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Removed remaining references to denseQR, some fixes in NonlinearOptimizer

release/4.3a0
Chris Beall 2010-10-22 01:46:33 +00:00
parent 3b09594a3c
commit acde4d99a5
9 changed files with 99 additions and 80 deletions
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4
examples/Makefile.am
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@ -16,8 +16,8 @@ noinst_PROGRAMS += PlanarSLAMExample_easy # Solves SLAM example from tutorial
noinst_PROGRAMS += PlanarSLAMSelfContained_advanced # Solves SLAM example from tutorial with all typedefs in the script
noinst_PROGRAMS += Pose2SLAMExample_easy # Solves SLAM example from tutorial by using Pose2SLAM and easy optimization interface
noinst_PROGRAMS += Pose2SLAMExample_advanced # Solves SLAM example from tutorial by using Pose2SLAM and advanced optimization interface
noinst_PROGRAMS += Pose2SLAMwSPCG_easy # Solves a simple Pose2 SLAM example with advanced SPCG solver
noinst_PROGRAMS += Pose2SLAMwSPCG_advanced # Solves a simple Pose2 SLAM example with easy SPCG solver
#noinst_PROGRAMS += Pose2SLAMwSPCG_easy # Solves a simple Pose2 SLAM example with advanced SPCG solver
#noinst_PROGRAMS += Pose2SLAMwSPCG_advanced # Solves a simple Pose2 SLAM example with easy SPCG solver
SUBDIRS = vSLAMexample # does not compile....
#----------------------------------------------------------------------------------------------------
# rules to build local programs

3
examples/Pose2SLAMExample_advanced.cpp
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@ -66,8 +66,7 @@ int main(int argc, char** argv) {
Ordering::shared_ptr ordering = graph->orderingCOLAMD(*initial);
/* 4.2.2 set up solver and optimize */
Optimizer::shared_solver solver(new Optimizer::solver(ordering));
Optimizer optimizer(graph, initial, solver);
Optimizer optimizer(graph, initial, ordering);
Optimizer::Parameters::verbosityLevel verbosity = pose2SLAM::Optimizer::Parameters::SILENT;
Optimizer optimizer_result = optimizer.levenbergMarquardt(1e-15, 1e-15, verbosity);

2
linear/GaussianMultifrontalSolver.cpp
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@ -16,7 +16,7 @@ GaussianMultifrontalSolver::GaussianMultifrontalSolver(const FactorGraph<Gaussia
junctionTree_(new GaussianJunctionTree(factorGraph)) {}
/* ************************************************************************* */
typename BayesTree<GaussianConditional>::sharedClique GaussianMultifrontalSolver::eliminate() const {
BayesTree<GaussianConditional>::sharedClique GaussianMultifrontalSolver::eliminate() const {
return junctionTree_->eliminate();
}

2
linear/GaussianMultifrontalSolver.h
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@ -56,7 +56,7 @@ public:
* Eliminate the factor graph sequentially. Uses a column elimination tree
* to recursively eliminate.
*/
typename BayesTree<GaussianConditional>::sharedClique eliminate() const;
BayesTree<GaussianConditional>::sharedClique eliminate() const;
/**
* Compute the least-squares solution of the GaussianFactorGraph. This

52
linear/NoiseModel.cpp
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@ -31,7 +31,6 @@
#include <gtsam/linear/NoiseModel.h>
#include <gtsam/linear/SharedDiagonal.h>
#include <gtsam/base/DenseQRUtil.h>
namespace ublas = boost::numeric::ublas;
typedef ublas::matrix_column<Matrix> column;
@ -220,23 +219,28 @@ SharedDiagonal Gaussian::QR(Matrix& Ab, boost::optional<std::vector<long>&> firs
// General QR, see also special version in Constrained
SharedDiagonal Gaussian::QRColumnWise(ublas::matrix<double, ublas::column_major>& Ab, vector<long>& firstZeroRows) const {
// get size(A) and maxRank
// TODO: really no rank problems ?
size_t m = Ab.size1(), n = Ab.size2()-1;
size_t maxRank = min(m,n);
// pre-whiten everything (cheaply if possible)
WhitenInPlace(Ab);
// Perform in-place Householder
#ifdef GT_USE_LAPACK
householder_denseqr_colmajor(Ab, &firstZeroRows[0]);
#else
householder(Ab, maxRank);
#endif
return Unit::Create(maxRank);
Matrix Abresult(Ab);
gtsam::print(Abresult, "Abresult before = ");
SharedDiagonal result = QR(Abresult, firstZeroRows);
gtsam::print(Abresult, "Abresult after = ");
Ab = Abresult;
return result;
// // get size(A) and maxRank
// // TODO: really no rank problems ?
// size_t m = Ab.size1(), n = Ab.size2()-1;
// size_t maxRank = min(m,n);
//
// // pre-whiten everything (cheaply if possible)
// WhitenInPlace(Ab);
//
// // Perform in-place Householder
//#ifdef GT_USE_LAPACK
// householder_denseqr_colmajor(Ab, &firstZeroRows[0]);
//#else
// householder(Ab, maxRank);
//#endif
//
// return Unit::Create(maxRank);
}
/* ************************************************************************* */
@ -400,10 +404,14 @@ SharedDiagonal Constrained::QR(Matrix& Ab, boost::optional<std::vector<long>&> f
}
SharedDiagonal Constrained::QRColumnWise(ublas::matrix<double, ublas::column_major>& Ab, vector<long>& firstZeroRows) const {
Matrix AbRowWise(Ab);
SharedDiagonal result = this->QR(AbRowWise, firstZeroRows);
Ab = AbRowWise;
return result;
// Matrix AbRowWise(Ab);
// SharedDiagonal result = this->QR(AbRowWise, firstZeroRows);
// Ab = AbRowWise;
// return result;
Matrix Abresult(Ab);
SharedDiagonal result = QR(Abresult, firstZeroRows);
Ab = Abresult;
return result;
}
/* ************************************************************************* */

