void
channel(Home home, const IntVarArgs& x, const SetVarArgs& y) {
if (home.failed()) return;
ViewArray<Int::CachedView<Int::IntView> > xa(home,x.size());
for (int i=x.size(); i--;)
new (&xa[i]) Int::CachedView<Int::IntView>(x[i]);
ViewArray<Set::CachedView<Set::SetView> > ya(home,y.size());
for (int i=y.size(); i--;)
new (&ya[i]) Set::CachedView<Set::SetView>(y[i]);
GECODE_ES_FAIL((Set::Int::ChannelInt<Set::SetView>::post(home,xa,ya)));
}
void apply( stk_classic::mesh::Bucket::iterator bucket_i ,
stk_classic::mesh::Bucket::iterator bucket_j ) const
{
typedef typename stk_classic::mesh::FieldTraits<field_type>::data_type scalar ;
stk_classic::mesh::BucketArray<field_type> xa( x_ , bucket_i , bucket_j );
scalar * xi = xa.contiguous_data();
scalar * xe = xi + xa.size();
std::fill(xi, xe, scalar_);
}
// [[Rcpp::export]]
RcppExport SEXP convolve4cpp(SEXP a, SEXP b) {
Rcpp::NumericVector xa(a), xb(b);
int n_xa = xa.size(), n_xb = xb.size();
Rcpp::NumericVector xab(n_xa + n_xb - 1);
typedef Rcpp::NumericVector::iterator vec_iterator;
vec_iterator ia = xa.begin(), ib = xb.begin();
vec_iterator iab = xab.begin();
for (int i = 0; i < n_xa; i++)
for (int j = 0; j < n_xb; j++)
iab[i + j] += ia[i] * ib[j];
return xab;
}
RcppExport SEXP convolve3cpp(SEXP a, SEXP b){
Rcpp::NumericVector xa(a);
Rcpp::NumericVector xb(b);
int n_xa = xa.size() ;
int n_xb = xb.size() ;
int nab = n_xa + n_xb - 1;
Rcpp::NumericVector xab(nab);
for (int i = 0; i < n_xa; i++)
for (int j = 0; j < n_xb; j++)
xab[i + j] += xa[i] * xb[j];
return xab ;
}
void apply( stk_classic::mesh::Bucket::iterator bucket_i ,
stk_classic::mesh::Bucket::iterator bucket_j ,
reduce_type * inout ) const
{
stk_classic::mesh::BucketArray<field_type> xa( field_x , bucket_i , bucket_j );
scalar * xi = xa.contiguous_data();
scalar * xe = xi + xa.size();
scalar tmp = 0 ;
while ( xi != xe ) { tmp += *xi * *xi ; ++xi; }
*inout += tmp ;
}
matrix2d<T> matrix2d<T>::extractMatrix2d(size_t x, size_t y, size_t w,
size_t h) {
/* TEST ME TEST ME TEST ME TEST ME TEST ME TEST ME TEST ME TEST ME TEST ME */
matrix2d<T> result(h, w);
size_t x2[] = {h, w}, s[] = {rows_, 1};
std::valarray<size_t> xa(x2, 2), sa(s, 2);
result.data_ = data_[(const std::gslice)std::gslice(y * rows_ + x, xa, sa)];
return result;
}
void apply( stk_classic::mesh::Bucket::iterator bucket_i ,
stk_classic::mesh::Bucket::iterator bucket_j ,
reduce_type * inout ) const
{
stk_classic::mesh::BucketArray<field_type> xa( field_x , bucket_i , bucket_j );
stk_classic::mesh::BucketArray<field_type> ya( field_y , bucket_i , bucket_j );
scalar * xi = xa.contiguous_data();
scalar * yi = ya.contiguous_data();
scalar * ye = yi + ya.size();
scalar tmp = 0 ;
while ( yi != ye ) { tmp += *xi++ * *yi++ ; }
*inout += tmp ;
}
RcppExport SEXP test_cpp(SEXP a, SEXP b) {
Rcpp::NumericVector xa(a);
Rcpp::NumericVector xb(b);
// Rcpp::StringVector aaa = "deine mudder";
Rcpp::StringVector aaa = a;
aaa.push_back("1") ;
int n_xa = xa.size(), n_xb = xb.size();
int nab = n_xa + n_xb - 1;
Rcpp::NumericVector xab(nab);
for (int i = 0; i < n_xa; i++)
for (int j = 0; j < n_xb; j++)
xab[i + j] += xa[i] * xb[j];
return aaa ;
return xab;
}
/*!
