nonrecursive version of optimizeWildfire for ISAM2 to avoid stack overflow problem
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76b88bd06e
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71bd64c693
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@ -422,7 +422,7 @@ size_t ISAM2::Impl::UpdateDelta(const boost::shared_ptr<ISAM2Clique>& root, std:
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} else {
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} else {
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// Optimize with wildfire
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// Optimize with wildfire
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lastBacksubVariableCount = optimizeWildfire(root, wildfireThreshold, replacedKeys, delta); // modifies delta_
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lastBacksubVariableCount = optimizeWildfireNonRecursive(root, wildfireThreshold, replacedKeys, delta); // modifies delta_
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#ifndef NDEBUG
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#ifndef NDEBUG
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for(size_t j=0; j<delta.size(); ++j)
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for(size_t j=0; j<delta.size(); ++j)
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@ -487,7 +487,7 @@ size_t ISAM2::Impl::UpdateDoglegDeltas(const ISAM2& isam, double wildfireThresho
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// Update variables
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// Update variables
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size_t varsUpdated = 0;
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size_t varsUpdated = 0;
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internal::updateDoglegDeltas(isam.root(), replacedKeys, grad, deltaNewton, RgProd, varsUpdated);
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internal::updateDoglegDeltas(isam.root(), replacedKeys, grad, deltaNewton, RgProd, varsUpdated);
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optimizeWildfire(isam.root(), wildfireThreshold, replacedKeys, deltaNewton);
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optimizeWildfireNonRecursive(isam.root(), wildfireThreshold, replacedKeys, deltaNewton);
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#if 0
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#if 0
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VectorValues expected = *allocateVectorValues(isam);
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VectorValues expected = *allocateVectorValues(isam);
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@ -19,7 +19,7 @@
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#pragma once
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#pragma once
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#include <stack>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/linear/JacobianFactor.h>
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#include <gtsam/linear/JacobianFactor.h>
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@ -110,7 +110,82 @@ void optimizeWildfire(const boost::shared_ptr<CLIQUE>& clique, double threshold,
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}
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}
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}
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}
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}
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}
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template<class CLIQUE>
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bool optimizeWildfireNode(const boost::shared_ptr<CLIQUE>& clique, double threshold,
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std::vector<bool>& changed, const std::vector<bool>& replaced, VectorValues& delta, int& count) {
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// if none of the variables in this clique (frontal and separator!) changed
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// significantly, then by the running intersection property, none of the
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// cliques in the children need to be processed
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// Are any clique variables part of the tree that has been redone?
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bool cliqueReplaced = replaced[(*clique)->frontals().front()];
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#ifndef NDEBUG
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BOOST_FOREACH(Index frontal, (*clique)->frontals()) {
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assert(cliqueReplaced == replaced[frontal]);
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}
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}
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#endif
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// If not redone, then has one of the separator variables changed significantly?
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bool recalculate = cliqueReplaced;
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if(!recalculate) {
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BOOST_FOREACH(Index parent, (*clique)->parents()) {
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if(changed[parent]) {
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recalculate = true;
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break;
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}
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}
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}
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// Solve clique if it was replaced, or if any parents were changed above the
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// threshold or themselves replaced.
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if(recalculate) {
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// Temporary copy of the original values, to check how much they change
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std::vector<Vector> originalValues((*clique)->nrFrontals());
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GaussianConditional::const_iterator it;
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for(it = (*clique)->beginFrontals(); it!=(*clique)->endFrontals(); it++) {
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originalValues[it - (*clique)->beginFrontals()] = delta[*it];
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}
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// Back-substitute
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(*clique)->solveInPlace(delta);
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count += (*clique)->nrFrontals();
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// Whether the values changed above a threshold, or always true if the
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// clique was replaced.
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bool valuesChanged = cliqueReplaced;
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for(it = (*clique)->beginFrontals(); it!=(*clique)->endFrontals(); it++) {
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if(!valuesChanged) {
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const Vector& oldValue(originalValues[it - (*clique)->beginFrontals()]);
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const SubVector& newValue(delta[*it]);
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if((oldValue - newValue).lpNorm<Eigen::Infinity>() >= threshold) {
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valuesChanged = true;
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break;
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}
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} else
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break;
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}
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// If the values were above the threshold or this clique was replaced
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if(valuesChanged) {
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// Set changed flag for each frontal variable and leave the new values
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BOOST_FOREACH(Index frontal, (*clique)->frontals()) {
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changed[frontal] = true;
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}
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} else {
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// Replace with the old values
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for(it = (*clique)->beginFrontals(); it!=(*clique)->endFrontals(); it++) {
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delta[*it] = originalValues[it - (*clique)->beginFrontals()];
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}
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}
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}
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return recalculate;
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}
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} // namespace internal
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/* ************************************************************************* */
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/* ************************************************************************* */
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template<class CLIQUE>
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template<class CLIQUE>
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@ -123,6 +198,31 @@ int optimizeWildfire(const boost::shared_ptr<CLIQUE>& root, double threshold, co
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return count;
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return count;
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}
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}
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/* ************************************************************************* */
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template<class CLIQUE>
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int optimizeWildfireNonRecursive(const boost::shared_ptr<CLIQUE>& root, double threshold, const std::vector<bool>& keys, VectorValues& delta) {
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std::vector<bool> changed(keys.size(), false);
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int count = 0;
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if (root) {
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std::stack<boost::shared_ptr<CLIQUE> > travStack;
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travStack.push(root);
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boost::shared_ptr<CLIQUE> currentNode = root;
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while (!travStack.empty()) {
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currentNode = travStack.top();
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travStack.pop();
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bool recalculate = internal::optimizeWildfireNode(currentNode, threshold, changed, keys, delta, count);
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if (recalculate) {
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BOOST_FOREACH(const typename CLIQUE::shared_ptr& child, currentNode->children_) {
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travStack.push(child);
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}
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}
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}
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}
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return count;
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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template<class CLIQUE>
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template<class CLIQUE>
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void nnz_internal(const boost::shared_ptr<CLIQUE>& clique, int& result) {
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void nnz_internal(const boost::shared_ptr<CLIQUE>& clique, int& result) {
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@ -640,6 +640,10 @@ template<class CLIQUE>
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int optimizeWildfire(const boost::shared_ptr<CLIQUE>& root,
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int optimizeWildfire(const boost::shared_ptr<CLIQUE>& root,
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double threshold, const std::vector<bool>& replaced, VectorValues& delta);
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double threshold, const std::vector<bool>& replaced, VectorValues& delta);
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template<class CLIQUE>
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int optimizeWildfireNonRecursive(const boost::shared_ptr<CLIQUE>& root,
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double threshold, const std::vector<bool>& replaced, VectorValues& delta);
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/**
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/**
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* Optimize along the gradient direction, with a closed-form computation to
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* Optimize along the gradient direction, with a closed-form computation to
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* perform the line search. The gradient is computed about \f$ \delta x=0 \f$.
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* perform the line search. The gradient is computed about \f$ \delta x=0 \f$.
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