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;
}
