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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/dogleg_strategy.h"
#include <algorithm>
#include <cmath>
#include "Eigen/Dense"
#include "ceres/array_utils.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/linear_solver.h"
#include "ceres/polynomial.h"
#include "ceres/sparse_matrix.h"
#include "ceres/trust_region_strategy.h"
#include "ceres/types.h"
#include "glog/logging.h"
namespace ceres::internal {
namespace {
const double kMaxMu = 1.0;
const double kMinMu = 1e-8;
} // namespace
DoglegStrategy::DoglegStrategy(const TrustRegionStrategy::Options& options)
: linear_solver_(options.linear_solver),
radius_(options.initial_radius),
max_radius_(options.max_radius),
min_diagonal_(options.min_lm_diagonal),
max_diagonal_(options.max_lm_diagonal),
mu_(kMinMu),
min_mu_(kMinMu),
max_mu_(kMaxMu),
mu_increase_factor_(10.0),
increase_threshold_(0.75),
decrease_threshold_(0.25),
dogleg_step_norm_(0.0),
reuse_(false),
dogleg_type_(options.dogleg_type) {
CHECK(linear_solver_ != nullptr);
CHECK_GT(min_diagonal_, 0.0);
CHECK_LE(min_diagonal_, max_diagonal_);
CHECK_GT(max_radius_, 0.0);
}
// If the reuse_ flag is not set, then the Cauchy point (scaled
// gradient) and the new Gauss-Newton step are computed from
// scratch. The Dogleg step is then computed as interpolation of these
// two vectors.
TrustRegionStrategy::Summary DoglegStrategy::ComputeStep(
const TrustRegionStrategy::PerSolveOptions& per_solve_options,
SparseMatrix* jacobian,
const double* residuals,
double* step) {
CHECK(jacobian != nullptr);
CHECK(residuals != nullptr);
CHECK(step != nullptr);
const int n = jacobian->num_cols();
if (reuse_) {
// Gauss-Newton and gradient vectors are always available, only a
// new interpolant need to be computed. For the subspace case,
// the subspace and the two-dimensional model are also still valid.
switch (dogleg_type_) {
case TRADITIONAL_DOGLEG:
ComputeTraditionalDoglegStep(step);
break;
case SUBSPACE_DOGLEG:
ComputeSubspaceDoglegStep(step);
break;
}
TrustRegionStrategy::Summary summary;
summary.num_iterations = 0;
summary.termination_type = LinearSolverTerminationType::SUCCESS;
return summary;
}
reuse_ = true;
// Check that we have the storage needed to hold the various
// temporary vectors.
if (diagonal_.rows() != n) {
diagonal_.resize(n, 1);
gradient_.resize(n, 1);
gauss_newton_step_.resize(n, 1);
}
// Vector used to form the diagonal matrix that is used to
// regularize the Gauss-Newton solve and that defines the
// elliptical trust region
//
// || D * step || <= radius_ .
//
jacobian->SquaredColumnNorm(diagonal_.data());
for (int i = 0; i < n; ++i) {
diagonal_[i] =
std::min(std::max(diagonal_[i], min_diagonal_), max_diagonal_);
}
diagonal_ = diagonal_.array().sqrt();
ComputeGradient(jacobian, residuals);
ComputeCauchyPoint(jacobian);
LinearSolver::Summary linear_solver_summary =
ComputeGaussNewtonStep(per_solve_options, jacobian, residuals);
TrustRegionStrategy::Summary summary;
summary.residual_norm = linear_solver_summary.residual_norm;
summary.num_iterations = linear_solver_summary.num_iterations;
summary.termination_type = linear_solver_summary.termination_type;
if (linear_solver_summary.termination_type ==
LinearSolverTerminationType::FATAL_ERROR) {
return summary;
}
if (linear_solver_summary.termination_type !=
LinearSolverTerminationType::FAILURE) {
switch (dogleg_type_) {
// Interpolate the Cauchy point and the Gauss-Newton step.
case TRADITIONAL_DOGLEG:
ComputeTraditionalDoglegStep(step);
break;
// Find the minimum in the subspace defined by the
// Cauchy point and the (Gauss-)Newton step.
case SUBSPACE_DOGLEG:
if (!ComputeSubspaceModel(jacobian)) {
summary.termination_type = LinearSolverTerminationType::FAILURE;
break;
}
ComputeSubspaceDoglegStep(step);
break;
}
}
return summary;
}
// The trust region is assumed to be elliptical with the
// diagonal scaling matrix D defined by sqrt(diagonal_).
