61 lines
1.9 KiB
C++
61 lines
1.9 KiB
C++
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/*
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* BayesNetPreconditioner.cpp
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* Created on: Dec 31, 2009
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* @Author: Frank Dellaert
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*/
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#include <boost/foreach.hpp>
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#include "BayesNetPreconditioner.h"
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namespace gtsam {
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/* ************************************************************************* */
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BayesNetPreconditioner::BayesNetPreconditioner(const GaussianFactorGraph& Ab,
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const GaussianBayesNet& Rd) :
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Ab_(Ab), Rd_(Rd) {
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}
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/* ************************************************************************* */
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// R*x = y by solving x=inv(R)*y
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VectorConfig BayesNetPreconditioner::backSubstitute(const VectorConfig& y) const {
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return gtsam::backSubstitute(Rd_, y);
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}
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/* ************************************************************************* */
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// gy=inv(L)*gx by solving L*gy=gx.
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VectorConfig BayesNetPreconditioner::backSubstituteTranspose(
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const VectorConfig& gx) const {
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return gtsam::backSubstituteTranspose(Rd_, gx);
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}
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/* ************************************************************************* */
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double BayesNetPreconditioner::error(const VectorConfig& y) const {
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return Ab_.error(x(y));
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}
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/* ************************************************************************* */
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// gradient is inv(R')*A'*(A*inv(R)*y-b),
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VectorConfig BayesNetPreconditioner::gradient(const VectorConfig& y) const {
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VectorConfig gx = Ab_ ^ Ab_.errors(x(y));
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return gtsam::backSubstituteTranspose(Rd_, gx);
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}
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/* ************************************************************************* */
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// Apply operator *
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Errors BayesNetPreconditioner::operator*(const VectorConfig& y) const {
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return Ab_ * x(y);
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}
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/* ************************************************************************* */
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// Apply operator inv(R')*A'*e
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VectorConfig BayesNetPreconditioner::operator^(const Errors& e) const {
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VectorConfig x = Ab_ ^ e; // x = A'*e2
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return gtsam::backSubstituteTranspose(Rd_, x);
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}
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/* ************************************************************************* */
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} // namespace gtsam
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