* \brief LAArmadillo::svd
* Compute singular value decomposition of x
* \param u
* \param d
* \param v
* \param x
* \return
*/
bool LAArmadillo::svd(OiMat &u, OiVec &d, OiMat &v, const OiMat &x){
int matSize = x.getRowCount();
arma::mat ua(matSize, matSize), va(matSize, matSize), xa(matSize, matSize);
arma::vec da(matSize);
this->oiMat2Arma(xa, x);
arma::svd(ua, da, va, xa);
this->arma2OiMat(u, ua);
this->arma2OiMat(v, va);
this->arma2OiVec(d, da);
return true;
}
int main() {
//std::vector<fixed16> xa(1024 * 1024)
//typedef float fp_t;
auto & os = std::cout;
{
float m_x = 0.0;
float m_y =-1343.13423412312312;
float m_z = 1.0;
auto m_ele = "FUK";
boost::io::ios_all_saver ias( os );
os << std::fixed << std::setprecision(4) << std::setw(10) << m_x << std::setw(10) << m_y << std::setw(10) << m_z << m_ele[0] << m_ele[1] << m_ele[2] << " 0 0 0 0 0 0 0 0 0 0 0 0\n";
}
// std::cout << "'" << std::fixed << std::setw(10) << std::setprecision(4) << 123.1234567890 << "'\n";
// std::cout << "'" << std::fixed << std::setw(10) << std::setprecision(4) << float(0.0) << "'\n";
// std::cout << 0.0 << "\n";
typedef fixed22 fp_t;
#if 0
std::valarray<fp_t> xa(1024 * 1024);
std::valarray<fp_t> xb(1024 * 1024);
std::valarray<fp_t> xc(1024 * 1024);
for( size_t i = 0; i < 100; ++i ) {
xa = 1.11;
xb = 2.21;
xc = xa + xb;
long sum = xc.sum();
std::cout << "sum: " << sum << "\n";
}
#endif
for( float i = -2.0; i < 2.0; i+= (1/(1024.0*2)) ) {
fp_t a = i;
std::cout << i << " " << float(a) << " " << a << "\n";
}
{
fp_t a = 1.001;
fp_t b = 2.0;
std::cout << a << " " << b << "\n";
}
}
void DeviceSkin::calcRegions()
{
const int numAreas = m_parameters.buttonAreas.size();
for (int i=0; i<numAreas; i++) {
QPolygon xa(m_parameters.buttonAreas[i].area.count());
int n = m_parameters.buttonAreas[i].area.count();
for (int p=0; p<n; p++) {
xa.setPoint(p,transform.map(m_parameters.buttonAreas[i].area[p]));
}
if ( n == 2 ) {
buttonRegions[i] = QRegion(xa.boundingRect());
} else {
buttonRegions[i] = QRegion(xa);
}
}
}
int main() {
try {
throw xa();
} catch (...) {
test();
}
try {
throw xb();
} catch (...) {
test();
}
try {
throw xc();
} catch (...) {
test();
}
_PASS;
}
void apply( stk_classic::mesh::Bucket::iterator bucket_i ,
stk_classic::mesh::Bucket::iterator bucket_j ) const
{
typedef typename stk_classic::mesh::FieldTraits<field_type>::data_type scalar ;
stk_classic::mesh::BucketArray<field_type> xa( x_ , bucket_i , bucket_j );
stk_classic::mesh::BucketArray<field_type> ya( y_ , bucket_i , bucket_j );
scalar * xi = xa.contiguous_data();
scalar * yi = ya.contiguous_data();
scalar * ye = yi + ya.size();
//is it worthwhile to optimize by adding special loops for
//cases where alpha or beta equal 1.0 or 0.0, or where x==y?
while(yi < ye) {
*yi = alpha_* *xi++ + beta_* *yi;
++yi;
}
}
Vector OdeErrControl(
Method &method,
const Scalar &ti ,
const Scalar &tf ,
const Vector &xi ,
const Scalar &smin ,
const Scalar &smax ,
Scalar &scur ,
const Vector &eabs ,
const Scalar &erel ,
Vector &ef ,
Vector &maxabs,
size_t &nstep )
{
// check simple vector class specifications
CheckSimpleVector<Scalar, Vector>();
size_t n = size_t(xi.size());
CPPAD_ASSERT_KNOWN(
smin <= smax,
"Error in OdeErrControl: smin > smax"
);
CPPAD_ASSERT_KNOWN(
size_t(eabs.size()) == n,
"Error in OdeErrControl: size of eabs is not equal to n"
);
CPPAD_ASSERT_KNOWN(
size_t(maxabs.size()) == n,
"Error in OdeErrControl: size of maxabs is not equal to n"
);
size_t m = method.order();
CPPAD_ASSERT_KNOWN(
m > 1,
"Error in OdeErrControl: m is less than or equal one"
);
bool ok;
bool minimum_step;
size_t i;
Vector xa(n), xb(n), eb(n), nan_vec(n);
// initialization
Scalar zero(0);
Scalar one(1);
Scalar two(2);
Scalar three(3);
Scalar m1(m-1);
Scalar ta = ti;
for(i = 0; i < n; i++)
{ nan_vec[i] = nan(zero);
ef[i] = zero;
xa[i] = xi[i];
if( zero <= xi[i] )
maxabs[i] = xi[i];
else maxabs[i] = - xi[i];
}
nstep = 0;
Scalar tb, step, lambda, axbi, a, r, root;
while( ! (ta == tf) )
{ // start with value suggested by error criteria
step = scur;
// check maximum
if( smax <= step )
step = smax;
// check minimum
minimum_step = step <= smin;
if( minimum_step )
step = smin;
// check if near the end
if( tf <= ta + step * three / two )
tb = tf;
else tb = ta + step;
// try using this step size
nstep++;
method.step(ta, tb, xa, xb, eb);
step = tb - ta;
// check if this steps error estimate is ok
ok = ! (hasnan(xb) || hasnan(eb));
if( (! ok) && minimum_step )
{ ef = nan_vec;
return nan_vec;
}
// compute value of lambda for this step
lambda = Scalar(10) * scur / step;
for(i = 0; i < n; i++)
{ if( zero <= xb[i] )
axbi = xb[i];
else axbi = - xb[i];
a = eabs[i] + erel * axbi;
if( ! (eb[i] == zero) )
{ r = ( a / eb[i] ) * step / (tf - ti);
root = exp( log(r) / m1 );
if( root <= lambda )
lambda = root;
}
}
if( ok && ( one <= lambda || step <= smin * three / two) )
{ // this step is within error limits or
// close to the minimum size
ta = tb;
for(i = 0; i < n; i++)
{ xa[i] = xb[i];
ef[i] = ef[i] + eb[i];
if( zero <= xb[i] )
axbi = xb[i];
else axbi = - xb[i];
if( axbi > maxabs[i] )
maxabs[i] = axbi;
}
}
if( ! ok )
{ // decrease step an see if method will work this time
scur = step / two;
}
else if( ! (ta == tf) )
{ // step suggested by the error criteria is not used
// on the last step because it may be very small.
