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gtsam/gtsam_unstable/nonlinear/ConcurrentBatchSmoother.cpp

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file ConcurrentBatchSmoother.cpp
* @brief A Levenberg-Marquardt Batch Smoother that implements the
* Concurrent Filtering and Smoothing interface.
* @author Stephen Williams
*/
#include <gtsam_unstable/nonlinear/ConcurrentBatchSmoother.h>
#include <gtsam/nonlinear/LinearContainerFactor.h>
#include <gtsam/linear/GaussianJunctionTree.h>
#include <gtsam/base/timing.h>
#include <gtsam/base/debug.h>
namespace gtsam {
/* ************************************************************************* */
void ConcurrentBatchSmoother::print(const std::string& s, const KeyFormatter& keyFormatter) const {
std::cout << s;
std::cout << " Factors:" << std::endl;
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, factors_) {
PrintNonlinearFactor(factor, " ", keyFormatter);
}
theta_.print("Values:\n");
}
/* ************************************************************************* */
bool ConcurrentBatchSmoother::equals(const ConcurrentSmoother& rhs, double tol) const {
const ConcurrentBatchSmoother* smoother = dynamic_cast<const ConcurrentBatchSmoother*>(&rhs);
return smoother
&& factors_.equals(smoother->factors_)
&& theta_.equals(smoother->theta_)
&& ordering_.equals(smoother->ordering_)
&& delta_.equals(smoother->delta_)
&& separatorValues_.equals(smoother->separatorValues_);
}
/* ************************************************************************* */
ConcurrentBatchSmoother::Result ConcurrentBatchSmoother::update(const NonlinearFactorGraph& newFactors, const Values& newTheta) {
gttic(update);
// Create the return result meta-data
Result result;
// Update all of the internal variables with the new information
gttic(augment_system);
{
// Add the new variables to theta
theta_.insert(newTheta);
// Add new variables to the end of the ordering
std::vector<size_t> dims;
dims.reserve(newTheta.size());
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, newTheta) {
ordering_.push_back(key_value.key);
dims.push_back(key_value.value.dim());
}
// Augment Delta
delta_.append(dims);
for(size_t i = delta_.size() - dims.size(); i < delta_.size(); ++i) {
delta_[i].setZero();
}
// Add the new factors to the graph, updating the variable index
insertFactors(newFactors);
}
gttoc(augment_system);
// Reorder the system to ensure efficient optimization (and marginalization) performance
gttic(reorder);
reorder();
gttoc(reorder);
// Optimize the factors using a modified version of L-M
gttic(optimize);
if(factors_.size() > 0) {
result = optimize();
}
gttoc(optimize);
// Moved presync code into the update function. Generally, only one call to smoother.update(*) is performed
// between synchronizations, so no extra work is being done. This also allows the presync code to be performed
// while the filter is still running (instead of during the synchronization when the filter is paused)
gttic(presync);
if(separatorValues_.size() > 0) {
updateSmootherSummarization();
}
gttoc(presync);
gttoc(update);
return result;
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::presync() {
gttic(presync);
gttoc(presync);
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::getSummarizedFactors(NonlinearFactorGraph& summarizedFactors, Values& separatorValues) {
gttic(get_summarized_factors);
// Copy the previous calculated smoother summarization factors into the output
summarizedFactors.push_back(smootherSummarization_);
// Copy the separator values into the output
separatorValues.insert(separatorValues_);
gttoc(get_summarized_factors);
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::synchronize(const NonlinearFactorGraph& smootherFactors, const Values& smootherValues,
const NonlinearFactorGraph& summarizedFactors, const Values& separatorValues) {
gttic(synchronize);
// Remove the previous filter summarization from the graph
removeFactors(filterSummarizationSlots_);
// Insert new linpoints into the values, augment the ordering, and store new dims to augment delta
std::vector<size_t> dims;
dims.reserve(smootherValues.size() + separatorValues.size());
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, smootherValues) {
Values::iterator iter = theta_.find(key_value.key);
