gtsam/gtsam_unstable/nonlinear/LCNLPSolver.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 LCNLPSolver.cpp
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* @author Duy-Nguyen Ta
* @author Krunal Chande
* @author Luca Carlone
* @date Dec 15, 2014
*/
#include <gtsam_unstable/nonlinear/LCNLPSolver.h>
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#include <gtsam_unstable/linear/QPSolver.h>
#include <iostream>
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namespace gtsam {
/* ************************************************************************* */
bool LCNLPSolver::isStationary(const VectorValues& delta) const {
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return delta.vector().lpNorm<Eigen::Infinity>() < errorTol;
}
/* ************************************************************************* */
bool LCNLPSolver::isPrimalFeasible(const LCNLPState& state) const {
return lcnlp_.linearEqualities.checkFeasibility(state.values, errorTol);
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}
/* ************************************************************************* */
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);
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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 ?
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return false;
}
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}
return true;
}
/* ************************************************************************* */
bool LCNLPSolver::isComplementary(const LCNLPState& state) const {
return lcnlp_.linearInequalities.checkFeasibilityAndComplimentary(
state.values, state.duals, errorTol);
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}
/* ************************************************************************* */
bool LCNLPSolver::checkConvergence(const LCNLPState& state,
const VectorValues& delta) const {
return isStationary(delta) && isPrimalFeasible(state)
&& isDualFeasible(state.duals) && isComplementary(state);
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}
/* ************************************************************************* */
VectorValues LCNLPSolver::initializeDuals() const {
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VectorValues duals;
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, lcnlp_.linearEqualities){
NonlinearConstraint::shared_ptr constraint
= boost::dynamic_pointer_cast<NonlinearConstraint>(factor);
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duals.insert(constraint->dualKey(), zero(factor->dim()));
}
return duals;
}
/* ************************************************************************* */
std::pair<Values, VectorValues> LCNLPSolver::optimize(
const Values& initialValues, bool useWarmStart, bool debug) const {
LCNLPState state(initialValues);
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state.duals = initializeDuals();
while (!state.converged && state.iterations < 100) {
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if (debug)
std::cout << "state: iteration " << state.iterations << std::endl;
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state = iterate(state, useWarmStart, debug);
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}
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if (debug)
std::cout << "Number of iterations: " << state.iterations << std::endl;
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return std::make_pair(state.values, state.duals);
}
/* ************************************************************************* */
LCNLPState LCNLPSolver::iterate(const LCNLPState& state, bool useWarmStart,
bool debug) const {
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// 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));
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// if (debug)
// qp.print("QP subproblem:");
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// solve the QP subproblem
VectorValues delta, duals;
QPSolver qpSolver(qp);
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,
useWarmStart);
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if (debug)
state.values.print("state.values: ");
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if (debug)
delta.print("delta = ");
// if (debug)
// duals.print("duals = ");
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// update new state
LCNLPState newState;
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newState.values = state.values.retract(delta);
newState.duals = duals;
newState.converged = checkConvergence(newState, delta);
newState.iterations = state.iterations + 1;
if (debug)
newState.print("newState: ");
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return newState;
}
} // namespace gtsam
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