void PGSSolver::solve(const MatrixX& _A, const VectorX& _B, VectorX& _x, int _interationCount) const
{
int size = _B.getSize();
//initialize x
_x.setSize(size);
_x.set(0);
//make the required number of iteration
for (int iteration = 0; iteration < _interationCount; ++iteration)
{
//loop through each element of x
for (int i = 0; i < size; ++i)
{
//compute a(i, j) * x(j)
float ax = 0;
for (int j = 0; j < size; ++j)
ax += _A(i, j) * _x(j);
//compute x(i) = (b(i) - ax) / a(i, i)
float res = (_B(i) - ax) / _A(i, i);
_x(i, res);
}
}
}
void AttachmentConstraint::PBDProject(VectorX& x, const SparseMatrix& inv_mass, unsigned int ns)
{
// LOOK
ScalarType k_prime = 1 - std::pow(1-*(m_p_pbd_stiffness), 1.0/ns);
EigenVector3 p = x.block_vector(m_p0);
EigenVector3 dp = m_fixd_point-p;
x.block_vector(m_p0) += k_prime * dp;
}
template <typename PointSource, typename PointTarget, typename MatScalar> inline void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::estimateRigidTransformation (
const pcl::PointCloud<PointSource> &cloud_src,
const std::vector<int> &indices_src,
const pcl::PointCloud<PointTarget> &cloud_tgt,
const std::vector<int> &indices_tgt,
Matrix4 &transformation_matrix) const
{
if (indices_src.size () != indices_tgt.size ())
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] Number or points in source (%lu) differs than target (%lu)!\n", indices_src.size (), indices_tgt.size ());
return;
}
if (indices_src.size () < 4) // need at least 4 samples
{
PCL_ERROR ("[pcl::IterativeClosestPointNonLinear::estimateRigidTransformationLM] ");
PCL_ERROR ("Need at least 4 points to estimate a transform! Source and target have %lu points!",
indices_src.size ());
return;
}
int n_unknowns = warp_point_->getDimension (); // get dimension of unknown space
VectorX x (n_unknowns);
x.setConstant (n_unknowns, 0);
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
tmp_idx_src_ = &indices_src;
tmp_idx_tgt_ = &indices_tgt;
OptimizationFunctorWithIndices functor (static_cast<int> (indices_src.size ()), this);
Eigen::NumericalDiff<OptimizationFunctorWithIndices> num_diff (functor);
//Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctorWithIndices> > lm (num_diff);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctorWithIndices>, MatScalar> lm (num_diff);
int info = lm.minimize (x);
// Compute the norm of the residuals
PCL_DEBUG ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] LM solver finished with exit code %i, having a residual norm of %g. \n", info, lm.fvec.norm ());
PCL_DEBUG ("Final solution: [%f", x[0]);
for (int i = 1; i < n_unknowns; ++i)
PCL_DEBUG (" %f", x[i]);
PCL_DEBUG ("]\n");
// Return the correct transformation
warp_point_->setParam (x);
transformation_matrix = warp_point_->getTransform ();
tmp_src_ = NULL;
tmp_tgt_ = NULL;
tmp_idx_src_ = tmp_idx_tgt_ = NULL;
}
bool BCCoreSiconos::callSolver(MatrixX& Mlcp, VectorX& b, VectorX& solution, VectorX& contactIndexToMu, ofstream& os)
{
#ifdef BUILD_BCPLUGIN_WITH_SICONOS
int NC3 = Mlcp.rows();
if(NC3<=0) return true;
int NC = NC3/3;
int CFS_DEBUG = 0;
int CFS_DEBUG_VERBOSE = 0;
if(CFS_DEBUG)
{
if(NC3%3 != 0 ){ os << " warning-1 " << std::endl;return false;}
if( b.rows()!= NC3){ os << " warning-2 " << std::endl;return false;}
if(solution.rows()!= NC3){ os << " warning-3 " << std::endl;return false;}
}
for(int ia=0;ia<NC;ia++)for(int i=0;i<3;i++)prob->q [3*ia+i]= b(((i==0)?(ia):(2*ia+i+NC-1)));
for(int ia=0;ia<NC;ia++) prob->mu[ ia ]= contactIndexToMu[ia];
prob->numberOfContacts = NC;
if( USE_FULL_MATRIX )
{
prob->M->storageType = 0;
prob->M->size0 = NC3;
prob->M->size1 = NC3;
double* ptmp = prob->M->matrix0 ;
for(int ia=0;ia<NC;ia++)for(int i =0;i <3 ;i ++)
{
for(int ja=0;ja<NC;ja++)for(int j =0;j <3;j ++)
