n));
}
