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template the directional methods

release/4.3a0
Varun Agrawal 2024-10-18 10:30:01 -04:00
parent 2d3a296d06
commit 2d4ee5057a
2 changed files with 35 additions and 49 deletions
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40
gtsam/nonlinear/NonlinearConjugateGradientOptimizer.cpp
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@ -28,46 +28,6 @@ namespace gtsam {
typedef internal::NonlinearOptimizerState State;
/* ************************************************************************* */
double FletcherReeves(const VectorValues& currentGradient,
const VectorValues& prevGradient) {
// Fletcher-Reeves: beta = g_n'*g_n/g_n-1'*g_n-1
const double beta = std::max(0.0, currentGradient.dot(currentGradient) /
prevGradient.dot(prevGradient));
return beta;
}
/* ************************************************************************* */
double PolakRibiere(const VectorValues& currentGradient,
const VectorValues& prevGradient) {
// Polak-Ribiere: beta = g_n'*(g_n-g_n-1)/g_n-1'*g_n-1
const double beta =
std::max(0.0, currentGradient.dot(currentGradient - prevGradient) /
prevGradient.dot(prevGradient));
return beta;
}
/* ************************************************************************* */
double HestenesStiefel(const VectorValues& currentGradient,
const VectorValues& prevGradient,
const VectorValues& direction) {
// Hestenes-Stiefel: beta = g_n'*(g_n-g_n-1)/(-s_n-1')*(g_n-g_n-1)
VectorValues d = currentGradient - prevGradient;
const double beta = std::max(0.0, currentGradient.dot(d) / -direction.dot(d));
return beta;
}
/* ************************************************************************* */
double DaiYuan(const VectorValues& currentGradient,
const VectorValues& prevGradient,
const VectorValues& direction) {
// Dai-Yuan: beta = g_n'*g_n/(-s_n-1')*(g_n-g_n-1)
const double beta =
std::max(0.0, currentGradient.dot(currentGradient) /
-direction.dot(currentGradient - prevGradient));
return beta;
}
/**
* @brief Return the gradient vector of a nonlinear factor graph
* @param nfg the graph

44
gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h
View File

@ -24,22 +24,48 @@
namespace gtsam {
/// Fletcher-Reeves formula for computing β, the direction of steepest descent.
double FletcherReeves(const VectorValues &currentGradient,
const VectorValues &prevGradient);
template <typename Gradient>
double FletcherReeves(const Gradient &currentGradient,
const Gradient &prevGradient) {
// Fletcher-Reeves: beta = g_n'*g_n/g_n-1'*g_n-1
const double beta =
currentGradient.dot(currentGradient) / prevGradient.dot(prevGradient);
return beta;
}
/// Polak-Ribiere formula for computing β, the direction of steepest descent.
double PolakRibiere(const VectorValues &currentGradient,
const VectorValues &prevGradient);
template <typename Gradient>
double PolakRibiere(const Gradient &currentGradient,
const Gradient &prevGradient) {
// Polak-Ribiere: beta = g_n'*(g_n-g_n-1)/g_n-1'*g_n-1
const double beta =
std::max(0.0, currentGradient.dot(currentGradient - prevGradient) /
prevGradient.dot(prevGradient));
return beta;
}
/// The Hestenes-Stiefel formula for computing β,
/// the direction of steepest descent.
double HestenesStiefel(const VectorValues &currentGradient,
const VectorValues &prevGradient,
const VectorValues &direction);
template <typename Gradient>
double HestenesStiefel(const Gradient &currentGradient,
const Gradient &prevGradient,
const Gradient &direction) {
// Hestenes-Stiefel: beta = g_n'*(g_n-g_n-1)/(-s_n-1')*(g_n-g_n-1)
VectorValues d = currentGradient - prevGradient;
const double beta = std::max(0.0, currentGradient.dot(d) / -direction.dot(d));
return beta;
}
/// The Dai-Yuan formula for computing β, the direction of steepest descent.
double DaiYuan(const VectorValues &currentGradient,
const VectorValues &prevGradient, const VectorValues &direction);
template <typename Gradient>
double DaiYuan(const Gradient &currentGradient, const Gradient &prevGradient,
const VectorValues &direction) {
// Dai-Yuan: beta = g_n'*g_n/(-s_n-1')*(g_n-g_n-1)
const double beta =
std::max(0.0, currentGradient.dot(currentGradient) /
-direction.dot(currentGradient - prevGradient));
return beta;
}
enum class DirectionMethod {
FletcherReeves,