{
ptmp[NC3*(3*ia+i)+(3*ja+j)]=Mlcp(((i==0)?(ia):(2*ia+i+NC-1)),((j==0)?(ja):(2*ja+j+NC-1)));
}
}
}
else
{
prob->M->storageType = 1;
prob->M->size0 = NC3;
prob->M->size1 = NC3;
sparsify_A( prob->M->matrix1 , Mlcp , NC , &os);
}
fc3d_driver(prob,reaction,velocity,solops, numops);
double* prea = reaction ;
for(int ia=0;ia<NC;ia++)for(int i=0;i<3;i++) solution(((i==0)?(ia):(2*ia+i+NC-1))) = prea[3*ia+i] ;
if(CFS_DEBUG_VERBOSE)
{
os << "=---------------------------------="<< std::endl;
os << "| res_error =" << solops->dparam[1] << std::endl;
os << "=---------------------------------="<< std::endl;
}
#endif
return true;
}
template <typename PointSource, typename PointTarget, typename MatScalar> void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::estimateRigidTransformation (
const pcl::PointCloud<PointSource> &cloud_src,
const pcl::PointCloud<PointTarget> &cloud_tgt,
Matrix4 &transformation_matrix) const
{
// <cloud_src,cloud_src> is the source dataset
if (cloud_src.points.size () != cloud_tgt.points.size ())
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] ");
PCL_ERROR ("Number or points in source (%lu) differs than target (%lu)!\n",
cloud_src.points.size (), cloud_tgt.points.size ());
return;
}
if (cloud_src.points.size () < 4) // need at least 4 samples
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] ");
PCL_ERROR ("Need at least 4 points to estimate a transform! Source and target have %lu points!\n",
cloud_src.points.size ());
return;
}
int n_unknowns = warp_point_->getDimension ();
VectorX x (n_unknowns);
x.setZero ();
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
OptimizationFunctor functor (static_cast<int> (cloud_src.points.size ()), this);
Eigen::NumericalDiff<OptimizationFunctor> num_diff (functor);
//Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, double> lm (num_diff);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, MatScalar> lm (num_diff);
int info = lm.minimize (x);
// Compute the norm of the residuals
PCL_DEBUG ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation]");
PCL_DEBUG ("LM solver finished with exit code %i, having a residual norm of %g. \n", info, lm.fvec.norm ());
PCL_DEBUG ("Final solution: [%f", x[0]);
for (int i = 1; i < n_unknowns; ++i)
PCL_DEBUG (" %f", x[i]);
PCL_DEBUG ("]\n");
// Return the correct transformation
warp_point_->setParam (x);
transformation_matrix = warp_point_->getTransform ();
tmp_src_ = NULL;
tmp_tgt_ = NULL;
}
// sping gradient: k*(current_length-rest_length)*current_direction;
void SpringConstraint::EvaluateGradient(const VectorX& x, VectorX& gradient)
{
// TODO
//EigenVector3 g_i = (*(m_p_stiffness))*(x.block_vector(m_p0) - m_fixd_point);
//gradient.block_vector(m_p0) += g_i;
EigenVector3 p1=x.block_vector(m_p1);
EigenVector3 p2=x.block_vector(m_p2);
float currentLength=(p1-p2).norm();
float force=(*this->m_p_stiffness)*(currentLength-this->m_rest_length);
EigenVector3 n1=p1-p2,n2=p2-p1;
n1.normalize();n2.normalize();
gradient.block_vector(m_p1)+=n1*force;
gradient.block_vector(m_p2)+=n2*force;
}
void VelocityTrackingObjective<Scalar>::setVelRefInWorldFrame(const VectorX& velRefInWorldFrame)
{
assert(velRefInWorldFrame.size()==model_.getNbSamples());
assert(velRefInWorldFrame==velRefInWorldFrame);
velRefInWorldFrame_ = velRefInWorldFrame;
}
GMMExpectationMaximization::Real GMMExpectationMaximization::gauss(const VectorX & mean,
const MatrixX & cov,const VectorX & pt) const
{
Real det = cov.determinant();
uint dim = mean.size();
// check that the covariance matrix is invertible
if (std::abs(det) < std::pow(m_epsilon,dim) * 0.1)
return 0.0; // the gaussian has approximately zero width: the probability of any point falling into it is approximately 0.