scur = lambda * step / two;
}
}
return xa;
}
void createAtomicLib() {
typedef CG<double> CGD;
typedef AD<CGD> ADCGD;
/**
* Tape model
*/
std::vector<ADCGD> xa(na_);
for (size_t j = 0; j < na_; j++)
xa[j] = xa_[j];
CppAD::Independent(xa);
std::vector<ADCGD> ya(ns_);
atomicFunction(xa, ya);
ADFun<CGD> fun;
fun.Dependent(ya);
/**
* Compile
*/
std::string lName = getAtomicLibName()+(ignoreParameters_ ? "" : "All");
ModelCSourceGen<double> compHelpL(fun, lName);
compHelpL.setCreateForwardZero(true);
compHelpL.setCreateForwardOne(true);
compHelpL.setCreateReverseOne(true);
compHelpL.setCreateReverseTwo(true);
compHelpL.setTypicalIndependentValues(xa_);
if (ignoreParameters_) {
std::vector<std::set<size_t> > jacSparAll = extra::jacobianSparsitySet<std::vector<std::set<size_t> > >(fun);
std::vector<std::set<size_t> > jacSpar(jacSparAll.size());
for (size_t i = 0; i < jacSparAll.size(); i++) {
// only differential information for states and controls
std::set<size_t>::const_iterator itEnd = jacSparAll[i].upper_bound(ns_ + nm_ - 1);
if (itEnd != jacSparAll[i].begin())
jacSpar[i].insert(jacSparAll[i].begin(), itEnd);
}
compHelpL.setCustomSparseJacobianElements(jacSpar);
std::vector<std::set<size_t> > hessSparAll = extra::hessianSparsitySet<std::vector<std::set<size_t> > >(fun);
std::vector<std::set<size_t> > hessSpar(hessSparAll.size());
for (size_t i = 0; i < ns_ + nm_; i++) {
std::set<size_t>::const_iterator it = hessSparAll[i].upper_bound(i); // only the lower left side
if (it != hessSparAll[i].begin())
hessSpar[i].insert(hessSparAll[i].begin(), it);
}
compHelpL.setCustomSparseHessianElements(hessSpar);
}
ModelLibraryCSourceGen<double> compDynHelpL(compHelpL);
compDynHelpL.setVerbose(verbose_);
SaveFilesModelLibraryProcessor<double>::saveLibrarySourcesTo(compDynHelpL, "sources_" + lName);
DynamicModelLibraryProcessor<double> p(compDynHelpL, lName);
GccCompiler<double> compiler;
if (!compilerFlags_.empty())
compiler.setCompileFlags(compilerFlags_);
atomicDynamicLib_.reset(p.createDynamicLibrary(compiler));
/**
* load the model
*/
atomicModel_.reset(atomicDynamicLib_->model(lName));
}
int
main2()
{
xa();
xb();
}
int xb() { xa1(); xb2(); xa(); }
ILOSTLBEGIN
#include <unistd.h>
#include <math.h>
int
main (int argc, char **argv)
{
int N = 5000;
if (argc > 1){
N = atoi(argv[1]);
}
double M = 100000;
double** w = new double*[N];
for (int i = 0; i < N; i++){
w[i] = new double[N];
for (int j = 0; j < N; j++){
if (i == j){
w[i][j] = i;
}
else {
w[i][j] = i-abs(i-j);
}
}
}
IloEnv env;
try {
IloModel model(env);
IloNumVarArray xa(env);
IloNumVarArray xi(env);
for (int i = 0; i < N; i++){
xa.add(IloNumVar(env));
xi.add(IloNumVar(env));
}
IloNumExpr obj = xa[0] + xi[0];
for (int i = 1; i < N; i++){
obj = obj + xa[i];
obj = obj + xi[i];
}
IloRangeArray con(env);
for (int i = 0; i < N; i++){
for (int j = 0; j < N; j++){
con.add(xa[i] + xi[j] >= w[i][j]);
}
}
model.add(con);
model.add(IloMinimize(env, obj));
// populate model
IloCplex cplex(model);
if ( !cplex.solve() ) {
env.error() << "Failed to optimize LP" << endl;
throw(-1);
}
env.out() << "Solution value = " << cplex.getObjValue() << endl;
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end();
return 0;
}
//--------------------------------------------------------
// apply_charged_surfaces
//--------------------------------------------------------
void ChargeRegulatorMethodEffectiveCharge::apply_local_forces()
{
double * q = lammpsInterface_->atom_charge();
_atomElectricalForce_.resize(nlocal(),nsd_);
double penalty = poissonSolver_->penalty_coefficient();
if (penalty <= 0.0) throw ATC_Error("ExtrinsicModelElectrostatic::apply_charged_surfaces expecting non zero penalty");
double dx[3];
const DENS_MAT & xa((interscaleManager_->per_atom_quantity("AtomicCoarseGrainingPositions"))->quantity());
// WORKSPACE - most are static
SparseVector<double> dv(nNodes_);
vector<SparseVector<double> > derivativeVectors;
derivativeVectors.reserve(nsd_);
const SPAR_MAT_VEC & shapeFunctionDerivatives((interscaleManager_->vector_sparse_matrix("InterpolateGradient"))->quantity());
DenseVector<INDEX> nodeIndices;
DENS_VEC nodeValues;
NODE_TO_XF_MAP::const_iterator inode;
for (inode = nodeXFMap_.begin(); inode != nodeXFMap_.end(); inode++) {
int node = inode->first;
DENS_VEC xI = (inode->second).first;
double qI = (inode->second).second;
double phiI = nodalChargePotential_[node];
for (int i = 0; i < nlocal(); i++) {
int atom = (atc_->internal_to_atom_map())(i);
double qa = q[atom];
if (qa != 0) {
double dxSq = 0.;
for (int j = 0; j < nsd_; j++) {
dx[j] = xa(i,j) - xI(j);
dxSq += dx[j]*dx[j];
}
if (dxSq < rCsq_) {
// first apply pairwise coulombic interaction
if (!useSlab_) {
double coulForce = qqrd2e_*qI*qa/(dxSq*sqrtf(dxSq));
for (int j = 0; j < nsd_; j++) {
_atomElectricalForce_(i,j) += dx[j]*coulForce; }
}
// second correct for FE potential induced by BCs
// determine shape function derivatives at atomic location
// and construct sparse vectors to store derivative data
for (int j = 0; j < nsd_; j++) {