if(iter == theta_.end()) {
theta_.insert(key_value.key, key_value.value);
ordering_.push_back(key_value.key);
dims.push_back(key_value.value.dim());
} else {
iter->value = key_value.value;
}
}
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, separatorValues) {
Values::iterator iter = theta_.find(key_value.key);
if(iter == theta_.end()) {
theta_.insert(key_value.key, key_value.value);
ordering_.push_back(key_value.key);
dims.push_back(key_value.value.dim());
} else {
iter->value = key_value.value;
}
}
// Augment Delta
delta_.append(dims);
for(size_t i = delta_.size() - dims.size(); i < delta_.size(); ++i) {
delta_[i].setZero();
}
// Insert the new smoother factors
insertFactors(smootherFactors);
// Insert the new filter summarized factors
filterSummarizationSlots_ = insertFactors(summarizedFactors);
// Update the list of root keys
separatorValues_ = separatorValues;
gttoc(synchronize);
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::postsync() {
gttic(postsync);
gttoc(postsync);
}
/* ************************************************************************* */
std::vector<size_t> ConcurrentBatchSmoother::insertFactors(const NonlinearFactorGraph& factors) {
gttic(insert_factors);
// create the output vector
std::vector<size_t> slots;
slots.reserve(factors.size());
// Insert the factor into an existing hole in the factor graph, if possible
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, factors) {
size_t slot;
if(availableSlots_.size() > 0) {
slot = availableSlots_.front();
availableSlots_.pop();
factors_.replace(slot, factor);
} else {
slot = factors_.size();
factors_.push_back(factor);
}
slots.push_back(slot);
}
gttoc(insert_factors);
return slots;
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::removeFactors(const std::vector<size_t>& slots) {
gttic(remove_factors);
// For each factor slot to delete...
SymbolicFactorGraph factors;
BOOST_FOREACH(size_t slot, slots) {
// Create a symbolic version for the variable index
factors.push_back(factors_.at(slot)->symbolic(ordering_));
// Remove the factor from the graph
factors_.remove(slot);
// Mark the factor slot as available
availableSlots_.push(slot);
}
gttoc(remove_factors);
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::reorder() {
// Recalculate the variable index
variableIndex_ = VariableIndex(*factors_.symbolic(ordering_));
// Initialize all variables to group0
std::vector<int> cmember(variableIndex_.size(), 0);
// Set all of the separator keys to Group1
if(separatorValues_.size() > 0) {
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, separatorValues_) {
cmember[ordering_.at(key_value.key)] = 1;
}
}
// Generate the permutation
Permutation forwardPermutation = *inference::PermutationCOLAMD_(variableIndex_, cmember);
// Permute the ordering, variable index, and deltas
ordering_.permuteInPlace(forwardPermutation);
variableIndex_.permuteInPlace(forwardPermutation);
delta_.permuteInPlace(forwardPermutation);
}
/* ************************************************************************* */
ConcurrentBatchSmoother::Result ConcurrentBatchSmoother::optimize() {
// Create output result structure
Result result;
result.nonlinearVariables = theta_.size() - separatorValues_.size();
result.linearVariables = separatorValues_.size();
// Set optimization parameters
double lambda = parameters_.lambdaInitial;
double lambdaFactor = parameters_.lambdaFactor;
double lambdaUpperBound = parameters_.lambdaUpperBound;
double lambdaLowerBound = 0.5 / parameters_.lambdaUpperBound;
size_t maxIterations = parameters_.maxIterations;
double relativeErrorTol = parameters_.relativeErrorTol;
double absoluteErrorTol = parameters_.absoluteErrorTol;
double errorTol = parameters_.errorTol;
// Create a Values that holds the current evaluation point
Values evalpoint = theta_.retract(delta_, ordering_);
result.error = factors_.error(evalpoint);
// Use a custom optimization loop so the linearization points can be controlled
double previousError;
VectorValues newDelta;
do {
previousError = result.error;
// Do next iteration
gttic(optimizer_iteration);
{
// Linearize graph around the linearization point
GaussianFactorGraph linearFactorGraph = *factors_.linearize(theta_, ordering_);
// Keep increasing lambda until we make make progress
