internal/ceres/dogleg_strategy.cc - ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2012 Google Inc. All rights reserved.
 // http://code.google.com/p/ceres-solver/
 //
 // 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.
 //
 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
 // 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
 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 // POSSIBILITY OF SUCH DAMAGE.
 //
 // Author: sameeragarwal@google.com (Sameer Agarwal)

 #include "ceres/dogleg_strategy.h"

 #include <cmath>
 #include "Eigen/Core"
 #include "ceres/array_utils.h"
 #include "ceres/internal/eigen.h"
 #include "ceres/linear_solver.h"
 #include "ceres/sparse_matrix.h"
 #include "ceres/trust_region_strategy.h"
 #include "ceres/types.h"
 #include "glog/logging.h"

 namespace ceres {
 namespace internal {
 namespace {
 const double kMaxMu = 1.0;
 const double kMinMu = 1e-8;
 }

 DoglegStrategy::DoglegStrategy(const TrustRegionStrategy::Options& options)
     : linear_solver_(options.linear_solver),
       radius_(options.initial_radius),
       max_radius_(options.max_radius),
       min_diagonal_(options.lm_min_diagonal),
       max_diagonal_(options.lm_max_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) {
   CHECK_NOTNULL(linear_solver_);
   CHECK_GT(min_diagonal_, 0.0);
   CHECK_LT(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.
 LinearSolver::Summary DoglegStrategy::ComputeStep(
     const TrustRegionStrategy::PerSolveOptions& per_solve_options,
     SparseMatrix* jacobian,
     const double* residuals,
     double* step) {
   CHECK_NOTNULL(jacobian);
   CHECK_NOTNULL(residuals);
   CHECK_NOTNULL(step);

   const int n = jacobian->num_cols();
   if (reuse_) {
     // Gauss-Newton and gradient vectors are always available, only a
     // new interpolant need to be computed.
     ComputeDoglegStep(step);
     LinearSolver::Summary linear_solver_summary;
     linear_solver_summary.num_iterations = 0;
     linear_solver_summary.termination_type = TOLERANCE;
     return linear_solver_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.
   jacobian->SquaredColumnNorm(diagonal_.data());
   for (int i = 0; i < n; ++i) {
     diagonal_[i] = min(max(diagonal_[i], min_diagonal_), max_diagonal_);
   }

   gradient_.setZero();
   jacobian->LeftMultiply(residuals, gradient_.data());

   // alpha * gradient is the Cauchy point.
   Vector Jg(jacobian->num_rows());
   Jg.setZero();
   jacobian->RightMultiply(gradient_.data(), Jg.data());
   alpha_ = gradient_.squaredNorm() / Jg.squaredNorm();

   LinearSolver::Summary linear_solver_summary =
       ComputeGaussNewtonStep(jacobian, residuals);

   // Interpolate the Cauchy point and the Gauss-Newton step.
   if (linear_solver_summary.termination_type != FAILURE) {
     ComputeDoglegStep(step);
   }

   return linear_solver_summary;
 }

 void DoglegStrategy::ComputeDoglegStep(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;
     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_;
     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();
   VLOG(3) << "Dogleg step size: " << dogleg_step_norm_
           << " radius: " << radius_;
 }

 LinearSolver::Summary DoglegStrategy::ComputeGaussNewtonStep(
     SparseMatrix* jacobian,
     const double* residuals) {
   const int n = jacobian->num_cols();
   LinearSolver::Summary linear_solver_summary;
   linear_solver_summary.termination_type = 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 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_ * mu_).array().sqrt();
     solve_options.D = lm_diagonal_.data();

     InvalidateArray(n, gauss_newton_step_.data());
     linear_solver_summary = linear_solver_->Solve(jacobian,
                                                   residuals,
                                                   solve_options,
                                                   gauss_newton_step_.data());

     if (linear_solver_summary.termination_type == FAILURE ||
         !IsArrayValid(n, gauss_newton_step_.data())) {
       mu_ *= mu_increase_factor_;
       VLOG(2) << "Increasing mu " << mu_;
       linear_solver_summary.termination_type = FAILURE;
       continue;
     }
     break;
   }

   return linear_solver_summary;
 }

 void DoglegStrategy::StepAccepted(double step_quality) {
   CHECK_GT(step_quality, 0.0);
   if (step_quality < decrease_threshold_) {
     radius_ *= 0.5;
     return;
   }

   if (step_quality > increase_threshold_) {
     radius_ = 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_ = 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_;
 }