shapeFunctionDerivatives[j]->row(i,nodeValues,nodeIndices);
derivativeVectors.push_back(dv);
for (int k = 0; k < nodeIndices.size(); k++) {
derivativeVectors[j](nodeIndices(k)) = nodeValues(k); }
}
// compute greens function from charge quadrature
SparseVector<double> shortFePotential(nNodes_);
shortFePotential.add_scaled(greensFunctions_[node],penalty*phiI);
// compute electric field induced by charge
DENS_VEC efield(nsd_);
for (int j = 0; j < nsd_; j++) {
efield(j) = -.1*dot(derivativeVectors[j],shortFePotential); }
// apply correction in atomic forces
double c = qV2e_*qa;
for (int j = 0; j < nsd_; j++) {
if ((!useSlab_) || (j==nsd_)) {
_atomElectricalForce_(i,j) -= c*efield(j);
}
}
}
}
}
}
}
extern "C" void glm_gibbs(double * rZ, double * rxo, double * rlam, int * rmodelprior, double * rpriorprob, double * rbeta1, double * rbeta2, int * rburnin, int * rniter, int * rscalemixture, double * ralpha, int * rno, int * rna, int * rp, double * B_mcmc, double * prob_mcmc, int * gamma_mcmc, double * phi_mcmc, double * lam_mcmc, double * B_rb, double * prob_rb, double * intercept_mcmc, double * xo_scale)
{
GetRNGstate();
//MCMC Variables//
int burnin=*rburnin;
int niter=*rniter;
//Dimensions//
int p=*rp;
int no=*rno;
int na=*rna;
//Phi Variables//
double phi=1.0;
//Yo Variables//
std::vector<double> Z(rZ, rZ+no);
std::vector<double> xo(rxo, rxo+no*p);
standardize_xo(xo,xo_scale,no,p);
std::vector<double> xoyo(p);
double yobar=0;
std::vector<double> xoxo(p*p);
dgemm_( &transT, &transN, &p, &p, &no, &unity, &*xo.begin(), &no, &*xo.begin(), &no, &inputscale0, &*xoxo.begin(), &p );
//Construct Xa//
std::vector<double> xa(p*(p+1)/2); //Triangular Packed Storage
std::vector<double> d(p);
chol_xa(xa,xoxo,d,p);
//Reserve Memory for Submatrices//
std::vector<double> xog; xog.reserve(no*p);
std::vector<double> xogyo; xogyo.reserve(p);
std::vector<double> xogxog_Lamg; xogxog_Lamg.reserve(p*p);
std::vector<double> xag; xag.reserve(na*p);
//Ya Variables//
std::vector<double> xaya(p);
//Beta Variables//
double intercept=0;
std::vector<double> Bols(p);
std::vector<double> B(p,0.0);
std::vector<double> Bg; Bg.reserve(p);
//Lambda Variables//
int scalemixture=*rscalemixture;
double alpha=*ralpha;
std::vector<double> lam(rlam,rlam+p);
std::vector<double> lamg; lamg.reserve(p); //vector instead of diagonal pxp matrix
//Gamma Variables//
std::vector<int> gamma(p,1);
int p_gamma=std::accumulate(gamma.begin(),gamma.end(),0);
bool gamma_diff=true;
int modelprior=*rmodelprior;
//Probability Variables//
std::vector<double> prob(p);
std::vector<double> odds(p);
std::vector<double> priorprob(rpriorprob,rpriorprob+p);
//Theta Variables//
double theta=0.5;
double beta1=*rbeta1;
double beta2=*rbeta2;
//Store Initial Values//
std::copy(B.begin(),B.end(),B_mcmc);
std::copy(prob.begin(),prob.end(),prob_mcmc);
std::copy(gamma.begin(),gamma.end(),gamma_mcmc);
std::copy(lam.begin(),lam.end(),lam_mcmc);
//Run Gibbs Sampler//
for (int t = 1; t < niter; ++t)
{
//Form Submatrices//
if(p_gamma) submatrices_uncollapsed(gamma_diff,B,xog,xag,lamg,Bg,gamma,lam,xo,xa,p_gamma,p,no,na);
//Draw xoyo//
draw_xoyo(Z,xoyo,yobar,xo,xog,Bg,phi,no,p,p_gamma,intercept);
//Draw xaya//
draw_uncollapsed_xaya(xaya,xa,xag,Bg,phi,na,p,p_gamma);
//Compute Probabilities//
if(modelprior==1)
{
bernoulli_probabilities(prob,odds,Bols,d,xoyo,xaya,priorprob,lam,phi);
}else if(modelprior==2)
{
betabinomial_probabilities(prob,odds,Bols,d,xoyo,xaya,theta,lam,phi);
}else
{
uniform_probabilities(prob,odds,Bols,d,xoyo,xaya,lam,phi);
}
//Draw Gamma//
draw_gamma(gamma,p_gamma,prob);
//Draw Theta//
if(modelprior==2) theta=Rf_rbeta(beta1+p_gamma,p-p_gamma+beta2);
//Draw Beta//
draw_beta(gamma,B,Bols,d,lam,phi);
//Draw Intercept//
intercept=yobar+sqrt(1/(no*phi))*Rf_rnorm(0,1);
//Draw Lambda//
if(scalemixture) draw_lambda_t(lam,gamma,alpha,B,phi);
//Store Draws//
intercept_mcmc[t]=intercept;
std::copy(gamma.begin(),gamma.end(),(gamma_mcmc+p*t));
std::copy(prob.begin(),prob.end(),(prob_mcmc+p*t));
std::copy(B.begin(),B.end(),(B_mcmc+p*t));
std::copy(lam.begin(),lam.end(),(lam_mcmc+p*t));
//Rao Blackwell//
if(t>=burnin) rao_blackwell(B_rb,prob_rb,B,prob,burnin,niter);
//Has Gamma Changed?//
gamma_diff=gamma_change(gamma_mcmc,t,p);
}
PutRNGstate();
}
QQuickStateOperation::ActionList QQuickParentChange::actions()
{
Q_D(QQuickParentChange);
if (!d->target || !d->parent)
return ActionList();
ActionList actions;
QQuickAction a;
a.event = this;
actions << a;
if (d->xString.isValid()) {
bool ok = false;
qreal x = d->xString.value.numberLiteral(&ok);
if (ok) {
QQuickAction xa(d->target, QLatin1String("x"), x);
actions << xa;
} else {
QQmlBinding *newBinding = new QQmlBinding(d->xString.value, d->target, qmlContext(this));
QQmlProperty property(d->target, QLatin1String("x"));
newBinding->setTarget(property);
QQuickAction xa;
xa.property = property;
xa.toBinding = QQmlAbstractBinding::getPointer(newBinding);
xa.fromValue = xa.property.read();
xa.deletableToBinding = true;
actions << xa;
}
}
if (d->yString.isValid()) {
