void DenseMatrix<T>::_matvec_blas(T alpha, T beta,
DenseVector<T>& dest,
const DenseVector<T>& arg,
bool trans) const
{
// Ensure that dest and arg sizes are compatible
if (!trans)
{
// dest ~ A * arg
// (mx1) (mxn) * (nx1)
if ((dest.size() != this->m()) || (arg.size() != this->n()))
{
libMesh::out << "Improper input argument sizes!" << std::endl;
libmesh_error();
}
}
else // trans == true
{
// Ensure that dest and arg are proper size
// dest ~ A^T * arg
// (nx1) (nxm) * (mx1)
if ((dest.size() != this->n()) || (arg.size() != this->m()))
{
libMesh::out << "Improper input argument sizes!" << std::endl;
libmesh_error();
}
}
// Calling sequence for dgemv:
//
// dgemv(TRANS,M,N,ALPHA,A,LDA,X,INCX,BETA,Y,INCY)
// TRANS - CHARACTER*1, 't' for transpose, 'n' for non-transpose multiply
// We store everything in row-major order, so pass the transpose flag for
// non-transposed matvecs and the 'n' flag for transposed matvecs
char TRANS[] = "t";
if (trans)
TRANS[0] = 'n';
// M - INTEGER.
// On entry, M specifies the number of rows of the matrix A.
// In C/C++, pass the number of *cols* of A
int M = this->n();
// N - INTEGER.
// On entry, N specifies the number of columns of the matrix A.
// In C/C++, pass the number of *rows* of A
int N = this->m();
// ALPHA - DOUBLE PRECISION.
// The scalar constant passed to this function
// A - DOUBLE PRECISION array of DIMENSION ( LDA, n ).
// Before entry, the leading m by n part of the array A must
// contain the matrix of coefficients.
// The matrix, *this. Note that _matvec_blas is called from
// a const function, vector_mult(), and so we have made this function const
// as well. Since BLAS knows nothing about const, we have to cast it away
// now.
DenseMatrix<T>& a_ref = const_cast< DenseMatrix<T>& > ( *this );
std::vector<T>& a = a_ref.get_values();
// LDA - INTEGER.
// On entry, LDA specifies the first dimension of A as declared
// in the calling (sub) program. LDA must be at least
// max( 1, m ).
int LDA = M;
// X - DOUBLE PRECISION array of DIMENSION at least
// ( 1 + ( n - 1 )*abs( INCX ) ) when TRANS = 'N' or 'n'
// and at least
// ( 1 + ( m - 1 )*abs( INCX ) ) otherwise.
// Before entry, the incremented array X must contain the
// vector x.
// Here, we must cast away the const-ness of "arg" since BLAS knows
// nothing about const
DenseVector<T>& x_ref = const_cast< DenseVector<T>& > ( arg );
std::vector<T>& x = x_ref.get_values();
// INCX - INTEGER.
// On entry, INCX specifies the increment for the elements of
// X. INCX must not be zero.
int INCX = 1;
// BETA - DOUBLE PRECISION.
// On entry, BETA specifies the scalar beta. When BETA is
// supplied as zero then Y need not be set on input.
// The second scalar constant passed to this function
// Y - DOUBLE PRECISION array of DIMENSION at least
// ( 1 + ( m - 1 )*abs( INCY ) ) when TRANS = 'N' or 'n'
// and at least
// ( 1 + ( n - 1 )*abs( INCY ) ) otherwise.
// Before entry with BETA non-zero, the incremented array Y
// must contain the vector y. On exit, Y is overwritten by the
// updated vector y.
// The input vector "dest"
std::vector<T>& y = dest.get_values();
// INCY - INTEGER.
