gtsam/gtsam/linear/RegularHessianFactor.h

218 lines
7.4 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 RegularHessianFactor.h
* @brief HessianFactor class with constant sized blocks
* @author Sungtae An
* @date March 2014
*/
#pragma once
#include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/RegularJacobianFactor.h>
#include <vector>
namespace gtsam {
template<size_t D>
class RegularHessianFactor: public HessianFactor {
public:
typedef Eigen::Matrix<double, D, 1> VectorD;
typedef Eigen::Matrix<double, D, D> MatrixD;
/** Construct an n-way factor. Gs contains the upper-triangle blocks of the
* quadratic term (the Hessian matrix) provided in row-order, gs the pieces
* of the linear vector term, and f the constant term.
*/
RegularHessianFactor(const KeyVector& js,
const std::vector<Matrix>& Gs, const std::vector<Vector>& gs, double f) :
HessianFactor(js, Gs, gs, f) {
checkInvariants();
}
/** Construct a binary factor. Gxx are the upper-triangle blocks of the
* quadratic term (the Hessian matrix), gx the pieces of the linear vector
* term, and f the constant term.
*/
RegularHessianFactor(Key j1, Key j2, const MatrixD& G11, const MatrixD& G12,
const VectorD& g1, const MatrixD& G22, const VectorD& g2, double f) :
HessianFactor(j1, j2, G11, G12, g1, G22, g2, f) {
}
/** Construct a ternary factor. Gxx are the upper-triangle blocks of the
* quadratic term (the Hessian matrix), gx the pieces of the linear vector
* term, and f the constant term.
*/
RegularHessianFactor(Key j1, Key j2, Key j3,
const MatrixD& G11, const MatrixD& G12, const MatrixD& G13, const VectorD& g1,
const MatrixD& G22, const MatrixD& G23, const VectorD& g2,
const MatrixD& G33, const VectorD& g3, double f) :
HessianFactor(j1, j2, j3, G11, G12, G13, g1, G22, G23, g2, G33, g3, f) {
}
/** Constructor with an arbitrary number of keys and with the augmented information matrix
* specified as a block matrix. */
template<typename KEYS>
RegularHessianFactor(const KEYS& keys,
const SymmetricBlockMatrix& augmentedInformation) :
HessianFactor(keys, augmentedInformation) {
checkInvariants();
}
/// Construct from RegularJacobianFactor
RegularHessianFactor(const RegularJacobianFactor<D>& jf)
: HessianFactor(jf) {}
/// Construct from a GaussianFactorGraph
RegularHessianFactor(const GaussianFactorGraph& factors,
const Scatter& scatter)
: HessianFactor(factors, scatter) {
checkInvariants();
}
/// Construct from a GaussianFactorGraph
RegularHessianFactor(const GaussianFactorGraph& factors)
: HessianFactor(factors) {
checkInvariants();
}
private:
/// Check invariants after construction
void checkInvariants() {
if (info_.cols() != 1 + (info_.nBlocks()-1) * (DenseIndex)D)
throw std::invalid_argument(
"RegularHessianFactor constructor was given non-regular factors");
}
// Use Eigen magic to access raw memory
typedef Eigen::Map<VectorD> DMap;
typedef Eigen::Map<const VectorD> ConstDMap;
// Scratch space for multiplyHessianAdd
// According to link below this is thread-safe.
// http://stackoverflow.com/questions/11160964/multiple-copies-of-the-same-object-c-thread-safe
mutable std::vector<VectorD> y_;
public:
/** y += alpha * A'*A*x */
void multiplyHessianAdd(double alpha, const VectorValues& x,
VectorValues& y) const override {
HessianFactor::multiplyHessianAdd(alpha, x, y);
}
/** y += alpha * A'*A*x */
void multiplyHessianAdd(double alpha, const double* x,
double* yvalues) const {
// Create a vector of temporary y_ values, corresponding to rows i
y_.resize(size());
for(VectorD & yi: y_)
yi.setZero();
// Accessing the VectorValues one by one is expensive
// So we will loop over columns to access x only once per column
// And fill the above temporary y_ values, to be added into yvalues after
VectorD xj(D);
for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
Key key = keys_[j];
const double* xj = x + key * D;
DenseIndex i = 0;
for (; i < j; ++i)
y_[i] += info_.aboveDiagonalBlock(i, j) * ConstDMap(xj);
// blocks on the diagonal are only half
y_[i] += info_.diagonalBlock(j) * ConstDMap(xj);
// for below diagonal, we take transpose block from upper triangular part
for (i = j + 1; i < (DenseIndex) size(); ++i)
y_[i] += info_.aboveDiagonalBlock(j, i).transpose() * ConstDMap(xj);
}
// copy to yvalues
for (DenseIndex i = 0; i < (DenseIndex) size(); ++i) {
Key key = keys_[i];
DMap(yvalues + key * D) += alpha * y_[i];
}
}
/// Raw memory version, with offsets TODO document reasoning
void multiplyHessianAdd(double alpha, const double* x, double* yvalues,
std::vector<size_t> offsets) const {
// Create a vector of temporary y_ values, corresponding to rows i
y_.resize(size());
for(VectorD & yi: y_)
yi.setZero();
// Accessing the VectorValues one by one is expensive
// So we will loop over columns to access x only once per column
// And fill the above temporary y_ values, to be added into yvalues after
for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
DenseIndex i = 0;
for (; i < j; ++i)
y_[i] += info_.aboveDiagonalBlock(i, j)
* ConstDMap(x + offsets[keys_[j]],
offsets[keys_[j] + 1] - offsets[keys_[j]]);
// blocks on the diagonal are only half
y_[i] += info_.diagonalBlock(j)
* ConstDMap(x + offsets[keys_[j]],
offsets[keys_[j] + 1] - offsets[keys_[j]]);
// for below diagonal, we take transpose block from upper triangular part
for (i = j + 1; i < (DenseIndex) size(); ++i)
y_[i] += info_.aboveDiagonalBlock(j, i).transpose()
* ConstDMap(x + offsets[keys_[j]],
offsets[keys_[j] + 1] - offsets[keys_[j]]);
}
// copy to yvalues
for (DenseIndex i = 0; i < (DenseIndex) size(); ++i)
DMap(yvalues + offsets[keys_[i]],
offsets[keys_[i] + 1] - offsets[keys_[i]]) += alpha * y_[i];
}
/** Return the diagonal of the Hessian for this factor (raw memory version) */
void hessianDiagonal(double* d) const override {
// Loop over all variables in the factor
for (DenseIndex pos = 0; pos < (DenseIndex) size(); ++pos) {
Key j = keys_[pos];
// Get the diagonal block, and insert its diagonal
DMap(d + D * j) += info_.diagonal(pos);
}
}
/// Add gradient at zero to d TODO: is it really the goal to add ??
void gradientAtZero(double* d) const override {
// Loop over all variables in the factor
for (DenseIndex pos = 0; pos < (DenseIndex) size(); ++pos) {
Key j = keys_[pos];
// Get the diagonal block, and insert its diagonal
DMap(d + D * j) -= info_.aboveDiagonalBlock(pos, size());;
}
}
/* ************************************************************************* */
};
// end class RegularHessianFactor
// traits
template<size_t D> struct traits<RegularHessianFactor<D> > : public Testable<
RegularHessianFactor<D> > {
};
}