gtsam/gtsam_unstable/nonlinear/LCNLPSolver.cpp

133 lines
4.5 KiB
C++

/* ----------------------------------------------------------------------------
* 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 LCNLPSolver.cpp
* @author Duy-Nguyen Ta
* @author Krunal Chande
* @author Luca Carlone
* @date Dec 15, 2014
*/
#include <gtsam_unstable/nonlinear/LCNLPSolver.h>
#include <gtsam_unstable/linear/QPSolver.h>
#include <iostream>
namespace gtsam {
/* ************************************************************************* */
bool LCNLPSolver::isStationary(const VectorValues& delta) const {
return delta.vector().lpNorm<Eigen::Infinity>() < errorTol;
}
/* ************************************************************************* */
bool LCNLPSolver::isPrimalFeasible(const LCNLPState& state) const {
return lcnlp_.linearEqualities.checkFeasibility(state.values, errorTol);
}
/* ************************************************************************* */
bool LCNLPSolver::isDualFeasible(const VectorValues& duals) const {
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, lcnlp_.linearInequalities) {
NonlinearConstraint::shared_ptr inequality = boost::dynamic_pointer_cast<NonlinearConstraint>(factor);
Key dualKey = inequality->dualKey();
if (!duals.exists(dualKey)) continue; // should be inactive constraint!
double dual = duals.at(dualKey)[0]; // because we only support single-valued inequalities
if (dual > 0.0) { // See the explanation in QPSolver::identifyLeavingConstraint, we want dual < 0 ?
return false;
}
}
return true;
}
/* ************************************************************************* */
bool LCNLPSolver::isComplementary(const LCNLPState& state) const {
return lcnlp_.linearInequalities.checkFeasibilityAndComplimentary(state.values, state.duals, errorTol);
}
/* ************************************************************************* */
bool LCNLPSolver::checkConvergence(const LCNLPState& state, const VectorValues& delta) const {
return isStationary(delta) && isPrimalFeasible(state) && isDualFeasible(state.duals) && isComplementary(state);
}
/* ************************************************************************* */
VectorValues LCNLPSolver::initializeDuals() const {
VectorValues duals;
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, lcnlp_.linearEqualities) {
NonlinearConstraint::shared_ptr constraint = boost::dynamic_pointer_cast<NonlinearConstraint>(factor);
duals.insert(constraint->dualKey(), zero(factor->dim()));
}
return duals;
}
/* ************************************************************************* */
std::pair<Values, VectorValues> LCNLPSolver::optimize(const Values& initialValues) const {
LCNLPState state(initialValues);
state.duals = initializeDuals();
while (!state.converged && state.iterations < 100) {
state = iterate(state);
}
std::cout << "Number of iterations: " << state.iterations << std::endl;
return std::make_pair(state.values, state.duals);
}
/* ************************************************************************* */
LCNLPState LCNLPSolver::iterate(const LCNLPState& state) const {
static const bool debug = true;
// construct the qp subproblem
QP qp;
qp.cost = *lcnlp_.cost.linearize(state.values);
qp.equalities.add(*lcnlp_.linearEqualities.linearize(state.values));
qp.inequalities.add(*lcnlp_.linearInequalities.linearize(state.values));
// if (debug)
// qp.print("QP subproblem:");
// solve the QP subproblem
VectorValues delta, duals;
QPSolver qpSolver(qp);
if (state.iterations == 0)
boost::tie(delta, duals) = qpSolver.optimize();
else {
VectorValues zeroInitialValues;
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, state.values) {
zeroInitialValues.insert(key_value.key, zero(key_value.value.dim()));
}
boost::tie(delta, duals) = qpSolver.optimize(zeroInitialValues, state.duals);
}
if (debug)
state.values.print("state.values: ");
if (debug)
delta.print("delta = ");
// if (debug)
// duals.print("duals = ");
// update new state
LCNLPState newState;
newState.values = state.values.retract(delta);
newState.duals = duals;
newState.converged = checkConvergence(newState, delta);
newState.iterations = state.iterations + 1;
if (debug)
newState.print("newState: ");
return newState;
}
}