// On entry, INCY specifies the increment for the elements of
// Y. INCY must not be zero.
int INCY = 1;
// Finally, ready to call the BLAS function
BLASgemv_(TRANS, &M, &N, &alpha, &(a[0]), &LDA, &(x[0]), &INCX, &beta, &(y[0]), &INCY);
}
void DenseMatrix<T>::_svd_lapack (DenseVector<Real> & sigma,
DenseMatrix<Number> & U,
DenseMatrix<Number> & VT)
{
// The calling sequence for dgetrf is:
// DGESVD( JOBU, JOBVT, M, N, A, LDA, S, U, LDU, VT, LDVT, WORK, LWORK, INFO )
// JOBU (input) CHARACTER*1
// Specifies options for computing all or part of the matrix U:
// = 'A': all M columns of U are returned in array U:
// = 'S': the first min(m,n) columns of U (the left singular
// vectors) are returned in the array U;
// = 'O': the first min(m,n) columns of U (the left singular
// vectors) are overwritten on the array A;
// = 'N': no columns of U (no left singular vectors) are
// computed.
char JOBU = 'S';
// JOBVT (input) CHARACTER*1
// Specifies options for computing all or part of the matrix
// V**T:
// = 'A': all N rows of V**T are returned in the array VT;
// = 'S': the first min(m,n) rows of V**T (the right singular
// vectors) are returned in the array VT;
// = 'O': the first min(m,n) rows of V**T (the right singular
// vectors) are overwritten on the array A;
// = 'N': no rows of V**T (no right singular vectors) are
// computed.
char JOBVT = 'S';
// Note: Lapack is going to compute the singular values of A^T. If
// A=U * S * V^T, then A^T = V * S * U^T, which means that the
// values returned in the "U_val" array actually correspond to the
// entries of the V matrix, and the values returned in the VT_val
// array actually correspond to the entries of U^T. Therefore, we
// pass VT in the place of U and U in the place of VT below!
std::vector<Real> sigma_val;
int M = this->n();
int N = this->m();
int min_MN = (M < N) ? M : N;
// Size user-provided storage appropriately. Inside svd_helper:
// U_val is sized to (M x min_MN)
// VT_val is sized to (min_MN x N)
// So, we set up U to have the shape of "VT_val^T", and VT to
// have the shape of "U_val^T".
//
// Finally, since the results are stored in column-major order by
// Lapack, but we actually want the transpose of what Lapack
// returns, this means (conveniently) that we don't even have to
// copy anything after the call to _svd_helper, it should already be
// in the correct order!
U.resize(N, min_MN);
VT.resize(min_MN, M);
_svd_helper(JOBU, JOBVT, sigma_val, VT.get_values(), U.get_values());
// Copy the singular values into sigma.
sigma.resize(cast_int<unsigned int>(sigma_val.size()));
for (unsigned int i=0; i<sigma.size(); i++)
sigma(i) = sigma_val[i];
}
void DenseMatrix<T>::_svd_solve_lapack(const DenseVector<T> & rhs,
DenseVector<T> & x,
Real rcond) const
{
// Since BLAS is expecting column-major storage, we first need to
// make a transposed copy of *this, then pass it to the gelss
// routine instead of the original. This extra copy is kind of a
// bummer, it might be better if we could use the full SVD to
// compute the least-squares solution instead... Note that it isn't
// completely terrible either, since A_trans gets overwritten by
// Lapack, and we usually would end up making a copy of A outside
// the function call anyway.
DenseMatrix<T> A_trans;
this->get_transpose(A_trans);
// M is INTEGER
// The number of rows of the input matrix. M >= 0.
// This is actually the number of *columns* of A_trans.
int M = A_trans.n();
// N is INTEGER
// The number of columns of the matrix A. N >= 0.
// This is actually the number of *rows* of A_trans.
int N = A_trans.m();
// We'll use the min and max of (M,N) several times below.
int max_MN = std::max(M,N);
int min_MN = std::min(M,N);
// NRHS is INTEGER
// The number of right hand sides, i.e., the number of columns
// of the matrices B and X. NRHS >= 0.
// This could later be generalized to solve for multiple right-hand
// sides...
int NRHS = 1;
// A is DOUBLE PRECISION array, dimension (LDA,N)
// On entry, the M-by-N matrix A.
