double L_BFGS<FunctionType>::Optimize(arma::mat& iterate,
const size_t maxIterations)
{
// Ensure that the cubes holding past iterations' information are the right
// size. Also set the current best point value to the maximum.
const size_t rows = function.GetInitialPoint().n_rows;
const size_t cols = function.GetInitialPoint().n_cols;
s.set_size(rows, cols, numBasis);
y.set_size(rows, cols, numBasis);
minPointIterate.second = std::numeric_limits<double>::max();
// The old iterate to be saved.
arma::mat oldIterate;
oldIterate.zeros(iterate.n_rows, iterate.n_cols);
// Whether to optimize until convergence.
bool optimizeUntilConvergence = (maxIterations == 0);
// The initial function value.
double functionValue = Evaluate(iterate);
// The gradient: the current and the old.
arma::mat gradient;
arma::mat oldGradient;
gradient.zeros(iterate.n_rows, iterate.n_cols);
oldGradient.zeros(iterate.n_rows, iterate.n_cols);
// The search direction.
arma::mat searchDirection;
searchDirection.zeros(iterate.n_rows, iterate.n_cols);
// The initial gradient value.
function.Gradient(iterate, gradient);
// The main optimization loop.
for (size_t itNum = 0; optimizeUntilConvergence || (itNum != maxIterations);
++itNum)
{
Rcpp::Rcout << "L-BFGS iteration " << itNum << "; objective " <<
function.Evaluate(iterate) << "." << std::endl;
// Break when the norm of the gradient becomes too small.
if (GradientNormTooSmall(gradient))
{
Rcpp::Rcout << "L-BFGS gradient norm too small (terminating successfully)."
<< std::endl;
break;
}
// Choose the scaling factor.
double scalingFactor = ChooseScalingFactor(itNum, gradient);
// Build an approximation to the Hessian and choose the search
// direction for the current iteration.
SearchDirection(gradient, itNum, scalingFactor, searchDirection);
// Save the old iterate and the gradient before stepping.
oldIterate = iterate;
oldGradient = gradient;
// Do a line search and take a step.
if (!LineSearch(functionValue, iterate, gradient, searchDirection))
{
Rcpp::Rcout << "Line search failed. Stopping optimization." << std::endl;
break; // The line search failed; nothing else to try.
}
// It is possible that the difference between the two coordinates is zero.
// In this case we terminate successfully.
if (accu(iterate != oldIterate) == 0)
{
Rcpp::Rcout << "L-BFGS step size of 0 (terminating successfully)."
<< std::endl;
break;
}
// Overwrite an old basis set.
UpdateBasisSet(itNum, iterate, oldIterate, gradient, oldGradient);
} // End of the optimization loop.
return function.Evaluate(iterate);
}
double L_BFGS<FunctionType>::Optimize(arma::mat& iterate,
const size_t maxIterations)
{
// Ensure that the cubes holding past iterations' information are the right
// size. Also set the current best point value to the maximum.
const size_t rows = function.GetInitialPoint().n_rows;
const size_t cols = function.GetInitialPoint().n_cols;
s.set_size(rows, cols, numBasis);
y.set_size(rows, cols, numBasis);
minPointIterate.second = std::numeric_limits<double>::max();
// The old iterate to be saved.
arma::mat oldIterate;
oldIterate.zeros(iterate.n_rows, iterate.n_cols);
// Whether to optimize until convergence.
bool optimizeUntilConvergence = (maxIterations == 0);
// The initial function value.
double functionValue = Evaluate(iterate);
double prevFunctionValue = functionValue;
// The gradient: the current and the old.
arma::mat gradient;
arma::mat oldGradient;
gradient.zeros(iterate.n_rows, iterate.n_cols);
oldGradient.zeros(iterate.n_rows, iterate.n_cols);
// The search direction.
arma::mat searchDirection;
searchDirection.zeros(iterate.n_rows, iterate.n_cols);
// The initial gradient value.
function.Gradient(iterate, gradient);
// The main optimization loop.
for (size_t itNum = 0; optimizeUntilConvergence || (itNum != maxIterations);
++itNum)
{
Log::Debug << "L-BFGS iteration " << itNum << "; objective " <<
function.Evaluate(iterate) << ", gradient norm " <<
arma::norm(gradient, 2) << ", " <<
((prevFunctionValue - functionValue) /
std::max(std::max(fabs(prevFunctionValue), fabs(functionValue)), 1.0)) << "." << std::endl;
prevFunctionValue = functionValue;
// Break when the norm of the gradient becomes too small.
//
// But don't do this on the first iteration to ensure we always take at
// least one descent step.
if (itNum > 0 && GradientNormTooSmall(gradient))
{
Log::Debug << "L-BFGS gradient norm too small (terminating successfully)."
<< std::endl;
break;
}
// Break if the objective is not a number.
if (std::isnan(functionValue))
{
Log::Warn << "L-BFGS terminated with objective " << functionValue << "; "
<< "are the objective and gradient functions implemented correctly?"
<< std::endl;
break;
}
// Choose the scaling factor.
double scalingFactor = ChooseScalingFactor(itNum, gradient);
// Build an approximation to the Hessian and choose the search
// direction for the current iteration.
SearchDirection(gradient, itNum, scalingFactor, searchDirection);
// Save the old iterate and the gradient before stepping.
oldIterate = iterate;
oldGradient = gradient;
// Do a line search and take a step.
if (!LineSearch(functionValue, iterate, gradient, searchDirection))
{
Log::Debug << "Line search failed. Stopping optimization." << std::endl;
break; // The line search failed; nothing else to try.
}
// It is possible that the difference between the two coordinates is zero.
// In this case we terminate successfully.
if (accu(iterate != oldIterate) == 0)
{
Log::Debug << "L-BFGS step size of 0 (terminating successfully)."
<< std::endl;
break;
}
// If we can't make progress on the gradient, then we'll also accept
// a stable function value.
const double denom =
std::max(
std::max(fabs(prevFunctionValue), fabs(functionValue)),
1.0);
if ((prevFunctionValue - functionValue) / denom <= factr)
{
Log::Debug << "L-BFGS function value stable (terminating successfully)."
<< std::endl;
break;
}
// Overwrite an old basis set.
UpdateBasisSet(itNum, iterate, oldIterate, gradient, oldGradient);
} // End of the optimization loop.
return function.Evaluate(iterate);
}