bool ok = false;
qreal y = d->yString.value.numberLiteral(&ok);
if (ok) {
QQuickAction ya(d->target, QLatin1String("y"), y);
actions << ya;
} else {
QQmlBinding *newBinding = new QQmlBinding(d->yString.value, d->target, qmlContext(this));
QQmlProperty property(d->target, QLatin1String("y"));
newBinding->setTarget(property);
QQuickAction ya;
ya.property = property;
ya.toBinding = QQmlAbstractBinding::getPointer(newBinding);
ya.fromValue = ya.property.read();
ya.deletableToBinding = true;
actions << ya;
}
}
if (d->scaleString.isValid()) {
bool ok = false;
qreal scale = d->scaleString.value.numberLiteral(&ok);
if (ok) {
QQuickAction sa(d->target, QLatin1String("scale"), scale);
actions << sa;
} else {
QQmlBinding *newBinding = new QQmlBinding(d->scaleString.value, d->target, qmlContext(this));
QQmlProperty property(d->target, QLatin1String("scale"));
newBinding->setTarget(property);
QQuickAction sa;
sa.property = property;
sa.toBinding = QQmlAbstractBinding::getPointer(newBinding);
sa.fromValue = sa.property.read();
sa.deletableToBinding = true;
actions << sa;
}
}
if (d->rotationString.isValid()) {
bool ok = false;
qreal rotation = d->rotationString.value.numberLiteral(&ok);
if (ok) {
QQuickAction ra(d->target, QLatin1String("rotation"), rotation);
actions << ra;
} else {
QQmlBinding *newBinding = new QQmlBinding(d->rotationString.value, d->target, qmlContext(this));
QQmlProperty property(d->target, QLatin1String("rotation"));
newBinding->setTarget(property);
QQuickAction ra;
ra.property = property;
ra.toBinding = QQmlAbstractBinding::getPointer(newBinding);
ra.fromValue = ra.property.read();
ra.deletableToBinding = true;
actions << ra;
}
}
if (d->widthString.isValid()) {
bool ok = false;
qreal width = d->widthString.value.numberLiteral(&ok);
if (ok) {
QQuickAction wa(d->target, QLatin1String("width"), width);
actions << wa;
} else {
QQmlBinding *newBinding = new QQmlBinding(d->widthString.value, d->target, qmlContext(this));
QQmlProperty property(d->target, QLatin1String("width"));
newBinding->setTarget(property);
QQuickAction wa;
wa.property = property;
wa.toBinding = QQmlAbstractBinding::getPointer(newBinding);
wa.fromValue = wa.property.read();
wa.deletableToBinding = true;
actions << wa;
}
}
if (d->heightString.isValid()) {
bool ok = false;
qreal height = d->heightString.value.numberLiteral(&ok);
if (ok) {
QQuickAction ha(d->target, QLatin1String("height"), height);
actions << ha;
} else {
QQmlBinding *newBinding = new QQmlBinding(d->heightString.value, d->target, qmlContext(this));
QQmlProperty property(d->target, QLatin1String("height"));
newBinding->setTarget(property);
QQuickAction ha;
ha.property = property;
ha.toBinding = QQmlAbstractBinding::getPointer(newBinding);
ha.fromValue = ha.property.read();
ha.deletableToBinding = true;
actions << ha;
}
}
return actions;
}
Vector OdeErrControl(
Method &method,
const Scalar &ti ,
const Scalar &tf ,
const Vector &xi ,
const Scalar &smin ,
const Scalar &smax ,
Scalar &scur ,
const Vector &eabs ,
const Scalar &erel ,
Vector &ef ,
Vector &maxabs,
size_t &nstep )
{
// check simple vector class specifications
CheckSimpleVector<Scalar, Vector>();
size_t n = xi.size();
CppADUsageError(
smin <= smax,
"Error in OdeErrControl: smin > smax"
);
CppADUsageError(
eabs.size() == n,
"Error in OdeErrControl: size of eabs is not equal to n"
);
CppADUsageError(
maxabs.size() == n,
"Error in OdeErrControl: size of maxabs is not equal to n"
);
size_t m = method.order();
CppADUsageError(
m > 1,
"Error in OdeErrControl: m is less than or equal one"
);
size_t i;
Vector xa(n), xb(n), eb(n);
// initialization
Scalar zero(0);
Scalar one(1);
Scalar two(2);
Scalar three(3);
Scalar m1(m-1);
Scalar ta = ti;
for(i = 0; i < n; i++)
{ ef[i] = zero;
xa[i] = xi[i];
if( zero <= xi[i] )
maxabs[i] = xi[i];
else maxabs[i] = - xi[i];
}
nstep = 0;
Scalar tb, step, lambda, axbi, a, r, root;
while( ! (ta == tf) )
{ // start with value suggested by error criteria
step = scur;
// check maximum
if( smax <= step )
step = smax;
// check minimum
if( step <= smin )
step = smin;
// check if near the end
if( tf <= ta + step * three / two )
tb = tf;
else tb = ta + step;
// try using this step size
nstep++;
method.step(ta, tb, xa, xb, eb);
step = tb - ta;
// compute value of lambda for this step
lambda = Scalar(10) * scur / step;
for(i = 0; i < n; i++)
{ if( zero <= xb[i] )
axbi = xb[i];
else axbi = - xb[i];
a = eabs[i] + erel * axbi;
if( ! (eb[i] == zero) )
{ r = ( a / eb[i] ) * step / (tf - ti);
root = exp( log(r) / m1 );
if( root <= lambda )
lambda = root;
}
}
if( one <= lambda || step <= smin * three / two )
{ // this step is within error limits or
// close to the minimum size
ta = tb;
for(i = 0; i < n; i++)
{ xa[i] = xb[i];
ef[i] = ef[i] + eb[i];
if( zero <= xb[i] )
axbi = xb[i];
else axbi = - xb[i];
if( axbi > maxabs[i] )
maxabs[i] = axbi;
}
}
// step suggested by error criteria
// do not use last step becasue it may be very small
if( ! (ta == tf) )
scur = lambda * step / two;
}
return xa;
}
void