// It is implemented by substituting step' = D * step.
// The trust region for step' is spherical.
// The gradient, the Gauss-Newton step, the Cauchy point,
// and all calculations involving the Jacobian have to
// be adjusted accordingly.
void DoglegStrategy::ComputeGradient(SparseMatrix* jacobian,
const double* residuals) {
gradient_.setZero();
jacobian->LeftMultiplyAndAccumulate(residuals, gradient_.data());
gradient_.array() /= diagonal_.array();
}
// The Cauchy point is the global minimizer of the quadratic model
// along the one-dimensional subspace spanned by the gradient.
void DoglegStrategy::ComputeCauchyPoint(SparseMatrix* jacobian) {
// alpha * -gradient is the Cauchy point.
Vector Jg(jacobian->num_rows());
Jg.setZero();
// The Jacobian is scaled implicitly by computing J * (D^-1 * (D^-1 * g))
// instead of (J * D^-1) * (D^-1 * g).
Vector scaled_gradient = (gradient_.array() / diagonal_.array()).matrix();
jacobian->RightMultiplyAndAccumulate(scaled_gradient.data(), Jg.data());
alpha_ = gradient_.squaredNorm() / Jg.squaredNorm();
}
// The dogleg step is defined as the intersection of the trust region
// boundary with the piecewise linear path from the origin to the Cauchy
// point and then from there to the Gauss-Newton point (global minimizer
// of the model function). The Gauss-Newton point is taken if it lies
// within the trust region.
void DoglegStrategy::ComputeTraditionalDoglegStep(double* dogleg) {
VectorRef dogleg_step(dogleg, gradient_.rows());
// Case 1. The Gauss-Newton step lies inside the trust region, and
// is therefore the optimal solution to the trust-region problem.
const double gradient_norm = gradient_.norm();
const double gauss_newton_norm = gauss_newton_step_.norm();
if (gauss_newton_norm <= radius_) {
dogleg_step = gauss_newton_step_;
dogleg_step_norm_ = gauss_newton_norm;
dogleg_step.array() /= diagonal_.array();
VLOG(3) << "GaussNewton step size: " << dogleg_step_norm_
<< " radius: " << radius_;
return;
}
// Case 2. The Cauchy point and the Gauss-Newton steps lie outside
// the trust region. Rescale the Cauchy point to the trust region
// and return.
if (gradient_norm * alpha_ >= radius_) {
dogleg_step = -(radius_ / gradient_norm) * gradient_;
dogleg_step_norm_ = radius_;
dogleg_step.array() /= diagonal_.array();
VLOG(3) << "Cauchy step size: " << dogleg_step_norm_
<< " radius: " << radius_;
return;
}
// Case 3. The Cauchy point is inside the trust region and the
// Gauss-Newton step is outside. Compute the line joining the two
// points and the point on it which intersects the trust region
// boundary.
// a = alpha * -gradient
// b = gauss_newton_step
const double b_dot_a = -alpha_ * gradient_.dot(gauss_newton_step_);
const double a_squared_norm = pow(alpha_ * gradient_norm, 2.0);
const double b_minus_a_squared_norm =
a_squared_norm - 2 * b_dot_a + pow(gauss_newton_norm, 2);
// c = a' (b - a)
// = alpha * -gradient' gauss_newton_step - alpha^2 |gradient|^2
const double c = b_dot_a - a_squared_norm;
const double d = sqrt(c * c + b_minus_a_squared_norm *
(pow(radius_, 2.0) - a_squared_norm));
double beta = (c <= 0) ? (d - c) / b_minus_a_squared_norm
: (radius_ * radius_ - a_squared_norm) / (d + c);
dogleg_step =
(-alpha_ * (1.0 - beta)) * gradient_ + beta * gauss_newton_step_;
dogleg_step_norm_ = dogleg_step.norm();
dogleg_step.array() /= diagonal_.array();
VLOG(3) << "Dogleg step size: " << dogleg_step_norm_
<< " radius: " << radius_;
}
// The subspace method finds the minimum of the two-dimensional problem
//
// min. 1/2 x' B' H B x + g' B x
// s.t. || B x ||^2 <= r^2
//
// where r is the trust region radius and B is the matrix with unit columns
// spanning the subspace defined by the steepest descent and Newton direction.