// else, compute pdf
MatrixX inverse_cov = cov.inverse();
VectorX dist = pt - mean;
Real exp = - (dist.dot(inverse_cov * dist)) / 2.0;
Real den = std::sqrt(std::pow(2.0 * M_PI,dim) * std::abs(det));
return std::exp(exp) / den;
}
void TrajectoryWalkgen<Scalar>::setState(const VectorX& state)
{
assert(state==state);
assert(state.size()==3);
noDynModel_.setState(state);
}
void Simulation::computeForces(const VectorX& x, VectorX& force)
{
VectorX gradient;
gradient.resize(m_mesh->m_system_dimension);
gradient.setZero();
// springs
for (std::vector<Constraint*>::iterator it = m_constraints.begin(); it != m_constraints.end(); ++it)
{
(*it)->EvaluateGradient(x, gradient);
}
// external forces
gradient -= m_external_force;
force = -gradient;
}
void MotionConstraint<Scalar>::computeFunction(const VectorX& x0, VectorX& func,
Scalar velLimit, Scalar accLimit)
{
int N = model_.getNbSamples();
const LinearDynamic<Scalar>& dynBaseVel = model_.getVelLinearDynamic();
const LinearDynamic<Scalar>& dynBaseAcc = model_.getAccLinearDynamic();
tmp_.fill(velLimit);
tmp2_.fill(accLimit);
func.noalias() = -getGradient()*x0;
func.segment(0, N).noalias() += tmp_;
func.segment(0, N).noalias() -= dynBaseVel.S * model_.getState();
func.segment(N, N).noalias() += tmp2_;
func.segment(N, N).noalias() -= dynBaseAcc.S * model_.getState();
}
void SpringConstraint::PBDProject(VectorX& x, const SparseMatrix& inv_mass, unsigned int ns)
{
// TODO
// change project order
ScalarType k_prime = 1 - std::pow(1-*(m_p_pbd_stiffness), 1.0/ns);
float rest_length=m_rest_length;
EigenVector3 p1 = x.block_vector(m_p1);
EigenVector3 p2 = x.block_vector(m_p2);
float current_length=(p1-p2).norm();
EigenVector3 current_direction=(p1-p2)/current_length;
EigenVector3 dp=(current_length-rest_length)*current_direction;
ScalarType w1=inv_mass.coeff(m_p1,m_p1);
ScalarType w2=inv_mass.coeff(m_p2,m_p2);
x.block_vector(m_p1) -= k_prime * dp*w1/(w1+w2);
x.block_vector(m_p2) += k_prime * dp*w2/(w1+w2);
}
const MatrixX& Jacobian::GetNullspace()
{
if(computeNullSpace_)
{
computeNullSpace_ = false;
/*jacobianInverseNoDls_ = jacobian_;
PseudoInverse(jacobianInverseNoDls_); // tmp while figuring out how to chose lambda*/
//ComputeSVD();
MatrixX id = MatrixX::Identity(jacobian_.cols(), jacobian_.cols());
ComputeSVD();
//Eigen::JacobiSVD<MatrixX> svd(jacobian_, Eigen::ComputeThinU | Eigen::ComputeThinV);
MatrixX res = MatrixX::Zero(id.rows(), id.cols());
for(int i =0; i < svd_.matrixV().cols(); ++ i)
{
VectorX v = svd_.matrixV().col(i);
