275 lines
10 KiB
C++
275 lines
10 KiB
C++
/**
|
|
* NonlinearOptimizer-inl.h
|
|
* This is a template definition file, include it where needed (only!)
|
|
* so that the appropriate code is generated and link errors avoided.
|
|
* @brief: Encapsulates nonlinear optimization state
|
|
* @Author: Frank Dellaert
|
|
* Created on: Sep 7, 2009
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include <iostream>
|
|
#include <boost/tuple/tuple.hpp>
|
|
#include "NonlinearOptimizer.h"
|
|
|
|
#define INSTANTIATE_NONLINEAR_OPTIMIZER(G,C) \
|
|
template class NonlinearOptimizer<G,C>;
|
|
|
|
using namespace std;
|
|
|
|
namespace gtsam {
|
|
|
|
/* ************************************************************************* */
|
|
inline bool check_convergence(double relativeErrorTreshold,
|
|
double absoluteErrorTreshold, double currentError, double newError,
|
|
int verbosity) {
|
|
// check if diverges
|
|
double absoluteDecrease = currentError - newError;
|
|
if (verbosity >= 2) {
|
|
if (absoluteDecrease < absoluteErrorTreshold)
|
|
cout << "absoluteDecrease: " << absoluteDecrease << " < " << absoluteErrorTreshold << endl;
|
|
else
|
|
cout << "absoluteDecrease: " << absoluteDecrease << " >= " << absoluteErrorTreshold << endl;
|
|
}
|
|
|
|
// calculate relative error decrease and update currentError
|
|
double relativeDecrease = absoluteDecrease / currentError;
|
|
if (verbosity >= 2) {
|
|
if (relativeDecrease < relativeErrorTreshold)
|
|
cout << "relativeDecrease: " << relativeDecrease << " < " << relativeErrorTreshold << endl;
|
|
else
|
|
cout << "relativeDecrease: " << relativeDecrease << " >= " << relativeErrorTreshold << endl;
|
|
}
|
|
bool converged = (relativeDecrease < relativeErrorTreshold)
|
|
|| (absoluteDecrease < absoluteErrorTreshold);
|
|
if (verbosity >= 1 && converged)
|
|
cout << "converged" << endl;
|
|
return converged;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
NonlinearOptimizer<G, C, L, S, W>::NonlinearOptimizer(shared_graph graph,
|
|
shared_config config, shared_solver solver, double lambda) :
|
|
graph_(graph), config_(config), lambda_(lambda), solver_(solver) {
|
|
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");
|
|
error_ = graph->error(*config);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// linearize and optimize
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
VectorConfig 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);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// One iteration of Gauss Newton
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::iterate(
|
|
verbosityLevel verbosity) const {
|
|
// linearize and optimize
|
|
VectorConfig delta = linearizeAndOptimizeForDelta();
|
|
|
|
// maybe show output
|
|
if (verbosity >= DELTA)
|
|
delta.print("delta");
|
|
|
|
// take old config and update it
|
|
shared_config newConfig(new C(expmap(*config_,delta)));
|
|
|
|
// maybe show output
|
|
if (verbosity >= CONFIG)
|
|
newConfig->print("newConfig");
|
|
|
|
NonlinearOptimizer newOptimizer = NonlinearOptimizer(graph_, newConfig, solver_, lambda_);
|
|
|
|
if (verbosity >= ERROR)
|
|
cout << "error: " << newOptimizer.error_ << endl;
|
|
|
|
return newOptimizer;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::gaussNewton(
|
|
double relativeThreshold, double absoluteThreshold,
|
|
verbosityLevel verbosity, int maxIterations) const {
|
|
static W writer(error_);
|
|
|
|
// check if we're already close enough
|
|
if (error_ < absoluteThreshold) {
|
|
if (verbosity >= ERROR) cout << "Exiting, as error = " << error_
|
|
<< " < absoluteThreshold (" << absoluteThreshold << ")" << endl;
|
|
return *this;
|
|
}
|
|
|
|
// linearize, solve, update
|
|
NonlinearOptimizer next = iterate(verbosity);
|
|
|
|
writer.write(next.error_);
|
|
|
|
// check convergence
|
|
bool converged = gtsam::check_convergence(relativeThreshold,
|
|
absoluteThreshold, error_, next.error_, verbosity);
|
|
|
|
// return converged state or iterate
|
|
if (converged)
|
|
return next;
|
|
else
|
|
return next.gaussNewton(relativeThreshold, absoluteThreshold, verbosity);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// Recursively try to do tempered Gauss-Newton steps until we succeed.
|
|
// Form damped system with given lambda, and return a new, more optimistic
|
|
// optimizer if error decreased or recurse with a larger lambda if not.
|
|
// TODO: in theory we can't infinitely recurse, but maybe we should put a max.