// On exit, the first min(m,n) rows of A are overwritten with
// its right singular vectors, stored rowwise.
//
// The data vector that will be passed to Lapack.
std::vector<T> & A_trans_vals = A_trans.get_values();
// LDA is INTEGER
// The leading dimension of the array A. LDA >= max(1,M).
int LDA = M;
// B is DOUBLE PRECISION array, dimension (LDB,NRHS)
// On entry, the M-by-NRHS right hand side matrix B.
// On exit, B is overwritten by the N-by-NRHS solution
// matrix X. If m >= n and RANK = n, the residual
// sum-of-squares for the solution in the i-th column is given
// by the sum of squares of elements n+1:m in that column.
//
// Since we don't want the user's rhs vector to be overwritten by
// the solution, we copy the rhs values into the solution vector "x"
// now. x needs to be long enough to hold both the (Nx1) solution
// vector or the (Mx1) rhs, so size it to the max of those.
x.resize(max_MN);
for (unsigned i=0; i<rhs.size(); ++i)
x(i) = rhs(i);
// Make the syntax below simpler by grabbing a reference to this array.
std::vector<T> & B = x.get_values();
// LDB is INTEGER
// The leading dimension of the array B. LDB >= max(1,max(M,N)).
int LDB = x.size();
// S is DOUBLE PRECISION array, dimension (min(M,N))
// The singular values of A in decreasing order.
// The condition number of A in the 2-norm = S(1)/S(min(m,n)).
std::vector<T> S(min_MN);
// RCOND is DOUBLE PRECISION
// RCOND is used to determine the effective rank of A.
// Singular values S(i) <= RCOND*S(1) are treated as zero.
// If RCOND < 0, machine precision is used instead.
Real RCOND = rcond;
// RANK is INTEGER
// The effective rank of A, i.e., the number of singular values
// which are greater than RCOND*S(1).
int RANK = 0;
// LWORK is INTEGER
// The dimension of the array WORK. LWORK >= 1, and also:
// LWORK >= 3*min(M,N) + max( 2*min(M,N), max(M,N), NRHS )
// For good performance, LWORK should generally be larger.
//
// If LWORK = -1, then a workspace query is assumed; the routine
// only calculates the optimal size of the WORK array, returns
// this value as the first entry of the WORK array, and no error
// message related to LWORK is issued by XERBLA.
//
// The factor of 1.5 is arbitrary and is used to satisfy the "should
// generally be larger" clause.
int LWORK = 1.5 * (3*min_MN + std::max(2*min_MN, std::max(max_MN, NRHS)));
// WORK is DOUBLE PRECISION array, dimension (MAX(1,LWORK))
// On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
std::vector<T> WORK(LWORK);
// INFO is INTEGER
// = 0: successful exit
// < 0: if INFO = -i, the i-th argument had an illegal value.
// > 0: the algorithm for computing the SVD failed to converge;
// if INFO = i, i off-diagonal elements of an intermediate
// bidiagonal form did not converge to zero.
int INFO = 0;
// LAPACKgelss_(const PetscBLASInt *, // M
// const PetscBLASInt *, // N
// const PetscBLASInt *, // NRHS
// PetscScalar *, // A
// const PetscBLASInt *, // LDA
// PetscScalar *, // B
// const PetscBLASInt *, // LDB
// PetscReal *, // S(out) = singular values of A in increasing order
// const PetscReal *, // RCOND = tolerance for singular values
// PetscBLASInt *, // RANK(out) = number of "non-zero" singular values
// PetscScalar *, // WORK
// const PetscBLASInt *, // LWORK
// PetscBLASInt *); // INFO
LAPACKgelss_(&M, &N, &NRHS, &A_trans_vals[0], &LDA, &B[0], &LDB, &S[0], &RCOND, &RANK, &WORK[0], &LWORK, &INFO);
// Check for errors in the Lapack call
if (INFO < 0)
libmesh_error_msg("Error, argument " << -INFO << " to LAPACKgelss_ had an illegal value.");
if (INFO > 0)
libmesh_error_msg("The algorithm for computing the SVD failed to converge!");
// Debugging: print singular values and information about condition number:
// libMesh::err << "RCOND=" << RCOND << std::endl;
// libMesh::err << "Singular values: " << std::endl;
// for (unsigned i=0; i<S.size(); ++i)
// libMesh::err << S[i] << std::endl;
// libMesh::err << "The condition number of A is approximately: " << S[0]/S.back() << std::endl;
// Lapack has already written the solution into B, but it will be
// the wrong size for non-square problems, so we need to resize it
// correctly. The size of the solution vector should be the number
// of columns of the original A matrix. Unfortunately, resizing a
// DenseVector currently also zeros it out (unlike a std::vector) so
// we'll resize the underlying storage directly (the size is not
// stored independently elsewhere).