1
nonlinear/LieValues.h
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@ -119,7 +119,6 @@ namespace gtsam {
/** Add a delta config to current config and returns a new config */
LieValues expmap(const VectorValues& delta, const Ordering& ordering) const;
/** Get a delta config about a linearization point c0 (*this) */
VectorValues logmap(const LieValues& cp, const Ordering& ordering) const;

47
nonlinear/NonlinearOptimization-inl.h
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@ -19,7 +19,8 @@
#pragma once
#include <gtsam/linear/Factorization.h>
#include <gtsam/linear/GaussianSequentialSolver.h>
#include <gtsam/linear/GaussianMultifrontalSolver.h>
#include <gtsam/linear/SubgraphSolver-inl.h>
#include <gtsam/nonlinear/NonlinearOptimizer-inl.h>
#include <gtsam/nonlinear/NonlinearOptimization.h>
@ -38,10 +39,9 @@ namespace gtsam {
Ordering::shared_ptr ordering = graph.orderingCOLAMD(initialEstimate);
// initial optimization state is the same in both cases tested
typedef NonlinearOptimizer<G, T> Optimizer;
typename Optimizer::shared_solver solver(new Factorization<G, T>(ordering));
typedef NonlinearOptimizer<G, T, GaussianFactorGraph, GaussianSequentialSolver> Optimizer;
Optimizer optimizer(boost::make_shared<const G>(graph),
boost::make_shared<const T>(initialEstimate), solver);
boost::make_shared<const T>(initialEstimate), ordering);
// Levenberg-Marquardt
Optimizer result = optimizer.levenbergMarquardt(parameters);
@ -53,28 +53,39 @@ namespace gtsam {
*/
template<class G, class T>
T optimizeMultiFrontal(const G& graph, const T& initialEstimate, const NonlinearOptimizationParameters& parameters) {
throw runtime_error("optimizeMultiFrontal: not implemented");
}
/**
* The multifrontal solver
*/
template<class G, class T>
T optimizeSPCG(const G& graph, const T& initialEstimate, const NonlinearOptimizationParameters& parameters = NonlinearOptimizationParameters()) {
// Use a variable ordering from COLAMD
Ordering::shared_ptr ordering = graph.orderingCOLAMD(initialEstimate);
// initial optimization state is the same in both cases tested
typedef NonlinearOptimizer<G, T, SubgraphPreconditioner, SubgraphSolver<G,T> > SPCGOptimizer;
typename SPCGOptimizer::shared_solver solver(new SubgraphSolver<G,T>(graph, initialEstimate));
SPCGOptimizer optimizer(
boost::make_shared<const G>(graph),
boost::make_shared<const T>(initialEstimate),
solver);
typedef NonlinearOptimizer<G, T, GaussianFactorGraph, GaussianMultifrontalSolver> Optimizer;
Optimizer optimizer(boost::make_shared<const G>(graph),
boost::make_shared<const T>(initialEstimate), ordering);
// Levenberg-Marquardt
SPCGOptimizer result = optimizer.levenbergMarquardt(parameters);
Optimizer result = optimizer.levenbergMarquardt(parameters);
return *result.config();
}
// /**
// * The multifrontal solver
// */
// template<class G, class T>
// T optimizeSPCG(const G& graph, const T& initialEstimate, const NonlinearOptimizationParameters& parameters = NonlinearOptimizationParameters()) {
//
// // initial optimization state is the same in both cases tested
// typedef NonlinearOptimizer<G, T, SubgraphPreconditioner, SubgraphSolver<G,T> > SPCGOptimizer;
// typename SPCGOptimizer::shared_solver solver(new SubgraphSolver<G,T>(graph, initialEstimate));
// SPCGOptimizer optimizer(
// boost::make_shared<const G>(graph),
// boost::make_shared<const T>(initialEstimate),
// solver);
//
// // Levenberg-Marquardt
// SPCGOptimizer result = optimizer.levenbergMarquardt(parameters);
// return *result.config();
// }
/**
* optimization that returns the values
*/

