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remove templates

release/4.3a0
Varun Agrawal 2024-10-15 12:29:07 -04:00
parent ba6e2b8d7f
commit 1de138678f
2 changed files with 15 additions and 23 deletions
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2
gtsam/nonlinear/NonlinearConjugateGradientOptimizer.cpp
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@ -65,7 +65,7 @@ Values NonlinearConjugateGradientOptimizer::advance(
}
GaussianFactorGraph::shared_ptr NonlinearConjugateGradientOptimizer::iterate() {
const auto [newValues, dummy] = nonlinearConjugateGradient<Values>(
const auto [newValues, dummy] = nonlinearConjugateGradient(
state_->values, params_, true /* single iteration */);
state_.reset(
new State(newValues, graph_.error(newValues), state_->iterations + 1));

36
gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h
View File

@ -52,21 +52,20 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
Values advance(const Values &current, const double alpha,
const VectorValues &g) const;
/**
* Perform a single iteration, returning GaussianFactorGraph corresponding to
/**
* Perform a single iteration, returning GaussianFactorGraph corresponding to
* the linearized factor graph.
*/
GaussianFactorGraph::shared_ptr iterate() override;
/**
* Optimize for the maximum-likelihood estimate, returning a the optimized
/**
* Optimize for the maximum-likelihood estimate, returning a the optimized
* variable assignments.
*/
const Values& optimize() override;
const Values &optimize() override;
/** Implement the golden-section line search algorithm */
template <class V, class W>
double lineSearch(const V currentValues, const W &gradient) {
double lineSearch(const Values &currentValues, const VectorValues &gradient) const {
/* normalize it such that it becomes a unit vector */
const double g = gradient.norm();
@ -78,7 +77,7 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
double minStep = -1.0 / g, maxStep = 0,
newStep = minStep + (maxStep - minStep) / (phi + 1.0);
V newValues = advance(currentValues, newStep, gradient);
Values newValues = advance(currentValues, newStep, gradient);
double newError = error(newValues);
while (true) {
@ -91,7 +90,7 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
return 0.5 * (minStep + maxStep);
}
const V testValues = advance(currentValues, testStep, gradient);
const Values testValues = advance(currentValues, testStep, gradient);
const double testError = error(testValues);
// update the working range
@ -120,18 +119,12 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
* formula suggested in
* http://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method.
*
* The V class
* denotes the state or the solution.
*
* The last parameter is a switch between gradient-descent and conjugate
* gradient
*/
template <class V>
std::tuple<V, int> nonlinearConjugateGradient(
const V &initial, const NonlinearOptimizerParams &params,
const bool singleIteration, const bool gradientDescent = false) {
// GTSAM_CONCEPT_MANIFOLD_TYPE(V)
std::tuple<Values, int> nonlinearConjugateGradient(
const Values &initial, const NonlinearOptimizerParams &params,
const bool singleIteration, const bool gradientDescent = false) const {
size_t iteration = 0;
// check if we're already close enough
@ -144,12 +137,12 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
return {initial, iteration};
}
V currentValues = initial;
Values currentValues = initial;
VectorValues currentGradient = gradient(currentValues), prevGradient,
direction = currentGradient;
/* do one step of gradient descent */
V prevValues = currentValues;
Values prevValues = currentValues;
double prevError = currentError;
double alpha = lineSearch(currentValues, direction);
currentValues = advance(prevValues, alpha, direction);
@ -205,5 +198,4 @@ class GTSAM_EXPORT NonlinearConjugateGradientOptimizer
}
};
} // \ namespace gtsam
} // namespace gtsam