x.get_values().resize(this->n());
}
//--------------------------------------------------------
// 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);
}
}
}
}
}
}
}
Real ExactErrorEstimator::find_squared_element_error(const System& system,
const std::string& var_name,
const Elem *elem,
const DenseVector<Number> &Uelem,
FEBase *fe,
MeshFunction *fine_values) const
{
// The (string) name of this system
const std::string& sys_name = system.name();
const unsigned int sys_num = system.number();
const unsigned int var = system.variable_number(var_name);
const unsigned int var_component =
system.variable_scalar_number(var, 0);
const Parameters& parameters = system.get_equation_systems().parameters;
// reinitialize the element-specific data
// for the current element
fe->reinit (elem);
// Get the data we need to compute with
const std::vector<Real> & JxW = fe->get_JxW();
const std::vector<std::vector<Real> >& phi_values = fe->get_phi();
const std::vector<std::vector<RealGradient> >& dphi_values = fe->get_dphi();
const std::vector<Point>& q_point = fe->get_xyz();
#ifdef LIBMESH_ENABLE_SECOND_DERIVATIVES
const std::vector<std::vector<RealTensor> >& d2phi_values = fe->get_d2phi();
#endif
// The number of shape functions
const unsigned int n_sf =
libmesh_cast_int<unsigned int>(Uelem.size());
// The number of quadrature points
const unsigned int n_qp =
libmesh_cast_int<unsigned int>(JxW.size());
Real error_val = 0;
// Begin the loop over the Quadrature points.
//
for (unsigned int qp=0; qp<n_qp; qp++)
{
// Real u_h = 0.;
// RealGradient grad_u_h;
Number u_h = 0.;
Gradient grad_u_h;
#ifdef LIBMESH_ENABLE_SECOND_DERIVATIVES
Tensor grad2_u_h;
#endif
// Compute solution values at the current
// quadrature point. This reqiures a sum
// over all the shape functions evaluated
// at the quadrature point.
for (unsigned int i=0; i<n_sf; i++)
{
// Values from current solution.