res += v * v.transpose();
}
Identitymin_ = id - res;
//Identitymin_ = id - (jacobianInverseNoDls_* jacobian_);
}
return Identitymin_;
}
bool Solver_LP_abstract::readLpFromFile(const std::string& filename,
VectorX &c, VectorX &lb, VectorX &ub,
MatrixXX &A, VectorX &Alb, VectorX &Aub)
{
std::ifstream in(filename.c_str(), std::ios::in | std::ios::binary);
typename MatrixXX::Index n=0, m=0;
in.read((char*) (&n),sizeof(typename MatrixXX::Index));
in.read((char*) (&m),sizeof(typename MatrixXX::Index));
c.resize(n);
lb.resize(n);
ub.resize(n);
A.resize(m,n);
Alb.resize(m);
Aub.resize(m);
in.read( (char *) c.data() , n*sizeof(typename MatrixXX::Scalar) );
in.read( (char *) lb.data() , n*sizeof(typename MatrixXX::Scalar) );
in.read( (char *) ub.data() , n*sizeof(typename MatrixXX::Scalar) );
in.read( (char *) A.data() , m*n*sizeof(typename MatrixXX::Scalar) );
in.read( (char *) Alb.data() , m*sizeof(typename MatrixXX::Scalar) );
in.read( (char *) Aub.data() , m*sizeof(typename MatrixXX::Scalar) );
in.close();
return true;
}
TYPED_TEST(TestSecondOrderMultinomialLogisticRegression, MinimizerOverfitSmall) {
MatrixX<TypeParam> X(2, 10);
VectorXi y(10);
X << 0.6097662 , 0.53395565, 0.9499446 , 0.67289898, 0.94173948,
0.56675891, 0.80363783, 0.85303565, 0.15903886, 0.99518533,
0.41655682, 0.29256121, 0.36103228, 0.29899503, 0.4957268 ,
-0.04277318, -0.28038614, -0.12334621, -0.17497722, 0.1492248;
y << 0, 0, 0, 0, 0, 1, 1, 1, 1, 1;
std::vector<MatrixX<TypeParam> > X_var;
for (int i=0; i<10; i++) {
//X_var.push_back(MatrixX<TypeParam>::Random(2, 2).array().abs() * 0.01);
//X_var.push_back(MatrixX<TypeParam>::Zero(2, 2));
VectorX<TypeParam> a = VectorX<TypeParam>::Random(2).array() * 0.01;
X_var.push_back(
a * a.transpose()
);
}
SecondOrderLogisticRegressionApproximation<TypeParam> mlr(X, X_var, y, 0);
MatrixX<TypeParam> eta = MatrixX<TypeParam>::Zero(2, 3);
GradientDescent<SecondOrderLogisticRegressionApproximation<TypeParam>, MatrixX<TypeParam>> minimizer(
std::make_shared<
ArmijoLineSearch<
SecondOrderLogisticRegressionApproximation<TypeParam>,
MatrixX<TypeParam>
>
>(),
[](TypeParam value, TypeParam gradNorm, size_t iterations) {
return iterations < 5000;
}
);
minimizer.minimize(mlr, eta);
EXPECT_GT(0.1, mlr.value(eta));
}
virtual void SetUp()
{
using namespace MPCWalkgen;
int nbSamples = 3;
Real samplingPeriod = 1.0;
bool autoCompute = true;
VectorX variable;
variable.setZero(2*nbSamples);
Real feedbackPeriod = 0.5;
vectorOfVector2 p(3);
p[0] = Vector2(1.0, 1.0);