channelSorted(Home home, const IntVarArgs& x, SetVar y) {
if (home.failed()) return;
ViewArray<IntView> xa(home,x);
GECODE_ES_FAIL(Set::Int::Match<Set::SetView>::post(home,y,xa));
}
void
FMultiGrid::ABecCoeff::set_coeffs (MGT_Solver & mgt_solver, FMultiGrid& fmg)
{
BL_ASSERT( fmg.m_baselevel >= 0 );
BL_ASSERT( fmg.m_baselevel == 0 || fmg.m_crse_ratio != IntVect::TheZeroVector() );
Array< Array<Real> > xa(fmg.m_nlevels);
Array< Array<Real> > xb(fmg.m_nlevels);
for (int lev=0; lev < fmg.m_nlevels; ++lev) {
xa[lev].resize(BL_SPACEDIM);
xb[lev].resize(BL_SPACEDIM);
if (lev + fmg.m_baselevel == 0) {
// For level 0, the boundary lives exactly on the faces
for (int n=0; n<BL_SPACEDIM; n++) {
xa[lev][n] = 0.0;
xb[lev][n] = 0.0;
}
} else if (lev == 0) {
const Real* dx = fmg.m_geom[0].CellSize();
for (int n=0; n<BL_SPACEDIM; n++) {
xa[lev][n] = 0.5 * fmg.m_crse_ratio[n] * dx[n];
xb[lev][n] = 0.5 * fmg.m_crse_ratio[n] * dx[n];
}
} else {
const Real* dx_crse = fmg.m_geom[lev-1].CellSize();
for (int n=0; n<BL_SPACEDIM; n++) {
xa[lev][n] = 0.5 * dx_crse[n];
xb[lev][n] = 0.5 * dx_crse[n];
}
}
}
switch (eq_type) {
case const_gravity_eq:
{
mgt_solver.set_const_gravity_coeffs(xa, xb);
break;
}
case (gravity_eq):
{
BL_ASSERT(coeffs_set);
mgt_solver.set_gravity_coefficients(b, xa, xb);
break;
}
case (macproj_eq):
{
BL_ASSERT(coeffs_set);
mgt_solver.set_mac_coefficients(b, xa, xb);
break;
}
case (general_eq):
{
BL_ASSERT(scalars_set && coeffs_set);
mgt_solver.set_abeclap_coeffs(alpha, a, beta, b, xa, xb);
break;
}
default:
{
BoxLib::Abort("FMultiGrid::ABecCoeff::set_coeffs: How did we get here?");
}
}
}
void AcadoOCPInternal::evaluate(int nfdir, int nadir){
// Initial constraint function
if(!rfcn_.f_.isNull()){
const Matrix<double>& lbr = input(ACADO_LBR);
ACADO::Vector lb(lbr.size(),&lbr.front());
const Matrix<double>& ubr = input(ACADO_UBR);
ACADO::Vector ub(ubr.size(),&ubr.front());
ocp_->subjectTo( ACADO::AT_START, lb <= (*rfcn_.fcn_)(*arg_) <= ub);
}
// Path constraint function
if(!cfcn_.f_.isNull()){
const Matrix<double>& lbc = input(ACADO_LBC);
ACADO::Vector lb(lbc.size(),&lbc.front());
const Matrix<double>& ubc = input(ACADO_UBC);
ACADO::Vector ub(ubc.size(),&ubc.front());
ocp_->subjectTo( lb <= (*cfcn_.fcn_)(*arg_) <= ub );
}
// State bounds
Matrix<double> &lbx = input(ACADO_LBX);
Matrix<double> &ubx = input(ACADO_UBX);
for(int i=0; i<nxd_; ++i)
ocp_->subjectTo( lbx.at(i) <= xd_[i] <= ubx.at(i) );
for(int i=nxd_; i<nx_; ++i)
ocp_->subjectTo( lbx.at(i) <= xa_[i-nxd_] <= ubx.at(i) );
// Pass bounds on state at initial time
Matrix<double> &lbx0 = input(ACADO_LBX0);
Matrix<double> &ubx0 = input(ACADO_UBX0);
for(int i=0; i<nxd_; ++i)
ocp_->subjectTo( ACADO::AT_START, lbx0.at(i) <= xd_[i] <= ubx0.at(i) );
for(int i=nxd_; i<nx_; ++i)
ocp_->subjectTo( ACADO::AT_START, lbx0.at(i) <= xa_[i-nxd_] <= ubx0.at(i) );
// ocp_->subjectTo( AT_END , xd_[1] == 0.0 );
// ocp_->subjectTo( AT_END , xd_[2] == 0.0 );
// Control bounds
Matrix<double> &lbu = input(ACADO_LBU);
Matrix<double> &ubu = input(ACADO_UBU);
for(int i=0; i<nu_; ++i)
ocp_->subjectTo( lbu.at(i) <= u_[i] <= ubu.at(i) );
// Parameter bounds
Matrix<double> &lbp = input(ACADO_LBP);
Matrix<double> &ubp = input(ACADO_UBP);
for(int i=0; i<np_; ++i)
ocp_->subjectTo( lbp.at(i) <= p_[i] <= ubp.at(i) );
// Periodic boundary condition
if(hasSetOption("periodic_bounds")){
const vector<int>& periodic = getOption("periodic_bounds");
if(periodic.size()!=nx_) throw CasadiException("wrong dimension for periodic_bounds");
for(int i=0; i<nxd_; ++i)
if(periodic[i])
ocp_->subjectTo( 0.0, xd_[i], -xd_[i], 0.0);
for(int i=nxd_; i<nx_; ++i)
if(periodic[i])
ocp_->subjectTo( 0.0, xa_[i-nxd_], -xa_[i-nxd_], 0.0);
}
algorithm_ = new ACADO::OptimizationAlgorithm(*ocp_);
// set print level
ACADO::PrintLevel printlevel;
if(getOption("print_level")=="none") printlevel = ACADO::NONE;
else if(getOption("print_level")=="low") printlevel = ACADO::LOW;
else if(getOption("print_level")=="medium") printlevel = ACADO::MEDIUM;
else if(getOption("print_level")=="high") printlevel = ACADO::HIGH;
else if(getOption("print_level")=="debug") printlevel = ACADO::DEBUG;
else throw CasadiException("Illegal print level. Allowed are \"none\", \"low\", \"medium\", \"high\", \"debug\"");
algorithm_->set(ACADO::INTEGRATOR_PRINTLEVEL, printlevel );
// Set integrator
if(hasSetOption("integrator")){
GenericType integ = getOption("integrator");
ACADO::IntegratorType itype;
if(integ=="rk4") itype=ACADO::INT_RK4;
else if(integ=="rk12") itype=ACADO::INT_RK12;
else if(integ=="rk23") itype=ACADO::INT_RK23;
else if(integ=="rk45") itype=ACADO::INT_RK45;
else if(integ=="rk78") itype=ACADO::INT_RK78;
else if(integ=="bdf") itype=ACADO::INT_BDF;
else if(integ=="discrete") itype=ACADO::INT_DISCRETE;
else if(integ=="unknown") itype=ACADO::INT_UNKNOWN;
#ifdef ACADO_HAS_USERDEF_INTEGRATOR
else if(integ=="casadi"){
if(ACADO::Integrator::integrator_creator_ || ACADO::Integrator::integrator_user_data_)
throw CasadiException("AcadoOCPInternal::AcadoOCPInternal: An instance already exists");