// This subspace by definition includes the Gauss-Newton point, which is
// therefore taken if it lies within the trust region.
void DoglegStrategy::ComputeSubspaceDoglegStep(double* dogleg) {
VectorRef dogleg_step(dogleg, gradient_.rows());
// The Gauss-Newton point is inside the trust region if |GN| <= radius_.
// This test is valid even though radius_ is a length in the two-dimensional
// subspace while gauss_newton_step_ is expressed in the (scaled)
// higher dimensional original space. This is because
//
// 1. gauss_newton_step_ by definition lies in the subspace, and
// 2. the subspace basis is orthonormal.
//
// As a consequence, the norm of the gauss_newton_step_ in the subspace is
// the same as its norm in the original space.
const double gauss_newton_norm = gauss_newton_step_.norm();
if (gauss_newton_norm <= radius_) {
dogleg_step = gauss_newton_step_;
dogleg_step_norm_ = gauss_newton_norm;
dogleg_step.array() /= diagonal_.array();
VLOG(3) << "GaussNewton step size: " << dogleg_step_norm_
<< " radius: " << radius_;
return;
}
// The optimum lies on the boundary of the trust region. The above problem
// therefore becomes
//
// min. 1/2 x^T B^T H B x + g^T B x
// s.t. || B x ||^2 = r^2
//
// Notice the equality in the constraint.
//
// This can be solved by forming the Lagrangian, solving for x(y), where
// y is the Lagrange multiplier, using the gradient of the objective, and
// putting x(y) back into the constraint. This results in a fourth order
// polynomial in y, which can be solved using e.g. the companion matrix.
// See the description of MakePolynomialForBoundaryConstrainedProblem for
// details. The result is up to four real roots y*, not all of which
// correspond to feasible points. The feasible points x(y*) have to be
// tested for optimality.
if (subspace_is_one_dimensional_) {
// The subspace is one-dimensional, so both the gradient and
// the Gauss-Newton step point towards the same direction.
// In this case, we move along the gradient until we reach the trust
// region boundary.
dogleg_step = -(radius_ / gradient_.norm()) * gradient_;
dogleg_step_norm_ = radius_;
dogleg_step.array() /= diagonal_.array();
VLOG(3) << "Dogleg subspace step size (1D): " << dogleg_step_norm_
<< " radius: " << radius_;
return;
}
Vector2d minimum(0.0, 0.0);
if (!FindMinimumOnTrustRegionBoundary(&minimum)) {
// For the positive semi-definite case, a traditional dogleg step
// is taken in this case.
LOG(WARNING) << "Failed to compute polynomial roots. "
<< "Taking traditional dogleg step instead.";
ComputeTraditionalDoglegStep(dogleg);
return;
}
// Test first order optimality at the minimum.
// The first order KKT conditions state that the minimum x*
// has to satisfy either || x* ||^2 < r^2 (i.e. has to lie within
// the trust region), or
//
// (B x* + g) + y x* = 0
//
// for some positive scalar y.
// Here, as it is already known that the minimum lies on the boundary, the
// latter condition is tested. To allow for small imprecisions, we test if
// the angle between (B x* + g) and -x* is smaller than acos(0.99).
// The exact value of the cosine is arbitrary but should be close to 1.
//
// This condition should not be violated. If it is, the minimum was not
// correctly determined.
const double kCosineThreshold = 0.99;
const Vector2d grad_minimum = subspace_B_ * minimum + subspace_g_;
const double cosine_angle =
-minimum.dot(grad_minimum) / (minimum.norm() * grad_minimum.norm());
if (cosine_angle < kCosineThreshold) {
LOG(WARNING) << "First order optimality seems to be violated "
<< "in the subspace method!\n"
<< "Cosine of angle between x and B x + g is " << cosine_angle
<< ".\n"
<< "Taking a regular dogleg step instead.\n"
<< "Please consider filing a bug report if this "
<< "happens frequently or consistently.\n";
ComputeTraditionalDoglegStep(dogleg);
return;
}
// Create the full step from the optimal 2d solution.
dogleg_step = subspace_basis_ * minimum;
dogleg_step_norm_ = radius_;
dogleg_step.array() /= diagonal_.array();
VLOG(3) << "Dogleg subspace step size: " << dogleg_step_norm_
<< " radius: " << radius_;
}
// Build the polynomial that defines the optimal Lagrange multipliers.
