140 lines
4.0 KiB
C++
140 lines
4.0 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file LinearEquality.h
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* @brief LinearEquality derived from Base with constrained noise model
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* @date Nov 27, 2014
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* @author Duy-Nguyen Ta
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*/
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#pragma once
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#include <gtsam/linear/JacobianFactor.h>
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namespace gtsam {
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/**
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* This class defines a linear equality constraints, inheriting JacobianFactor
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* with the special Constrained noise model
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*/
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class LinearEquality: public JacobianFactor {
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public:
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typedef LinearEquality This; ///< Typedef to this class
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typedef JacobianFactor Base; ///< Typedef to base class
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typedef boost::shared_ptr<This> shared_ptr; ///< shared_ptr to this class
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private:
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Key dualKey_;
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public:
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/** default constructor for I/O */
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LinearEquality() :
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Base() {
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}
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/**
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* Construct from a constrained noisemodel JacobianFactor with a dual key.
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*/
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explicit LinearEquality(const JacobianFactor& jf, Key dualKey) :
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Base(jf), dualKey_(dualKey) {
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if (!jf.isConstrained()) {
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throw std::runtime_error(
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"Cannot convert an unconstrained JacobianFactor to LinearEquality");
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}
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}
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/** Conversion from HessianFactor (does Cholesky to obtain Jacobian matrix) */
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explicit LinearEquality(const HessianFactor& hf) {
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throw std::runtime_error("Cannot convert HessianFactor to LinearEquality");
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}
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/** Construct unary factor */
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LinearEquality(Key i1, const Matrix& A1, const Vector& b, Key dualKey) :
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Base(i1, A1, b, noiseModel::Constrained::All(b.rows())), dualKey_(dualKey) {
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}
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/** Construct binary factor */
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LinearEquality(Key i1, const Matrix& A1, Key i2, const Matrix& A2,
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const Vector& b, Key dualKey) :
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Base(i1, A1, i2, A2, b, noiseModel::Constrained::All(b.rows())), dualKey_(
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dualKey) {
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}
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/** Construct ternary factor */
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LinearEquality(Key i1, const Matrix& A1, Key i2, const Matrix& A2, Key i3,
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const Matrix& A3, const Vector& b, Key dualKey) :
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Base(i1, A1, i2, A2, i3, A3, b, noiseModel::Constrained::All(b.rows())), dualKey_(
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dualKey) {
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}
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/** Construct an n-ary factor
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* @tparam TERMS A container whose value type is std::pair<Key, Matrix>, specifying the
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* collection of keys and matrices making up the factor. */
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template<typename TERMS>
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LinearEquality(const TERMS& terms, const Vector& b, Key dualKey) :
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Base(terms, b, noiseModel::Constrained::All(b.rows())), dualKey_(dualKey) {
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}
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/** Virtual destructor */
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virtual ~LinearEquality() {
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}
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/** equals */
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virtual bool equals(const GaussianFactor& lf, double tol = 1e-9) const {
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return Base::equals(lf, tol);
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}
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/** print */
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virtual void print(const std::string& s = "", const KeyFormatter& formatter =
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DefaultKeyFormatter) const {
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Base::print(s, formatter);
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}
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/** Clone this LinearEquality */
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virtual GaussianFactor::shared_ptr clone() const {
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return boost::static_pointer_cast < GaussianFactor
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> (boost::make_shared < LinearEquality > (*this));
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}
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/// dual key
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Key dualKey() const {
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return dualKey_;
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}
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/// for active set method: equality constraints are always active
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bool active() const {
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return true;
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}
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/** Special error_vector for constraints (A*x-b) */
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Vector error_vector(const VectorValues& c) const {
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return unweighted_error(c);
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}
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/** Special error for constraints.
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* I think it should be zero, as this function is meant for objective cost.
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* But the name "error" can be misleading.
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* TODO: confirm with Frank!! */
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virtual double error(const VectorValues& c) const {
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return 0.0;
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}
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};
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// \ LinearEquality
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/// traits
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template<> struct traits<LinearEquality> : public Testable<LinearEquality> {
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};
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} // \ namespace gtsam
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