p[1] = Vector2(-1.0, 1.0);
p[2] = Vector2(1.0, -1.0);
HumanoidFeetSupervisor<Real> feetSupervisor(nbSamples,
samplingPeriod);
feetSupervisor.setLeftFootCopConvexPolygon(ConvexPolygon<Real>(p));
feetSupervisor.setRightFootCopConvexPolygon(ConvexPolygon<Real>(p));
feetSupervisor.updateTimeline(variable, feedbackPeriod);
LIPModel<Real> lip(nbSamples, samplingPeriod, autoCompute);
lip.setFeedbackPeriod(feedbackPeriod);
HumanoidCopConstraint<Real> copCtr(lip, feetSupervisor);
VectorX x0 = VectorX::Zero(6);
function_ = copCtr.getFunction(x0);
gradient_ = copCtr.getGradient(x0.rows());
supBounds_ = copCtr.getSupBounds(x0);
infBounds_ = copCtr.getInfBounds(x0);
}
void HumanoidFootConstraint<Scalar>::computeBoundsVectors(const VectorX& x0)
{
int M = feetSupervisor_.getNbPreviewedSteps();
assert(x0.rows()==2*M);
supBound_.setConstant(2*M, Constant<Scalar>::MAXIMUM_BOUND_VALUE);
infBound_.setConstant(2*M, -Constant<Scalar>::MAXIMUM_BOUND_VALUE);
for(int i = 0; i<M; ++i)
{
const ConvexPolygon<Scalar>& cp = feetSupervisor_.getKinematicConvexPolygon(i);
supBound_(i) = cp.getXSupBound();
supBound_(i + M) = cp.getYSupBound();
infBound_(i) = cp.getXInfBound();
infBound_(i + M) = cp.getYInfBound();
// This if-else statement adds the required terms to vectors supBound_ and infBound_
// so that constraints are expressed in world frame
if(i==0)
{
supBound_(i) += feetSupervisor_.getSupportFootStateX()(0);
supBound_(i + M) += feetSupervisor_.getSupportFootStateY()(0);
infBound_(i) += feetSupervisor_.getSupportFootStateX()(0);
infBound_(i + M) += feetSupervisor_.getSupportFootStateY()(0);
}
else
{
supBound_(i) += x0(i - 1);
supBound_(i + M) += x0(M + i - 1);
infBound_(i) += x0(i - 1);
infBound_(i + M) += x0(M + i - 1);
}
}
// As we optimize a variation dX_ of the QP variable X_ (such that X_ = x0 + dX_),
// the bound constraints need to be translated of x0 too.
supBound_ -= x0;
infBound_ -= x0;
}
lbfgs::status lbfgs::cpu_lbfgs(float *h_x)
{
const size_t NX = m_costFunction.getNumberOfUnknowns();
floatdouble *d_x = new floatdouble[NX];
for (size_t idx = 0; idx < NX; ++idx)
d_x[idx] = h_x[idx];
VectorX xk = VectorX::Map(d_x, NX); // x_k, current solution
VectorX gk(NX); // g_k, gradient at x_k
VectorX gkm1(NX); // g_{k-1}, gradient at x_{k-1}
VectorX z(NX); // z, search direction
floatdouble fk; // f_k, value at x_k
floatdouble fkm1; // f_{k-1}, value at x_{k-1}
floatdouble H0 = 1.0f; // H_0, initial inverse Hessian (diagonal, same value for all elements)
// treat arrays as ring buffers!
