308 lines
11 KiB
C++
308 lines
11 KiB
C++
/**
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* NonlinearOptimizer-inl.h
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* This is a template definition file, include it where needed (only!)
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* so that the appropriate code is generated and link errors avoided.
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* @brief: Encapsulates nonlinear optimization state
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* @Author: Frank Dellaert
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* Created on: Sep 7, 2009
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*/
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#pragma once
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#include <iostream>
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#include <boost/tuple/tuple.hpp>
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#include <gtsam/nonlinear/NonlinearOptimizer.h>
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#define INSTANTIATE_NONLINEAR_OPTIMIZER(G,C) \
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template class NonlinearOptimizer<G,C>;
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using namespace std;
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namespace gtsam {
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/* ************************************************************************* */
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inline bool check_convergence(
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double relativeErrorTreshold,
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double absoluteErrorTreshold,
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double errorThreshold,
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double currentError, double newError, int verbosity) {
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if ( verbosity >= 2 ) {
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if ( newError <= errorThreshold )
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cout << "errorThreshold: " << newError << " < " << errorThreshold << endl;
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else
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cout << "errorThreshold: " << newError << " > " << errorThreshold << endl;
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}
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if ( newError <= errorThreshold ) return true ;
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// check if diverges
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double absoluteDecrease = currentError - newError;
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if (verbosity >= 2) {
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if (absoluteDecrease < absoluteErrorTreshold)
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cout << "absoluteDecrease: " << absoluteDecrease << " < " << absoluteErrorTreshold << endl;
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else
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cout << "absoluteDecrease: " << absoluteDecrease << " >= " << absoluteErrorTreshold << endl;
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}
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// calculate relative error decrease and update currentError
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double relativeDecrease = absoluteDecrease / currentError;
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if (verbosity >= 2) {
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if (relativeDecrease < relativeErrorTreshold)
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cout << "relativeDecrease: " << relativeDecrease << " < " << relativeErrorTreshold << endl;
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else
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cout << "relativeDecrease: " << relativeDecrease << " >= " << relativeErrorTreshold << endl;
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}
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bool converged = (relativeDecrease < relativeErrorTreshold)
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|| (absoluteDecrease < absoluteErrorTreshold);
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if (verbosity >= 1 && converged)
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cout << "converged" << endl;
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return converged;
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}
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W>::NonlinearOptimizer(shared_graph graph,
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shared_values config, shared_solver solver, double lambda) :
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graph_(graph), config_(config), lambda_(lambda), solver_(solver) {
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if (!graph) throw std::invalid_argument(
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"NonlinearOptimizer constructor: graph = NULL");
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if (!config) throw std::invalid_argument(
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"NonlinearOptimizer constructor: config = NULL");
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if (!solver) throw std::invalid_argument(
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"NonlinearOptimizer constructor: solver = NULL");
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error_ = graph->error(*config);
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}
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/* ************************************************************************* */
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// linearize and optimize
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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VectorValues NonlinearOptimizer<G, C, L, S, W>::linearizeAndOptimizeForDelta() const {
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boost::shared_ptr<L> linearized = solver_->linearize(*graph_, *config_);
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NonlinearOptimizer prepared(graph_, config_, solver_->prepareLinear(*linearized), error_, lambda_);
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return prepared.solver_->optimize(*linearized);
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}
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/* ************************************************************************* */
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// One iteration of Gauss Newton
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::iterate(
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verbosityLevel verbosity) const {
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// linearize and optimize
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VectorValues delta = linearizeAndOptimizeForDelta();
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// maybe show output
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if (verbosity >= DELTA)
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delta.print("delta");
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// take old config and update it
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shared_values newValues(new C(solver_->expmap(*config_, delta)));
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// maybe show output
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if (verbosity >= CONFIG)
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newValues->print("newValues");
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NonlinearOptimizer newOptimizer = NonlinearOptimizer(graph_, newValues, solver_, lambda_);
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if (verbosity >= ERROR)
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cout << "error: " << newOptimizer.error_ << endl;
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return newOptimizer;
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}
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::gaussNewton(
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double relativeThreshold, double absoluteThreshold,
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verbosityLevel verbosity, int maxIterations) const {
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static W writer(error_);
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// check if we're already close enough
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if (error_ < absoluteThreshold) {
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if (verbosity >= ERROR) cout << "Exiting, as error = " << error_
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<< " < absoluteThreshold (" << absoluteThreshold << ")" << endl;
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return *this;
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}
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// linearize, solve, update
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NonlinearOptimizer next = iterate(verbosity);
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writer.write(next.error_);
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// check convergence
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bool converged = gtsam::check_convergence(
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relativeThreshold,
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absoluteThreshold,
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0.0,
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error_,
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next.error_,
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verbosity);
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// return converged state or iterate
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if (converged)
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return next;
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else
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return next.gaussNewton(relativeThreshold, absoluteThreshold, verbosity);
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}
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/* ************************************************************************* */
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// Recursively try to do tempered Gauss-Newton steps until we succeed.
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// Form damped system with given lambda, and return a new, more optimistic
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// optimizer if error decreased or recurse with a larger lambda if not.
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// TODO: in theory we can't infinitely recurse, but maybe we should put a max.