while(true) {
// Add prior factors at the current solution
gttic(damp);
GaussianFactorGraph dampedFactorGraph(linearFactorGraph);
dampedFactorGraph.reserve(linearFactorGraph.size() + delta_.size());
{
// for each of the variables, add a prior at the current solution
for(size_t j=0; j<delta_.size(); ++j) {
Matrix A = lambda * eye(delta_[j].size());
Vector b = lambda * delta_[j];
SharedDiagonal model = noiseModel::Unit::Create(delta_[j].size());
GaussianFactor::shared_ptr prior(new JacobianFactor(j, A, b, model));
dampedFactorGraph.push_back(prior);
}
}
gttoc(damp);
result.lambdas++;
gttic(solve);
// Solve Damped Gaussian Factor Graph
newDelta = GaussianJunctionTree(dampedFactorGraph).optimize(parameters_.getEliminationFunction());
// update the evalpoint with the new delta
evalpoint = theta_.retract(newDelta, ordering_);
gttoc(solve);
// Evaluate the new error
gttic(compute_error);
double error = factors_.error(evalpoint);
gttoc(compute_error);
if(error < result.error) {
// Keep this change
// Update the error value
result.error = error;
// Update the linearization point
theta_ = evalpoint;
// Reset the deltas to zeros
delta_.setZero();
// Put the linearization points and deltas back for specific variables
if(separatorValues_.size() > 0) {
theta_.update(separatorValues_);
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, separatorValues_) {
Index index = ordering_.at(key_value.key);
delta_.at(index) = newDelta.at(index);
}
}
// Decrease lambda for next time
lambda /= lambdaFactor;
if(lambda < lambdaLowerBound) {
lambda = lambdaLowerBound;
}
// End this lambda search iteration
break;
} else {
// Reject this change
// Increase lambda and continue searching
lambda *= lambdaFactor;
if(lambda > lambdaUpperBound) {
// The maximum lambda has been used. Print a warning and end the search.
std::cout << "Warning: Levenberg-Marquardt giving up because cannot decrease error with maximum lambda" << std::endl;
break;
}
}
} // end while
}
gttoc(optimizer_iteration);
result.iterations++;
} while(result.iterations < maxIterations &&
!checkConvergence(relativeErrorTol, absoluteErrorTol, errorTol, previousError, result.error, NonlinearOptimizerParams::SILENT));
return result;
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::updateSmootherSummarization() {
// The smoother summarization factors are the resulting marginal factors on the separator
// variables that result from marginalizing out all of the other variables
// These marginal factors will be cached for later transmission to the filter using
// linear container factors
// Note: This assumes the ordering already has the separator variables eliminated last
// Clear out any existing smoother summarized factors
smootherSummarization_.resize(0);
// Create the linear factor graph
GaussianFactorGraph linearFactorGraph = *factors_.linearize(theta_, ordering_);
// Construct an elimination tree to perform sparse elimination
std::vector<EliminationForest::shared_ptr> forest( EliminationForest::Create(linearFactorGraph, variableIndex_) );
// This is a forest. Only the top-most node/index of each tree needs to be eliminated; all of the children will be eliminated automatically
// Find the subset of nodes/keys that must be eliminated
std::set<Index> indicesToEliminate;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, theta_) {
indicesToEliminate.insert(ordering_.at(key_value.key));
}
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, separatorValues_) {
indicesToEliminate.erase(ordering_.at(key_value.key));
}
std::vector<Index> indices(indicesToEliminate.begin(), indicesToEliminate.end());
BOOST_FOREACH(Index index, indices) {
EliminationForest::removeChildrenIndices(indicesToEliminate, forest.at(index));
}
// Eliminate each top-most key, returning a Gaussian Factor on some of the remaining variables
// Convert the marginal factors into Linear Container Factors and store
BOOST_FOREACH(Index index, indicesToEliminate) {
GaussianFactor::shared_ptr gaussianFactor = forest.at(index)->eliminateRecursive(parameters_.getEliminationFunction());
LinearContainerFactor::shared_ptr marginalFactor(new LinearContainerFactor(gaussianFactor, ordering_, theta_));
smootherSummarization_.push_back(marginalFactor);
}