36
nonlinear/NonlinearOptimizer-inl.h
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@ -74,14 +74,14 @@ namespace gtsam {
/* ************************************************************************* */
template<class G, class C, class L, class S, class W>
NonlinearOptimizer<G, C, L, S, W>::NonlinearOptimizer(shared_graph graph,
shared_values config, shared_solver solver, double lambda) :
graph_(graph), config_(config), lambda_(lambda), solver_(solver) {
shared_values config, shared_ordering ordering, double lambda) :
graph_(graph), config_(config), ordering_(ordering), lambda_(lambda) {
if (!graph) throw std::invalid_argument(
"NonlinearOptimizer constructor: graph = NULL");
if (!config) throw std::invalid_argument(
"NonlinearOptimizer constructor: config = NULL");
if (!solver) throw std::invalid_argument(
"NonlinearOptimizer constructor: solver = NULL");
if (!ordering) throw std::invalid_argument(
"NonlinearOptimizer constructor: ordering = NULL");
error_ = graph->error(*config);
}
@ -90,9 +90,9 @@ namespace gtsam {
/* ************************************************************************* */
template<class G, class C, class L, class S, class W>
VectorValues NonlinearOptimizer<G, C, L, S, W>::linearizeAndOptimizeForDelta() const {
boost::shared_ptr<L> linearized = solver_->linearize(*graph_, *config_);
NonlinearOptimizer prepared(graph_, config_, solver_->prepareLinear(*linearized), error_, lambda_);
return prepared.solver_->optimize(*linearized);
boost::shared_ptr<L> linearized = graph_->linearize(*config_, *ordering_);
NonlinearOptimizer prepared(graph_, config_, ordering_, error_, lambda_);
return *S(*linearized).optimize();
}
/* ************************************************************************* */
@ -109,13 +109,13 @@ namespace gtsam {
delta.print("delta");
// take old config and update it
shared_values newValues(new C(solver_->expmap(*config_, delta)));
shared_values newValues(new C(config_->expmap(delta, *ordering_)));
// maybe show output
if (verbosity >= Parameters::CONFIG)
newValues->print("newValues");
NonlinearOptimizer newOptimizer = NonlinearOptimizer(graph_, newValues, solver_, lambda_);
NonlinearOptimizer newOptimizer = NonlinearOptimizer(graph_, newValues, ordering_, lambda_);
if (verbosity >= Parameters::ERROR)
cout << "error: " << newOptimizer.error_ << endl;
@ -177,17 +177,17 @@ namespace gtsam {
damped.print("damped");
// solve
VectorValues delta = solver_->optimize(damped);
VectorValues delta = *S(damped).optimize();
if (verbosity >= Parameters::TRYDELTA)
delta.print("delta");
// update config
shared_values newValues(new C(solver_->expmap(*config_, delta))); // TODO: updateValues
shared_values newValues(new C(config_->expmap(delta, *ordering_))); // TODO: updateValues
// if (verbosity >= TRYCONFIG)
// newValues->print("config");
// create new optimization state with more adventurous lambda
NonlinearOptimizer next(graph_, newValues, solver_, lambda_ / factor);
NonlinearOptimizer next(graph_, newValues, ordering_, lambda_ / factor);
if (verbosity >= Parameters::TRYLAMBDA) cout << "next error = " << next.error_ << endl;
if(lambdaMode >= Parameters::CAUTIOUS) {
@ -199,7 +199,7 @@ namespace gtsam {
// If we're cautious, see if the current lambda is better
// todo: include stopping criterion here?
if(lambdaMode == Parameters::CAUTIOUS) {
NonlinearOptimizer sameLambda(graph_, newValues, solver_, lambda_);
NonlinearOptimizer sameLambda(graph_, newValues, ordering_, lambda_);
if(sameLambda.error_ <= next.error_)
return sameLambda;
}
@ -212,7 +212,7 @@ namespace gtsam {
// A more adventerous lambda was worse. If we're cautious, try the same lambda.
if(lambdaMode == Parameters::CAUTIOUS) {
NonlinearOptimizer sameLambda(graph_, newValues, solver_, lambda_);
NonlinearOptimizer sameLambda(graph_, newValues, ordering_, lambda_);
if(sameLambda.error_ <= error_)
return sameLambda;
}
@ -223,9 +223,9 @@ namespace gtsam {
// TODO: can we avoid copying the config ?
if(lambdaMode >= Parameters::BOUNDED && lambda_ >= 1.0e5) {
return NonlinearOptimizer(graph_, newValues, solver_, lambda_);;
return NonlinearOptimizer(graph_, newValues, ordering_, lambda_);;
} else {
NonlinearOptimizer cautious(graph_, config_, solver_, lambda_ * factor);
NonlinearOptimizer cautious(graph_, config_, ordering_, lambda_ * factor);
return cautious.try_lambda(linear, verbosity, factor, lambdaMode);
}
@ -248,8 +248,8 @@ namespace gtsam {
cout << "lambda = " << lambda_ << endl;
// linearize all factors once
boost::shared_ptr<L> linear = solver_->linearize(*graph_, *config_);
NonlinearOptimizer prepared(graph_, config_, solver_->prepareLinear(*linear), error_, lambda_);
boost::shared_ptr<L> linear = graph_->linearize(*config_, *ordering_);
NonlinearOptimizer prepared(graph_, config_, ordering_, error_, lambda_);
if (verbosity >= Parameters::LINEAR)
linear->print("linear");