// Let the Lagrangian be
//
// L(x, y) = 0.5 x^T B x + x^T g + y (0.5 x^T x - 0.5 r^2). (1)
//
// Stationary points of the Lagrangian are given by
//
// 0 = d L(x, y) / dx = Bx + g + y x (2)
// 0 = d L(x, y) / dy = 0.5 x^T x - 0.5 r^2 (3)
//
// For any given y, we can solve (2) for x as
//
// x(y) = -(B + y I)^-1 g . (4)
//
// As B + y I is 2x2, we form the inverse explicitly:
//
// (B + y I)^-1 = (1 / det(B + y I)) adj(B + y I) (5)
//
// where adj() denotes adjugation. This should be safe, as B is positive
// semi-definite and y is necessarily positive, so (B + y I) is indeed
// invertible.
// Plugging (5) into (4) and the result into (3), then dividing by 0.5 we
// obtain
//
// 0 = (1 / det(B + y I))^2 g^T adj(B + y I)^T adj(B + y I) g - r^2
// (6)
//
// or
//
// det(B + y I)^2 r^2 = g^T adj(B + y I)^T adj(B + y I) g (7a)
// = g^T adj(B)^T adj(B) g
// + 2 y g^T adj(B)^T g + y^2 g^T g (7b)
//
// as
//
// adj(B + y I) = adj(B) + y I = adj(B)^T + y I . (8)
//
// The left hand side can be expressed explicitly using
//
// det(B + y I) = det(B) + y tr(B) + y^2 . (9)
//
// So (7) is a polynomial in y of degree four.
// Bringing everything back to the left hand side, the coefficients can
// be read off as
//
// y^4 r^2
// + y^3 2 r^2 tr(B)
// + y^2 (r^2 tr(B)^2 + 2 r^2 det(B) - g^T g)
// + y^1 (2 r^2 det(B) tr(B) - 2 g^T adj(B)^T g)
// + y^0 (r^2 det(B)^2 - g^T adj(B)^T adj(B) g)
//
Vector DoglegStrategy::MakePolynomialForBoundaryConstrainedProblem() const {
const double detB = subspace_B_.determinant();
const double trB = subspace_B_.trace();
const double r2 = radius_ * radius_;
Matrix2d B_adj;
// clang-format off
B_adj << subspace_B_(1, 1) , -subspace_B_(0, 1),
-subspace_B_(1, 0) , subspace_B_(0, 0);
// clang-format on
Vector polynomial(5);
polynomial(0) = r2;
polynomial(1) = 2.0 * r2 * trB;
polynomial(2) = r2 * (trB * trB + 2.0 * detB) - subspace_g_.squaredNorm();
polynomial(3) =
-2.0 * (subspace_g_.transpose() * B_adj * subspace_g_ - r2 * detB * trB);
polynomial(4) = r2 * detB * detB - (B_adj * subspace_g_).squaredNorm();
return polynomial;
}
// Given a Lagrange multiplier y that corresponds to a stationary point
// of the Lagrangian L(x, y), compute the corresponding x from the
// equation
//
// 0 = d L(x, y) / dx
// = B * x + g + y * x
// = (B + y * I) * x + g
//
DoglegStrategy::Vector2d DoglegStrategy::ComputeSubspaceStepFromRoot(
double y) const {
const Matrix2d B_i = subspace_B_ + y * Matrix2d::Identity();
return -B_i.partialPivLu().solve(subspace_g_);
}
// This function evaluates the quadratic model at a point x in the
// subspace spanned by subspace_basis_.
double DoglegStrategy::EvaluateSubspaceModel(const Vector2d& x) const {
return 0.5 * x.dot(subspace_B_ * x) + subspace_g_.dot(x);
}
// This function attempts to solve the boundary-constrained subspace problem
//
// min. 1/2 x^T B^T H B x + g^T B x
// s.t. || B x ||^2 = r^2
//
// where B is an orthonormal subspace basis and r is the trust-region radius.
//
// This is done by finding the roots of a fourth degree polynomial. If the
// root finding fails, the function returns false and minimum will be set
// to (0, 0). If it succeeds, true is returned.