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::PrintNonlinearFactor(const NonlinearFactor::shared_ptr& factor, const std::string& indent, const KeyFormatter& keyFormatter) {
std::cout << indent;
if(factor) {
if(boost::dynamic_pointer_cast<LinearContainerFactor>(factor)) {
std::cout << "l( ";
} else {
std::cout << "f( ";
}
BOOST_FOREACH(Key key, *factor) {
std::cout << keyFormatter(key) << " ";
}
std::cout << ")" << std::endl;
} else {
std::cout << "{ NULL }" << std::endl;
}
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::PrintLinearFactor(const GaussianFactor::shared_ptr& factor, const Ordering& ordering, const std::string& indent, const KeyFormatter& keyFormatter) {
std::cout << indent;
if(factor) {
std::cout << "g( ";
BOOST_FOREACH(Index index, *factor) {
std::cout << keyFormatter(ordering.key(index)) << " ";
}
std::cout << ")" << std::endl;
} else {
std::cout << "{ NULL }" << std::endl;
}
}
/* ************************************************************************* */
std::vector<Index> ConcurrentBatchSmoother::EliminationForest::ComputeParents(const VariableIndex& structure) {
// Number of factors and variables
const size_t m = structure.nFactors();
const size_t n = structure.size();
static const Index none = std::numeric_limits<Index>::max();
// Allocate result parent vector and vector of last factor columns
std::vector<Index> parents(n, none);
std::vector<Index> prevCol(m, none);
// for column j \in 1 to n do
for (Index j = 0; j < n; j++) {
// for row i \in Struct[A*j] do
BOOST_FOREACH(const size_t i, structure[j]) {
if (prevCol[i] != none) {
Index k = prevCol[i];
// find root r of the current tree that contains k
Index r = k;
while (parents[r] != none)
r = parents[r];
if (r != j) parents[r] = j;
}
prevCol[i] = j;
}
}
return parents;
}
/* ************************************************************************* */
std::vector<ConcurrentBatchSmoother::EliminationForest::shared_ptr> ConcurrentBatchSmoother::EliminationForest::Create(const GaussianFactorGraph& factorGraph, const VariableIndex& structure) {
// Compute the tree structure
std::vector<Index> parents(ComputeParents(structure));
// Number of variables
const size_t n = structure.size();
static const Index none = std::numeric_limits<Index>::max();
// Create tree structure
std::vector<shared_ptr> trees(n);
for (Index k = 1; k <= n; k++) {
Index j = n - k; // Start at the last variable and loop down to 0
trees[j].reset(new EliminationForest(j)); // Create a new node on this variable
if (parents[j] != none) // If this node has a parent, add it to the parent's children
trees[parents[j]]->add(trees[j]);
}
// Hang factors in right places
BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, factorGraph) {
if(factor && factor->size() > 0) {
Index j = *std::min_element(factor->begin(), factor->end());
if(j < structure.size())
trees[j]->add(factor);
}
}
return trees;
}
/* ************************************************************************* */
GaussianFactor::shared_ptr ConcurrentBatchSmoother::EliminationForest::eliminateRecursive(GaussianFactorGraph::Eliminate function) {
// Create the list of factors to be eliminated, initially empty, and reserve space
GaussianFactorGraph factors;
factors.reserve(this->factors_.size() + this->subTrees_.size());
// Add all factors associated with the current node
factors.push_back(this->factors_.begin(), this->factors_.end());
// for all subtrees, eliminate into Bayes net and a separator factor, added to [factors]
BOOST_FOREACH(const shared_ptr& child, subTrees_)
factors.push_back(child->eliminateRecursive(function));
// Combine all factors (from this node and from subtrees) into a joint factor
GaussianFactorGraph::EliminationResult eliminated(function(factors, 1));
return eliminated.second;
}
/* ************************************************************************* */
void ConcurrentBatchSmoother::EliminationForest::removeChildrenIndices(std::set<Index>& indices, const ConcurrentBatchSmoother::EliminationForest::shared_ptr& tree) {
BOOST_FOREACH(const EliminationForest::shared_ptr& child, tree->children()) {
indices.erase(child->key());
removeChildrenIndices(indices, child);
}
}
/* ************************************************************************* */
}/// namespace gtsam
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