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::try_lambda(
|
|
const L& linear, verbosityLevel verbosity, double factor, LambdaMode lambdaMode) const {
|
|
|
|
if (verbosity >= TRYLAMBDA)
|
|
cout << "trying lambda = " << lambda_ << endl;
|
|
|
|
// add prior-factors
|
|
L damped = linear.add_priors(1.0/sqrt(lambda_));
|
|
if (verbosity >= DAMPED)
|
|
damped.print("damped");
|
|
|
|
// solve
|
|
VectorConfig delta = solver_->optimize(damped);
|
|
if (verbosity >= TRYDELTA)
|
|
delta.print("delta");
|
|
|
|
// update config
|
|
shared_config newConfig(new C(expmap(*config_,delta))); // TODO: updateConfig
|
|
// if (verbosity >= TRYCONFIG)
|
|
// newConfig->print("config");
|
|
|
|
// create new optimization state with more adventurous lambda
|
|
NonlinearOptimizer next(graph_, newConfig, solver_, lambda_ / factor);
|
|
if (verbosity >= TRYLAMBDA) cout << "next error = " << next.error_ << endl;
|
|
|
|
if(lambdaMode >= CAUTIOUS) {
|
|
throw runtime_error("CAUTIOUS mode not working yet, please use BOUNDED.");
|
|
}
|
|
|
|
if(next.error_ <= error_) {
|
|
|
|
// If we're cautious, see if the current lambda is better
|
|
// todo: include stopping criterion here?
|
|
if(lambdaMode == CAUTIOUS) {
|
|
NonlinearOptimizer sameLambda(graph_, newConfig, solver_, lambda_);
|
|
if(sameLambda.error_ <= next.error_)
|
|
return sameLambda;
|
|
}
|
|
|
|
// Either we're not cautious, or we are but the adventerous lambda is better than the same one.
|
|
return next;
|
|
} else if (lambda_ > 1e+10) // if lambda gets too big, something is broken
|
|
throw runtime_error("Lambda has grown too large!");
|
|
else {
|
|
|
|
// A more adventerous lambda was worse. If we're cautious, try the same lambda.
|
|
if(lambdaMode == CAUTIOUS) {
|
|
NonlinearOptimizer sameLambda(graph_, newConfig, solver_, lambda_);
|
|
if(sameLambda.error_ <= error_)
|
|
return sameLambda;
|
|
}
|
|
|
|
// Either we're not cautious, or the same lambda was worse than the current error.
|
|
// The more adventerous lambda was worse too, so make lambda more conservative
|
|
// and keep the same config.
|
|
|
|
// TODO: can we avoid copying the config ?
|
|
if(lambdaMode >= BOUNDED && lambda_ >= 1.0e5) {
|
|
return NonlinearOptimizer(graph_, newConfig, solver_, lambda_);;
|
|
} else {
|
|
NonlinearOptimizer cautious(graph_, config_, solver_, lambda_ * factor);
|
|
return cautious.try_lambda(linear, verbosity, factor, lambdaMode);
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// One iteration of Levenberg Marquardt
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::iterateLM(
|
|
verbosityLevel verbosity, double lambdaFactor, LambdaMode lambdaMode) const {
|
|
|
|
// maybe show output
|
|
if (verbosity >= CONFIG)
|
|
config_->print("config");
|
|
if (verbosity >= ERROR)
|
|
cout << "error: " << error_ << endl;
|
|
if (verbosity >= LAMBDA)
|
|
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_);
|
|
if (verbosity >= LINEAR)
|
|
linear->print("linear");
|
|
|
|
// try lambda steps with successively larger lambda until we achieve descent
|
|
if (verbosity >= LAMBDA) cout << "Trying Lambda for the first time" << endl;
|
|
return prepared.try_lambda(*linear, verbosity, lambdaFactor, lambdaMode);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
template<class G, class C, class L, class S, class W>
|
|
NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::levenbergMarquardt(
|
|
double relativeThreshold, double absoluteThreshold,
|
|
verbosityLevel verbosity, int maxIterations, double lambdaFactor, LambdaMode lambdaMode) const {
|
|
|
|
if (maxIterations <= 0) return *this;
|
|
|
|
// check if we're already close enough
|
|
if (error_ < absoluteThreshold) {
|
|
if (verbosity >= ERROR) cout << "Exiting, as error = " << error_
|
|
<< " < absoluteThreshold (" << absoluteThreshold << ")" << endl;
|
|
return *this;
|
|
}
|
|
|
|
// do one iteration of LM
|
|
NonlinearOptimizer next = iterateLM(verbosity, lambdaFactor, lambdaMode);
|
|
|
|
// check convergence
|
|
// TODO: move convergence checks here and incorporate in verbosity levels
|
|
// TODO: build into iterations somehow as an instance variable
|
|
bool converged = gtsam::check_convergence(relativeThreshold,
|
|
absoluteThreshold, error_, next.error_, verbosity);
|
|
|
|
// return converged state or iterate
|
|
if (converged || maxIterations <= 1) {
|
|
// maybe show output
|
|
if (verbosity >= CONFIG)
|
|
next.config_->print("final config");
|
|
if (verbosity >= ERROR)
|
|
cout << "final error: " << next.error_ << endl;
|
|
if (verbosity >= LAMBDA)
|
|
cout << "final lambda = " << next.lambda_ << endl;
|
|
return next;
|
|
} else
|
|
return next.levenbergMarquardt(relativeThreshold, absoluteThreshold,
|
|
verbosity, maxIterations-1, lambdaFactor, lambdaMode);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
|
|
}
|