//
// In the failure case, another step should be taken, such as the traditional
// dogleg step.
bool DoglegStrategy::FindMinimumOnTrustRegionBoundary(Vector2d* minimum) const {
CHECK(minimum != nullptr);
// Return (0, 0) in all error cases.
minimum->setZero();
// Create the fourth-degree polynomial that is a necessary condition for
// optimality.
const Vector polynomial = MakePolynomialForBoundaryConstrainedProblem();
// Find the real parts y_i of its roots (not only the real roots).
Vector roots_real;
if (!FindPolynomialRoots(polynomial, &roots_real, nullptr)) {
// Failed to find the roots of the polynomial, i.e. the candidate
// solutions of the constrained problem. Report this back to the caller.
return false;
}
// For each root y, compute B x(y) and check for feasibility.
// Notice that there should always be four roots, as the leading term of
// the polynomial is r^2 and therefore non-zero. However, as some roots
// may be complex, the real parts are not necessarily unique.
double minimum_value = std::numeric_limits<double>::max();
bool valid_root_found = false;
for (int i = 0; i < roots_real.size(); ++i) {
const Vector2d x_i = ComputeSubspaceStepFromRoot(roots_real(i));
// Not all roots correspond to points on the trust region boundary.
// There are at most four candidate solutions. As we are interested
// in the minimum, it is safe to consider all of them after projecting
// them onto the trust region boundary.
if (x_i.norm() > 0) {
const double f_i = EvaluateSubspaceModel((radius_ / x_i.norm()) * x_i);
valid_root_found = true;
if (f_i < minimum_value) {
minimum_value = f_i;
*minimum = x_i;
}
}
}
return valid_root_found;
}
LinearSolver::Summary DoglegStrategy::ComputeGaussNewtonStep(
const PerSolveOptions& per_solve_options,
SparseMatrix* jacobian,
const double* residuals) {
const int n = jacobian->num_cols();
LinearSolver::Summary linear_solver_summary;
linear_solver_summary.termination_type = LinearSolverTerminationType::FAILURE;
// The Jacobian matrix is often quite poorly conditioned. Thus it is
// necessary to add a diagonal matrix at the bottom to prevent the
// linear solver from failing.
//
// We do this by computing the same diagonal matrix as the one used
// by Levenberg-Marquardt (other choices are possible), and scaling
// it by a small constant (independent of the trust region radius).
//
// If the solve fails, the multiplier to the diagonal is increased
// up to max_mu_ by a factor of mu_increase_factor_ every time. If
// the linear solver is still not successful, the strategy returns
// with LinearSolverTerminationType::FAILURE.
//
// Next time when a new Gauss-Newton step is requested, the
// multiplier starts out from the last successful solve.
//
// When a step is declared successful, the multiplier is decreased
// by half of mu_increase_factor_.
while (mu_ < max_mu_) {
// Dogleg, as far as I (sameeragarwal) understand it, requires a
// reasonably good estimate of the Gauss-Newton step. This means
// that we need to solve the normal equations more or less
// exactly. This is reflected in the values of the tolerances set
// below.
//
// For now, this strategy should only be used with exact
// factorization based solvers, for which these tolerances are
// automatically satisfied.
//
// The right way to combine inexact solves with trust region
// methods is to use Stiehaug's method.
LinearSolver::PerSolveOptions solve_options;
solve_options.q_tolerance = 0.0;
solve_options.r_tolerance = 0.0;
lm_diagonal_ = diagonal_ * std::sqrt(mu_);
solve_options.D = lm_diagonal_.data();
// As in the LevenbergMarquardtStrategy, solve Jy = r instead
// of Jx = -r and later set x = -y to avoid having to modify
// either jacobian or residuals.
InvalidateArray(n, gauss_newton_step_.data());
linear_solver_summary = linear_solver_->Solve(
jacobian, residuals, solve_options, gauss_newton_step_.data());
if (per_solve_options.dump_format_type == CONSOLE ||
(per_solve_options.dump_format_type != CONSOLE &&
!per_solve_options.dump_filename_base.empty())) {
if (!DumpLinearLeastSquaresProblem(per_solve_options.dump_filename_base,
per_solve_options.dump_format_type,
jacobian,
solve_options.D,
residuals,
gauss_newton_step_.data(),
0)) {
LOG(ERROR) << "Unable to dump trust region problem."