if(integrators_.size() <= n_nodes_)
throw CasadiException("AcadoOCPInternal::AcadoOCPInternal: Number of integrators does not match number of shooting nodes");
ACADO::Integrator::integrator_creator_ = &AcadoIntegratorBackend::create;
ACADO::Integrator::integrator_user_data_ = this;
itype=ACADO::INT_UNKNOWN;
}
#endif
else throw CasadiException("AcadoOCPInternal::evaluate: no such integrator: " + integ.toString());
algorithm_->set(ACADO::INTEGRATOR_TYPE, itype);
};
// Set integrator tolerance
if(hasSetOption("integrator_tolerance")) algorithm_->set( ACADO::INTEGRATOR_TOLERANCE, getOption("integrator_tolerance").toDouble());
if(hasSetOption("absolute_tolerance")) algorithm_->set( ACADO::ABSOLUTE_TOLERANCE, getOption("absolute_tolerance").toDouble());
if(hasSetOption("kkt_tolerance")) algorithm_->set( ACADO::KKT_TOLERANCE, getOption("kkt_tolerance").toDouble());
if(hasSetOption("max_num_iterations")) algorithm_->set( ACADO::MAX_NUM_ITERATIONS, getOption("max_num_iterations").toInt() );
if(hasSetOption("max_num_integrator_steps")) algorithm_->set( ACADO::MAX_NUM_INTEGRATOR_STEPS, getOption("max_num_integrator_steps").toInt() );
if(hasSetOption("relaxation_parameter")) algorithm_->set( ACADO::RELAXATION_PARAMETER, getOption("relaxation_parameter").toDouble());
if(hasSetOption("dynamic_sensitivity")){
if(getOption("dynamic_sensitivity") == "forward_sensitivities")
algorithm_->set( ACADO::DYNAMIC_SENSITIVITY, ACADO::FORWARD_SENSITIVITY );
else if(getOption("dynamic_sensitivity") == "backward_sensitivities")
algorithm_->set( ACADO::DYNAMIC_SENSITIVITY, ACADO::BACKWARD_SENSITIVITY );
else
throw CasadiException("illegal dynamic_sensitivity");
}
if(hasSetOption("hessian_approximation")){
int hess;
GenericType op = getOption("hessian_approximation");
if(op=="exact_hessian") hess = ACADO::EXACT_HESSIAN;
else if(op == "constant_hessian") hess = ACADO::CONSTANT_HESSIAN;
else if(op == "full_bfgs_update") hess = ACADO::FULL_BFGS_UPDATE;
else if(op == "block_bfgs_update") hess = ACADO::BLOCK_BFGS_UPDATE;
else if(op == "gauss_newton") hess = ACADO::GAUSS_NEWTON;
else if(op == "gauss_newton_with_block_bfgs") hess = ACADO::GAUSS_NEWTON_WITH_BLOCK_BFGS;
else throw CasadiException("illegal hessian approximation");
algorithm_->set( ACADO::HESSIAN_APPROXIMATION, hess);
}
// should the states be initialized by a forward integration?
bool auto_init = getOption("auto_init").toInt();
// Initialize differential states
if(nxd_>0){
// Initial guess
Matrix<double> &x0 = input(ACADO_X_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid xd(nxd_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(nxd_,&x0.at(i*nx_));
xd.setVector(i,v);
}
// Pass to acado
algorithm_->initializeDifferentialStates(xd,auto_init ? ACADO::BT_TRUE : ACADO::BT_FALSE);
}
// Initialize algebraic states
if(nxa_>0){
// Initial guess
Matrix<double> &x0 = input(ACADO_X_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid xa(nxa_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(nxa_,&x0.at(i*nx_+nxd_));
xa.setVector(i,v);
}
// Pass to acado
algorithm_->initializeAlgebraicStates(xa,auto_init ? ACADO::BT_TRUE : ACADO::BT_FALSE);
}
// Initialize controls
if(nu_>0){
// Initial guess
Matrix<double> &u0 = input(ACADO_U_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid u(nu_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(nu_,&u0.at(i*nu_));
u.setVector(i,v);
}
// Pass to acado
algorithm_->initializeControls(u);
}
// Initialize parameters
if(np_>0){
// Initial guess
Matrix<double> &p0 = input(ACADO_P_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid p(np_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(np_,&p0.front()); // NB!
p.setVector(i,v);
}
// Pass to acado
algorithm_->initializeParameters(p);
}
// Solve
algorithm_->solve();
// Get the optimal state trajectory
if(nxd_>0){
Matrix<double> &xopt = output(ACADO_X_OPT);
ACADO::VariablesGrid xd;
algorithm_->getDifferentialStates(xd);
assert(xd.getNumPoints()==n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
// Copy to result
ACADO::Vector v = xd.getVector(i);
&xopt.at(i*nx_) << v;
}
}
if(nxa_>0){
Matrix<double> &xopt = output(ACADO_X_OPT);
ACADO::VariablesGrid xa;
algorithm_->getAlgebraicStates(xa);
assert(xa.getNumPoints()==n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
// Copy to result
ACADO::Vector v = xa.getVector(i);
&xopt.at(i*nx_ + nxd_) << v;
}
}
// Get the optimal control trajectory
if(nu_>0){
Matrix<double> &uopt = output(ACADO_U_OPT);
ACADO::VariablesGrid u;
algorithm_->getControls(u);
assert(u.getNumPoints()==n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
// Copy to result
ACADO::Vector v = u.getVector(i);
&uopt.at(i*nu_) << v;
}
}
// Get the optimal parameters
if(np_>0){
Matrix<double> &popt = output(ACADO_P_OPT);
ACADO::Vector p;
algorithm_->getParameters(p);
&popt.front() << p;
}
// Get the optimal cost
double cost = algorithm_->getObjectiveValue();
output(ACADO_COST).set(cost);
}
/*************************************************************************
Dense solver.