u_h += phi_values[i][qp]*Uelem(i);
grad_u_h += dphi_values[i][qp]*Uelem(i);
#ifdef LIBMESH_ENABLE_SECOND_DERIVATIVES
grad2_u_h += d2phi_values[i][qp]*Uelem(i);
#endif
}
// Compute the value of the error at this quadrature point
if (error_norm.type(var) == L2 ||
error_norm.type(var) == H1 ||
error_norm.type(var) == H2)
{
Number val_error = u_h;
if (_exact_value)
val_error -= _exact_value(q_point[qp],parameters,sys_name,var_name);
else if (_exact_values.size() > sys_num && _exact_values[sys_num])
val_error -= _exact_values[sys_num]->
component(var_component, q_point[qp], system.time);
else if (_equation_systems_fine)
val_error -= (*fine_values)(q_point[qp]);
// Add the squares of the error to each contribution
error_val += JxW[qp]*TensorTools::norm_sq(val_error);
}
// Compute the value of the error in the gradient at this
// quadrature point
if (error_norm.type(var) == H1 ||
error_norm.type(var) == H1_SEMINORM ||
error_norm.type(var) == H2)
{
Gradient grad_error = grad_u_h;
if(_exact_deriv)
grad_error -= _exact_deriv(q_point[qp],parameters,sys_name,var_name);
else if (_exact_derivs.size() > sys_num && _exact_derivs[sys_num])
grad_error -= _exact_derivs[sys_num]->
component(var_component, q_point[qp], system.time);
else if(_equation_systems_fine)
grad_error -= fine_values->gradient(q_point[qp]);
error_val += JxW[qp]*grad_error.size_sq();
}
#ifdef LIBMESH_ENABLE_SECOND_DERIVATIVES
// Compute the value of the error in the hessian at this
// quadrature point
if ((error_norm.type(var) == H2_SEMINORM ||
error_norm.type(var) == H2))
{
Tensor grad2_error = grad2_u_h;
if(_exact_hessian)
grad2_error -= _exact_hessian(q_point[qp],parameters,sys_name,var_name);
else if (_exact_hessians.size() > sys_num && _exact_hessians[sys_num])
grad2_error -= _exact_hessians[sys_num]->
component(var_component, q_point[qp], system.time);
else if (_equation_systems_fine)
grad2_error -= fine_values->hessian(q_point[qp]);
error_val += JxW[qp]*grad2_error.size_sq();
}
#endif
} // end qp loop
libmesh_assert_greater_equal (error_val, 0.);
return error_val;
}
bool LineSearcher::MoreThuenteLineSearch(
DenseVector ¶m, DenseVector &direc, DenseVector &grad, double finit,
double &stepsize,
std::function<double(DenseVector &, DenseVector &)> &funcgrad) {
itercnt_ = 0;
int brackt, stage1, uinfo = 0;
double dg;
double stx, fx, dgx;
double sty, fy, dgy;
double fxm, dgxm, fym, dgym, fm, dgm;
double ftest1, dginit, dgtest;
double width, prev_width;
double stmin, stmax;
double fval;
if (stepsize < 0) {
LOG(FATAL) << "Stepsize less than 0";
return false;
}
dginit = direc.dot(grad);
if (dginit > 0) {
LOG(FATAL) << "Direction not decent";
return false;
}
if (tparam_.size() != param.size()) {
tparam_.resize(param.size());
}
/* Initialize local variables. */
brackt = 0;
stage1 = 1;
dgtest = alpha_ * dginit;
width = maxstep_ - minstep_;
prev_width = 2.0 * width;
stx = sty = 0.;
fx = fy = finit;
dgx = dgy = dginit;
while (itercnt_ < maxtries_) {
/*
Set the minimum and maximum steps to correspond to the
present interval of uncertainty.
*/
if (brackt) {
stmin = std::min(stx, sty);
stmax = std::min(stx, sty);
} else {
stmin = stx;
stmax = stepsize + 4.0 * (stepsize - stx);
}
/* Clip the step in the range of [minstep_, maxstep_]. */
if (stepsize < minstep_)
stepsize = minstep_;
if (stepsize > maxstep_)
stepsize = maxstep_;
/*
If an unusual termination is to occur then let
stepsize be the lowest point obtained so far.
*/
if ((brackt && ((stepsize <= stmin || stepsize >= stmax) ||
(itercnt_ + 1 >= maxtries_) || uinfo != 0)) ||
(brackt && (stmax - stmin <= parameps_ * stmax))) {
stepsize = stx;
}
tparam_ = param + stepsize * direc;
fval = funcgrad(tparam_, grad);
dg = grad.dot(direc);
ftest1 = finit + stepsize * dgtest;
++itercnt_;
/* Test for errors and convergence. */
if (brackt && ((stepsize <= stmin || stmax <= stepsize) || uinfo != 0)) {
/* Rounding errors prevent further progress. */
return false;
}
if (stepsize == maxstep_ && fval <= ftest1 && dg <= dgtest) {
/* The step is the maximum value. */
return false;
}
if (stepsize == minstep_ && (ftest1 < fval || dgtest <= dg)) {
/* The step is the minimum value. */
return false;
}
if (brackt && (stmax - stmin) <= parameps_ * stmax) {
/* Relative width of the interval of uncertainty is at most xtol. */
return false;
}
if (maxtries_ <= itercnt_) {
/* Maximum number of iteration. */
return false;
}
if (fval <= ftest1 && std::fabs(dg) <= beta_ * (-dginit)) {
/* The sufficient decrease condition and the directional derivative
* condition hold. */
param.swap(tparam_);
return true;
}
/*
In the first stage we seek a step for which the modified
function has a nonpositive value and nonnegative derivative.