VectorX s[HISTORY_SIZE]; // s, history of solution updates
VectorX y[HISTORY_SIZE]; // y, history of gradient updates
floatdouble alpha[HISTORY_SIZE]; // alpha, history of alphas (needed for z updates)
floatdouble rho [HISTORY_SIZE]; // rho, history of rhos (needed for z updates)
for (size_t i = 0; i < HISTORY_SIZE; ++i)
{
s[i] = VectorX(NX);
y[i] = VectorX(NX);
}
cpu_cost_function *cpucf = (cpu_cost_function*)&m_costFunction;
cpucf->cpu_f_gradf(xk.data(), &fk, gk.data());
size_t evals = 1;
status stat = LBFGS_REACHED_MAX_ITER;
#ifdef LBFGS_VERBOSE
std::cout << "lbfgs::cpu_lbfgs()" << std::endl;
#endif
size_t it;
for (it = 0; it < m_maxIter; ++it)
{
#ifdef LBFGS_VERBOSE
printf("f(x) = % 12e, ||grad||_2 = % 12e\n", fk, gk.norm());
#endif
// Check for convergence
// ---------------------
floatdouble xSquaredNorm = std::max<floatdouble>(1.0f, xk.squaredNorm());
if (gk.squaredNorm() < (m_gradientEps * m_gradientEps) * xSquaredNorm)
{
stat = LBFGS_BELOW_GRADIENT_EPS;
break;
}
// Find search direction
// ---------------------
z = -gk;
const size_t MAX_IDX = std::min<size_t>(it, HISTORY_SIZE);
for (size_t i = 1; i <= MAX_IDX; ++i)
{
const size_t idx = index(it - i);
alpha[idx] = s[idx].dot(z) * rho[idx];
z -= alpha[idx] * y[idx];
}
z *= H0;
for (size_t i = MAX_IDX; i > 0; --i)
{
const size_t idx = index(it - i);
const floatdouble beta = rho[idx] * y[idx].dot(z);
z += s[idx] * (alpha[idx] - beta);
}
// Perform backtracking line search
// --------------------------------
gkm1 = gk;
fkm1 = fk;
floatdouble step;
if (!cpu_linesearch(xk, z, cpucf, fk, gk, evals, gkm1, fkm1, stat, step, m_maxEvals))
{
break;
}
// Update s, y, rho and H_0
// ------------------------
s[index(it)] = z * step; // = x_k - x_{k-1}
y[index(it)] = gk - gkm1;
floatdouble yDotS = y[index(it)].dot(s[index(it)]);
rho[index(it)] = 1.0f / yDotS;
floatdouble yNorm2 = y[index(it)].squaredNorm();
if (yNorm2 > 1e-5f)
H0 = yDotS / yNorm2;
}
for (size_t i = 0; i < NX; ++i)
h_x[i] = float(xk[i]);
#ifdef LBFGS_VERBOSE
std::cout << "Number of iterations: " << it << std::endl;
std::cout << "Number of function/gradient evaluations: " << evals << std::endl;
std::cout << "Reason for termination: " << statusToString(stat) << std::endl;
#endif
return stat;
}
/**
* Return the norm of the error vector.
**/
virtual Scalar getErrorNorm() const {
return errorVector_.norm();
}
bool cpu_linesearch(VectorX &xk, VectorX &z,
cpu_cost_function *cpucf, floatdouble &fk, VectorX &gk,
size_t &evals, const VectorX &gkm1, const floatdouble &fkm1,
lbfgs::status &stat, floatdouble &step, size_t maxEvals)
{
const floatdouble c1 = 1e-4f;
const floatdouble c2 = 0.9f;
const floatdouble alpha_0 = 0.0;
const floatdouble phi_0 = fk;
const floatdouble phi_prime_0 = z.dot(gk);
if (phi_prime_0 >= 0.0)
{
stat = lbfgs::LBFGS_LINE_SEARCH_FAILED;
return false;
}
const floatdouble alpha_max = 1e8;
floatdouble alpha = 1.0;
floatdouble alpha_old = 0.0;
bool second_iter = false;
floatdouble alpha_low, alpha_high;
floatdouble phi_low, phi_high;
floatdouble phi_prime_low, phi_prime_high;
for (;;)
{
xk += (alpha - alpha_old) * z;
cpucf->cpu_f_gradf(xk.data(), &fk, gk.data());
++evals;
const floatdouble phi_alpha = fk;
const floatdouble phi_prime_alpha = z.dot(gk);
const bool armijo_violated = (phi_alpha > phi_0 + c1 * alpha * phi_prime_0 || (second_iter && phi_alpha >= phi_0));
const bool strong_wolfe = (std::abs(phi_prime_alpha) <= -c2 * phi_prime_0);
// If both Armijo and Strong Wolfe hold, we're done
if (!armijo_violated && strong_wolfe)
{
step = alpha;
return true;
}
if (evals >= maxEvals)
{
stat = lbfgs::LBFGS_REACHED_MAX_EVALS;
return false;
}
// If Armijio condition is violated, we've bracketed a viable minimum
// Interval is [alpha_0, alpha]
if (armijo_violated)
{
alpha_low = alpha_0;
alpha_high = alpha;
phi_low = phi_0;
phi_high = phi_alpha;
phi_prime_low = phi_prime_0;
phi_prime_high = phi_prime_alpha;
break;
}
// If the directional derivative at alpha is positive, we've bracketed a viable minimum
// Interval is [alpha, alpha_0]
if (phi_prime_alpha >= 0)
{
alpha_low = alpha;
alpha_high = alpha_0;
phi_low = phi_alpha;
phi_high = phi_0;
phi_prime_low = phi_prime_alpha;
phi_prime_high = phi_prime_0;
break;
}
// Else look to the "right" of alpha for a viable minimum
floatdouble alpha_new = alpha + 4 * (alpha - alpha_old);
alpha_old = alpha;
alpha = alpha_new;
if (alpha > alpha_max)
{
stat = lbfgs::LBFGS_LINE_SEARCH_FAILED;
return false;
}
second_iter = true;
}
// The minimum is now bracketed in [alpha_low, alpha_high]
// Find it...