<< " Filename base: "
<< per_solve_options.dump_filename_base;
}
}
if (linear_solver_summary.termination_type ==
LinearSolverTerminationType::FATAL_ERROR) {
return linear_solver_summary;
}
if (linear_solver_summary.termination_type ==
LinearSolverTerminationType::FAILURE ||
!IsArrayValid(n, gauss_newton_step_.data())) {
mu_ *= mu_increase_factor_;
VLOG(2) << "Increasing mu " << mu_;
linear_solver_summary.termination_type =
LinearSolverTerminationType::FAILURE;
continue;
}
break;
}
if (linear_solver_summary.termination_type !=
LinearSolverTerminationType::FAILURE) {
// The scaled Gauss-Newton step is D * GN:
//
// - (D^-1 J^T J D^-1)^-1 (D^-1 g)
// = - D (J^T J)^-1 D D^-1 g
// = D -(J^T J)^-1 g
//
gauss_newton_step_.array() *= -diagonal_.array();
}
return linear_solver_summary;
}
void DoglegStrategy::StepAccepted(double step_quality) {
CHECK_GT(step_quality, 0.0);
if (step_quality < decrease_threshold_) {
radius_ *= 0.5;
}
if (step_quality > increase_threshold_) {
radius_ = std::max(radius_, 3.0 * dogleg_step_norm_);
}
// Reduce the regularization multiplier, in the hope that whatever
// was causing the rank deficiency has gone away and we can return
// to doing a pure Gauss-Newton solve.
mu_ = std::max(min_mu_, 2.0 * mu_ / mu_increase_factor_);
reuse_ = false;
}
void DoglegStrategy::StepRejected(double /*step_quality*/) {
radius_ *= 0.5;
reuse_ = true;
}
void DoglegStrategy::StepIsInvalid() {
mu_ *= mu_increase_factor_;
reuse_ = false;
}
double DoglegStrategy::Radius() const { return radius_; }
bool DoglegStrategy::ComputeSubspaceModel(SparseMatrix* jacobian) {
// Compute an orthogonal basis for the subspace using QR decomposition.
Matrix basis_vectors(jacobian->num_cols(), 2);
basis_vectors.col(0) = gradient_;
basis_vectors.col(1) = gauss_newton_step_;
Eigen::ColPivHouseholderQR<Matrix> basis_qr(basis_vectors);
switch (basis_qr.rank()) {
case 0:
// This should never happen, as it implies that both the gradient
// and the Gauss-Newton step are zero. In this case, the minimizer should
// have stopped due to the gradient being too small.
LOG(ERROR) << "Rank of subspace basis is 0. "
<< "This means that the gradient at the current iterate is "
<< "zero but the optimization has not been terminated. "
<< "You may have found a bug in Ceres.";
return false;
case 1:
// Gradient and Gauss-Newton step coincide, so we lie on one of the
// major axes of the quadratic problem. In this case, we simply move
// along the gradient until we reach the trust region boundary.
subspace_is_one_dimensional_ = true;
return true;
case 2:
subspace_is_one_dimensional_ = false;
break;
default:
LOG(ERROR) << "Rank of the subspace basis matrix is reported to be "
<< "greater than 2. As the matrix contains only two "
<< "columns this cannot be true and is indicative of "
<< "a bug.";
return false;
}
// The subspace is two-dimensional, so compute the subspace model.
// Given the basis U, this is
//
// subspace_g_ = g_scaled^T U
//
// and
//
// subspace_B_ = U^T (J_scaled^T J_scaled) U
//
// As J_scaled = J * D^-1, the latter becomes
//
// subspace_B_ = ((U^T D^-1) J^T) (J (D^-1 U))
// = (J (D^-1 U))^T (J (D^-1 U))
subspace_basis_ =
basis_qr.householderQ() * Matrix::Identity(jacobian->num_cols(), 2);
subspace_g_ = subspace_basis_.transpose() * gradient_;
Eigen::Matrix<double, 2, Eigen::Dynamic, Eigen::RowMajor> Jb(
2, jacobian->num_rows());
Jb.setZero();
Vector tmp;
tmp = (subspace_basis_.col(0).array() / diagonal_.array()).matrix();
jacobian->RightMultiplyAndAccumulate(tmp.data(), Jb.row(0).data());
tmp = (subspace_basis_.col(1).array() / diagonal_.array()).matrix();
jacobian->RightMultiplyAndAccumulate(tmp.data(), Jb.row(1).data());
subspace_B_ = Jb * Jb.transpose();
return true;
}
} // namespace ceres::internal