*/
if (stage1 && fval <= ftest1 && std::min(alpha_, beta_) * dginit <= dg) {
stage1 = 0;
}
/*
A modified function is used to predict the step only if
we have not obtained a step for which the modified
function has a nonpositive function value and nonnegative
derivative, and if a lower function value has been
obtained but the decrease is not sufficient.
*/
if (stage1 && ftest1 < fval && fval <= fx) {
/* Define the modified function and derivative values. */
fm = fval - stepsize * dgtest;
fxm = fx - stx * dgtest;
fym = fy - sty * dgtest;
dgm = dg - dgtest;
dgxm = dgx - dgtest;
dgym = dgy - dgtest;
/*
Call update_trial_interval() to update the interval of
uncertainty and to compute the new step.
*/
uinfo =
update_trial_interval(&stx, &fxm, &dgxm, &sty, &fym, &dgym, &stepsize,
&fm, &dgm, stmin, stmax, &brackt);
/* Reset the function and gradient values for f. */
fx = fxm + stx * dgtest;
fy = fym + sty * dgtest;
dgx = dgxm + dgtest;
dgy = dgym + dgtest;
} else {
/*
Call update_trial_interval() to update the interval of
uncertainty and to compute the new step.
*/
uinfo = update_trial_interval(&stx, &fx, &dgx, &sty, &fy, &dgy, &stepsize,
&fval, &dg, stmin, stmax, &brackt);
}
/*
Force a sufficient decrease in the interval of uncertainty.
*/
if (brackt) {
if (0.66 * prev_width <= fabs(sty - stx)) {
stepsize = stx + 0.5 * (sty - stx);
}
prev_width = width;
width = std::fabs(sty - stx);
}
}
return false;
}
bool LineSearcher::BackTrackLineSearch(
DenseVector ¶m, DenseVector &direc, DenseVector &grad, double finit,
double &stepsize,
std::function<double(DenseVector &, DenseVector &)> &funcgrad) {
itercnt_ = 0;
double stepupdate;
double dginit = direc.dot(grad), dgtest, fval, dgval;
const double stepshrink = 0.5, stepexpand = 2.1;
if (dginit > 0) {
LOG(FATAL) << "initial direction is not a decent direction";
return false;
}
if (tparam_.size() != param.size()) {
tparam_.resize(param.size());
}
dgtest = dginit * alpha_;
while (itercnt_ < maxtries_) {
tparam_ = param + stepsize * direc;
fval = funcgrad(tparam_, grad);
if (fval > finit + stepsize * dgtest) {
stepupdate = stepshrink;
} else {
if (lscondtype_ == LineSearchConditionType::Armijo) {
break;
}
dgval = direc.dot(grad);
if (dgval < beta_ * dginit) {
stepupdate = stepexpand;
} else {
if (lscondtype_ == LineSearchConditionType::Wolfe) {
break;
}
if (dgval > -beta_ * dginit) {
stepupdate = stepshrink;
} else {
break;
}
}
}
if (stepsize < minstep_) {
LOG(ERROR) << "Small than smallest step size";
return false;
}
if (stepsize > maxstep_) {
LOG(ERROR) << "Large than largest step size";
return false;
}
stepsize *= stepupdate;
++itercnt_;
}
if (itercnt_ >= maxtries_) {
LOG(ERROR) << "Exceed Maximum number of iteration count";
return false;
}
param.swap(tparam_);
return true;
}