This subroutine solves a system A*X=B, where A is NxN non-denegerate
real matrix, X and B are NxM real matrices.
Additional features include:
* automatic detection of degenerate cases
* iterative improvement
INPUT PARAMETERS
A - array[0..N-1,0..N-1], system matrix
N - size of A
B - array[0..N-1,0..M-1], right part
M - size of right part
OUTPUT PARAMETERS
Info - return code:
* -3 if A is singular, or VERY close to singular.
X is filled by zeros in such cases.
* -1 if N<=0 or M<=0 was passed
* 1 if task is solved (matrix A may be near singular,
check R1/RInf parameters for condition numbers).
Rep - solver report, see below for more info
X - array[0..N-1,0..M-1], it contains:
* solution of A*X=B if A is non-singular (well-conditioned
or ill-conditioned, but not very close to singular)
* zeros, if A is singular or VERY close to singular
(in this case Info=-3).
SOLVER REPORT
Subroutine sets following fields of the Rep structure:
* R1 reciprocal of condition number: 1/cond(A), 1-norm.
* RInf reciprocal of condition number: 1/cond(A), inf-norm.
SEE ALSO:
DenseSolverR() - solves A*x = b, where x and b are Nx1 matrices.
-- ALGLIB --
Copyright 24.08.2009 by Bochkanov Sergey
*************************************************************************/
void rmatrixsolvem(const ap::real_2d_array& a,
int n,
const ap::real_2d_array& b,
int m,
int& info,
densesolverreport& rep,
ap::real_2d_array& x)
{
int i;
int j;
int k;
int rfs;
int nrfs;
ap::integer_1d_array p;
ap::real_1d_array xc;
ap::real_1d_array y;
ap::real_1d_array bc;
ap::real_1d_array xa;
ap::real_1d_array xb;
ap::real_1d_array tx;
ap::real_2d_array da;
double v;
double verr;
bool smallerr;
bool terminatenexttime;
//
// prepare: check inputs, allocate space...
//
if( n<=0||m<=0 )
{
info = -1;
return;
}
da.setlength(n, n);
x.setlength(n, m);
y.setlength(n);
xc.setlength(n);
bc.setlength(n);
tx.setlength(n+1);
xa.setlength(n+1);
xb.setlength(n+1);
//
// factorize matrix, test for exact/near singularity
//
for(i = 0; i <= n-1; i++)
{
ap::vmove(&da(i, 0), &a(i, 0), ap::vlen(0,n-1));
}
rmatrixlu(da, n, n, p);
rep.r1 = rmatrixlurcond1(da, n);
rep.rinf = rmatrixlurcondinf(da, n);
if( ap::fp_less(rep.r1,10*ap::machineepsilon)||ap::fp_less(rep.rinf,10*ap::machineepsilon) )
{
for(i = 0; i <= n-1; i++)
{
for(j = 0; j <= m-1; j++)
{
x(i,j) = 0;
}
}
rep.r1 = 0;
rep.rinf = 0;
info = -3;
return;
}
info = 1;
//
// solve
//
for(k = 0; k <= m-1; k++)
{
//
// First, non-iterative part of solution process:
// * pivots
// * L*y = b
// * U*x = y
//
ap::vmove(bc.getvector(0, n-1), b.getcolumn(k, 0, n-1));
for(i = 0; i <= n-1; i++)
{
if( p(i)!=i )
{
v = bc(i);
bc(i) = bc(p(i));
bc(p(i)) = v;
}
}
y(0) = bc(0);
for(i = 1; i <= n-1; i++)
{
v = ap::vdotproduct(&da(i, 0), &y(0), ap::vlen(0,i-1));
y(i) = bc(i)-v;
}
xc(n-1) = y(n-1)/da(n-1,n-1);
for(i = n-2; i >= 0; i--)
{
v = ap::vdotproduct(&da(i, i+1), &xc(i+1), ap::vlen(i+1,n-1));
xc(i) = (y(i)-v)/da(i,i);
}
//
// Iterative improvement of xc:
// * calculate r = bc-A*xc using extra-precise dot product
// * solve A*y = r
// * update x:=x+r
//
// This cycle is executed until one of two things happens:
// 1. maximum number of iterations reached
// 2. last iteration decreased error to the lower limit
//
nrfs = densesolverrfsmax(n, rep.r1, rep.rinf);
terminatenexttime = false;
for(rfs = 0; rfs <= nrfs-1; rfs++)
{
if( terminatenexttime )
{
break;
}
//
// generate right part
//
smallerr = true;
for(i = 0; i <= n-1; i++)
{
ap::vmove(&xa(0), &a(i, 0), ap::vlen(0,n-1));
xa(n) = -1;
ap::vmove(&xb(0), &xc(0), ap::vlen(0,n-1));
xb(n) = b(i,k);
xdot(xa, xb, n+1, tx, v, verr);
bc(i) = -v;
smallerr = smallerr&&ap::fp_less(fabs(v),4*verr);
}
if( smallerr )
{
terminatenexttime = true;
}
//
// solve
//
for(i = 0; i <= n-1; i++)
{
if( p(i)!=i )
{
v = bc(i);
bc(i) = bc(p(i));
bc(p(i)) = v;
}
}
y(0) = bc(0);
for(i = 1; i <= n-1; i++)
{
v = ap::vdotproduct(&da(i, 0), &y(0), ap::vlen(0,i-1));
y(i) = bc(i)-v;
}
tx(n-1) = y(n-1)/da(n-1,n-1);
for(i = n-2; i >= 0; i--)
{
v = ap::vdotproduct(&da(i, i+1), &tx(i+1), ap::vlen(i+1,n-1));
tx(i) = (y(i)-v)/da(i,i);
}
//
// update
//
ap::vadd(&xc(0), &tx(0), ap::vlen(0,n-1));
}
//
// Store xc
//
ap::vmove(x.getcolumn(k, 0, n-1), xc.getvector(0, n-1));
}
}