32
nonlinear/NonlinearOptimizer.h
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@ -63,15 +63,16 @@ namespace gtsam {
*
*
*/
template<class G, class T, class L = GaussianFactorGraph, class S = Factorization<G, T>, class W = NullOptimizerWriter>
template<class G, class T, class L = GaussianFactorGraph, class GS = GaussianSequentialSolver, class W = NullOptimizerWriter>
class NonlinearOptimizer {
public:
// For performance reasons in recursion, we store configs in a shared_ptr
typedef boost::shared_ptr<const T> shared_values;
typedef boost::shared_ptr<const G> shared_graph;
typedef boost::shared_ptr<const S> shared_solver;
typedef const S solver;
typedef boost::shared_ptr<Ordering> shared_ordering;
//typedef boost::shared_ptr<const GS> shared_solver;
//typedef const GS solver;
typedef NonlinearOptimizationParameters Parameters;
private:
@ -85,13 +86,14 @@ namespace gtsam {
const shared_values config_;
double error_; // TODO FD: no more const because in constructor I need to set it after checking :-(
const shared_ordering ordering_;
// the linear system solver
//const shared_solver solver_;
// keep current lambda for use within LM only
// TODO: red flag, should we have an LM class ?
const double lambda_;
// the linear system solver
const shared_solver solver_;
// Recursively try to do tempered Gauss-Newton steps until we succeed
NonlinearOptimizer try_lambda(const L& linear,
Parameters::verbosityLevel verbosity, double factor, Parameters::LambdaMode lambdaMode) const;
@ -101,22 +103,22 @@ namespace gtsam {
/**
* Constructor that evaluates new error
*/
NonlinearOptimizer(shared_graph graph, shared_values config, shared_solver solver,
NonlinearOptimizer(shared_graph graph, shared_values config, shared_ordering ordering,
const double lambda = 1e-5);
/**
* Constructor that does not do any computation
*/
NonlinearOptimizer(shared_graph graph, shared_values config, shared_solver solver,
NonlinearOptimizer(shared_graph graph, shared_values config, shared_ordering ordering,
const double error, const double lambda): graph_(graph), config_(config),
error_(error), lambda_(lambda), solver_(solver) {}
error_(error), ordering_(ordering), lambda_(lambda) {}
/**
* Copy constructor
*/
NonlinearOptimizer(const NonlinearOptimizer<G, T, L, S> &optimizer) :
NonlinearOptimizer(const NonlinearOptimizer<G, T, L, GS> &optimizer) :
graph_(optimizer.graph_), config_(optimizer.config_),
error_(optimizer.error_), lambda_(optimizer.lambda_), solver_(optimizer.solver_) {}
error_(optimizer.error_), ordering_(optimizer.ordering_), lambda_(optimizer.lambda_) {}
/**
* Return current error
@ -205,8 +207,8 @@ namespace gtsam {
double relativeThreshold = 1e-5, absoluteThreshold = 1e-5;
// initial optimization state is the same in both cases tested
shared_solver solver(new S(ordering));
NonlinearOptimizer optimizer(graph, config, solver);
GS solver(*graph->linearize(*config, *ordering));
NonlinearOptimizer optimizer(graph, config, ordering);
// Levenberg-Marquardt
NonlinearOptimizer result = optimizer.levenbergMarquardt(relativeThreshold,
@ -236,8 +238,8 @@ namespace gtsam {
double relativeThreshold = 1e-5, absoluteThreshold = 1e-5;
// initial optimization state is the same in both cases tested
shared_solver solver(new S(ordering));
NonlinearOptimizer optimizer(graph, config, solver);
GS solver(*graph->linearize(*config, *ordering));
NonlinearOptimizer optimizer(graph, config, ordering);
// Gauss-Newton
NonlinearOptimizer result = optimizer.gaussNewton(relativeThreshold,