gtsam/cpp/NonlinearConstraint-inl.h

61 lines
1.7 KiB
C++

/*
* @file NonlinearConstraint-inl.h
* @brief Implementation for NonlinearConstraints
* @author alexgc
*/
#pragma once
#include <iostream>
#include "NonlinearConstraint.h"
namespace gtsam {
/**
* Implementations of unary nonlinear constraints
*/
template <class Config>
void NonlinearConstraint1<Config>::print(const std::string& s) const {
std::cout << "NonlinearConstraint [" << s << "]:\n"
<< " Variable: " << key_ << "\n"
<< " p: " << this->p_ << "\n"
<< " lambda key: " << this->lagrange_key_ << std::endl;
}
template <class Config>
bool NonlinearConstraint1<Config>::equals(const Factor<Config>& f, double tol) const {
const NonlinearConstraint1<Config>* p = dynamic_cast<const NonlinearConstraint1<Config>*> (&f);
if (p == NULL) return false;
if (key_ != p->key_) return false;
if (this->lagrange_key_ != p->lagrange_key_) return false;
if (g_ != p->g_) return false;
if (gradG_ != p->gradG_) return false;
return this->p_ == p->p_;
}
template <class Config>
std::pair<GaussianFactor::shared_ptr, GaussianFactor::shared_ptr>
NonlinearConstraint1<Config>::linearize(const Config& config, const VectorConfig& lagrange) const {
// extract lagrange multiplier
Vector lambda = lagrange[this->lagrange_key_];
// find the error
Vector g = g_(config, key_);
// construct the gradient
Matrix grad = gradG_(config, key_);
// construct probabilistic factor
Matrix A1 = vector_scale(grad, lambda);
GaussianFactor::shared_ptr factor(new
GaussianFactor(key_, A1, this->lagrange_key_, eye(this->p_), zero(this->p_), 1.0));
// construct the constraint
GaussianFactor::shared_ptr constraint(new GaussianFactor("x", grad, g, 0.0));
return std::make_pair(factor, constraint);
}
}