size_t tries = 0;
const size_t minTries = 10;
while (true)
{
tries++;
alpha_old = alpha;
// Quadratic interpolation:
// Least-squares fit a parabola to (alpha_low, phi_low),
// (alpha_high, phi_high) with gradients phi_prime_low and
// phi_prime_high and select the minimum of that parabola as
// the new alpha
alpha = 0.5f * (alpha_low + alpha_high);
alpha += (phi_high - phi_low) / (phi_prime_low - phi_prime_high);
if (alpha < alpha_low || alpha > alpha_high)
alpha = 0.5f * (alpha_low + alpha_high);
xk += (alpha - alpha_old) * z;
cpucf->cpu_f_gradf(xk.data(), &fk, gk.data());
++evals;
const floatdouble phi_j = fk;
const floatdouble phi_prime_j = z.dot(gk);
const bool armijo_violated = (phi_j > phi_0 + c1 * alpha * phi_prime_0 || phi_j >= phi_low);
const bool strong_wolfe = (std::abs(phi_prime_j) <= -c2 * phi_prime_0);
if (!armijo_violated && strong_wolfe)
{
// The Armijo and Strong Wolfe conditions hold
step = alpha;
break;
}
else if (std::abs(alpha_high - alpha_low) < 1e-5 && tries > minTries)
{
// The search interval has become too small
stat = lbfgs::LBFGS_LINE_SEARCH_FAILED;
return false;
}
else if (armijo_violated)
{
alpha_high = alpha;
phi_high = phi_j;
phi_prime_high = phi_prime_j;
}
else
{
if (phi_prime_j * (alpha_high - alpha_low) >= 0)
{
alpha_high = alpha_low;
phi_high = phi_low;
phi_prime_high = phi_prime_low;
}
alpha_low = alpha;
phi_low = phi_j;
phi_prime_low = phi_prime_j;
}
if (evals >= maxEvals)
{
stat = lbfgs::LBFGS_REACHED_MAX_EVALS;
return false;
}
}
return true;
}
// attachment spring gradient: k*(current_length)*current_direction
void AttachmentConstraint::EvaluateGradient(const VectorX& x, VectorX& gradient)
{
// LOOK
EigenVector3 g_i = (*(m_p_stiffness))*(x.block_vector(m_p0) - m_fixd_point);
gradient.block_vector(m_p0) += g_i;
}
void TrajectoryWalkgen<Scalar>::setVelRefInWorldFrame(const VectorX& velRef)
{
assert(velRef==velRef);
assert(velRef.size()==noDynModel_.getNbSamples());
velTrackingObj_.setVelRefInWorldFrame(velRef);
}
void TrajectoryWalkgen<Scalar>::setPosRefInWorldFrame(const VectorX& posRef)
{
assert(posRef==posRef);
assert(posRef.size()==noDynModel_.getNbSamples());
posTrackingObj_.setPosRefInWorldFrame(posRef);
}
std::vector<double> Configurator::eigenToStdVector(const VectorX vec) {
return std::vector<double>(vec.data(), vec.data() + vec.size());
}