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::try_lambda(
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const L& linear, verbosityLevel verbosity, double factor, LambdaMode lambdaMode) const {
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if (verbosity >= TRYLAMBDA)
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cout << "trying lambda = " << lambda_ << endl;
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// add prior-factors
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L damped = linear.add_priors(1.0/sqrt(lambda_), GaussianVariableIndex<>(linear));
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if (verbosity >= DAMPED)
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damped.print("damped");
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// solve
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VectorValues delta = solver_->optimize(damped);
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if (verbosity >= TRYDELTA)
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delta.print("delta");
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// update config
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shared_values newValues(new C(solver_->expmap(*config_, delta))); // TODO: updateValues
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// if (verbosity >= TRYCONFIG)
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// newValues->print("config");
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// create new optimization state with more adventurous lambda
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NonlinearOptimizer next(graph_, newValues, solver_, lambda_ / factor);
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if (verbosity >= TRYLAMBDA) cout << "next error = " << next.error_ << endl;
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if(lambdaMode >= CAUTIOUS) {
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throw runtime_error("CAUTIOUS mode not working yet, please use BOUNDED.");
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}
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if(next.error_ <= error_) {
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// If we're cautious, see if the current lambda is better
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// todo: include stopping criterion here?
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if(lambdaMode == CAUTIOUS) {
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NonlinearOptimizer sameLambda(graph_, newValues, solver_, lambda_);
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if(sameLambda.error_ <= next.error_)
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return sameLambda;
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}
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// Either we're not cautious, or we are but the adventerous lambda is better than the same one.
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return next;
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} else if (lambda_ > 1e+10) // if lambda gets too big, something is broken
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throw runtime_error("Lambda has grown too large!");
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else {
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// A more adventerous lambda was worse. If we're cautious, try the same lambda.
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if(lambdaMode == CAUTIOUS) {
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NonlinearOptimizer sameLambda(graph_, newValues, solver_, lambda_);
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if(sameLambda.error_ <= error_)
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return sameLambda;
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}
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// Either we're not cautious, or the same lambda was worse than the current error.
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// The more adventerous lambda was worse too, so make lambda more conservative
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// and keep the same config.
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// TODO: can we avoid copying the config ?
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if(lambdaMode >= BOUNDED && lambda_ >= 1.0e5) {
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return NonlinearOptimizer(graph_, newValues, solver_, lambda_);;
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} else {
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NonlinearOptimizer cautious(graph_, config_, solver_, lambda_ * factor);
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return cautious.try_lambda(linear, verbosity, factor, lambdaMode);
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}
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}
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}
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/* ************************************************************************* */
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// One iteration of Levenberg Marquardt
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::iterateLM(
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verbosityLevel verbosity, double lambdaFactor, LambdaMode lambdaMode) const {
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// maybe show output
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if (verbosity >= CONFIG)
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config_->print("config");
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if (verbosity >= ERROR)
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cout << "error: " << error_ << endl;
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if (verbosity >= LAMBDA)
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cout << "lambda = " << lambda_ << endl;
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// linearize all factors once
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boost::shared_ptr<L> linear = solver_->linearize(*graph_, *config_);
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NonlinearOptimizer prepared(graph_, config_, solver_->prepareLinear(*linear), error_, lambda_);
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if (verbosity >= LINEAR)
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linear->print("linear");
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// try lambda steps with successively larger lambda until we achieve descent
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if (verbosity >= LAMBDA) cout << "Trying Lambda for the first time" << endl;
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return prepared.try_lambda(*linear, verbosity, lambdaFactor, lambdaMode);
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}
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/* ************************************************************************* */
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::levenbergMarquardt(
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double relativeThreshold, double absoluteThreshold,
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verbosityLevel verbosity, int maxIterations, double lambdaFactor, LambdaMode lambdaMode) const {
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return levenbergMarquardt(NonLinearOptimizerPara (absoluteThreshold, relativeThreshold, absoluteThreshold,
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maxIterations, lambdaFactor, verbosity, lambdaMode)) ;
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}
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template<class G, class C, class L, class S, class W>
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NonlinearOptimizer<G, C, L, S, W> NonlinearOptimizer<G, C, L, S, W>::
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levenbergMarquardt(const NonLinearOptimizerPara ¶) const {
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if (para.maxIterations_ <= 0) return *this;
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// check if we're already close enough
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if (error_ < para.sumError_) {
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if (para.verbosity_ >= ERROR)
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cout << "Exiting, as error = " << error_ << " < " << para.sumError_ << endl;
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return *this;
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}
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// do one iteration of LM
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NonlinearOptimizer next = iterateLM(para.verbosity_, para.lambdaFactor_, para.lambdaMode_);
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// check convergence
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// TODO: move convergence checks here and incorporate in verbosity levels
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// TODO: build into iterations somehow as an instance variable
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bool converged = gtsam::check_convergence(
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para.relDecrease_,
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para.absDecrease_,
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para.sumError_,
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error_,
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next.error_,
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para.verbosity_);
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// return converged state or iterate
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if (converged || para.maxIterations_ <= 1) {
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// maybe show output
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if (para.verbosity_ >= CONFIG)
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next.config_->print("final config");
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if (para.verbosity_ >= ERROR)
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cout << "final error: " << next.error_ << endl;
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if (para.verbosity_ >= LAMBDA)
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cout << "final lambda = " << next.lambda_ << endl;
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return next;
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} else {
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NonLinearOptimizerPara newPara = para ;
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newPara.maxIterations_ = newPara.maxIterations_ - 1;
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return next.levenbergMarquardt(newPara) ;
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}
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}
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/* ************************************************************************* */
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}
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