 }  // namespace internal
 }  // namespace ceres
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2012 Google Inc. All rights reserved.
	// http://code.google.com/p/ceres-solver/
	//
	// 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.
	//
	// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
	// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
	// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
	// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
	// 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
	// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
	// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
	// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
	// POSSIBILITY OF SUCH DAMAGE.
	//
	// Author: sameeragarwal@google.com (Sameer Agarwal)

	#include "ceres/dogleg_strategy.h"

	#include <cmath>
	#include "Eigen/Core"
	#include "ceres/array_utils.h"
	#include "ceres/internal/eigen.h"
	#include "ceres/linear_solver.h"
	#include "ceres/sparse_matrix.h"
	#include "ceres/trust_region_strategy.h"
	#include "ceres/types.h"
	#include "glog/logging.h"

	namespace ceres {
	namespace internal {
	namespace {
	const double kMaxMu = 1.0;
	const double kMinMu = 1e-8;
	}

	DoglegStrategy::DoglegStrategy(const TrustRegionStrategy::Options& options)
	: linear_solver_(options.linear_solver),
	radius_(options.initial_radius),
	max_radius_(options.max_radius),
	min_diagonal_(options.lm_min_diagonal),
	max_diagonal_(options.lm_max_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) {
	CHECK_NOTNULL(linear_solver_);
	CHECK_GT(min_diagonal_, 0.0);
	CHECK_LT(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.
	LinearSolver::Summary DoglegStrategy::ComputeStep(
	const TrustRegionStrategy::PerSolveOptions& per_solve_options,
	SparseMatrix* jacobian,
	const double* residuals,
	double* step) {
	CHECK_NOTNULL(jacobian);
	CHECK_NOTNULL(residuals);
	CHECK_NOTNULL(step);

	const int n = jacobian->num_cols();
	if (reuse_) {
	// Gauss-Newton and gradient vectors are always available, only a
	// new interpolant need to be computed.
	ComputeDoglegStep(step);
	LinearSolver::Summary linear_solver_summary;
	linear_solver_summary.num_iterations = 0;
	linear_solver_summary.termination_type = TOLERANCE;
	return linear_solver_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.
	jacobian->SquaredColumnNorm(diagonal_.data());
	for (int i = 0; i < n; ++i) {
	diagonal_[i] = min(max(diagonal_[i], min_diagonal_), max_diagonal_);
	}

	gradient_.setZero();
	jacobian->LeftMultiply(residuals, gradient_.data());

	// alpha * gradient is the Cauchy point.
	Vector Jg(jacobian->num_rows());
	Jg.setZero();
	jacobian->RightMultiply(gradient_.data(), Jg.data());
	alpha_ = gradient_.squaredNorm() / Jg.squaredNorm();

	LinearSolver::Summary linear_solver_summary =
	ComputeGaussNewtonStep(jacobian, residuals);

	// Interpolate the Cauchy point and the Gauss-Newton step.
	if (linear_solver_summary.termination_type != FAILURE) {
	ComputeDoglegStep(step);
	}

	return linear_solver_summary;
	}

	void DoglegStrategy::ComputeDoglegStep(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;
	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_;
	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();
	VLOG(3) << "Dogleg step size: " << dogleg_step_norm_
	<< " radius: " << radius_;
	}

	LinearSolver::Summary DoglegStrategy::ComputeGaussNewtonStep(
	SparseMatrix* jacobian,
	const double* residuals) {
	const int n = jacobian->num_cols();
	LinearSolver::Summary linear_solver_summary;
	linear_solver_summary.termination_type = 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 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_ * mu_).array().sqrt();
	solve_options.D = lm_diagonal_.data();

	InvalidateArray(n, gauss_newton_step_.data());
	linear_solver_summary = linear_solver_->Solve(jacobian,
	residuals,
	solve_options,
	gauss_newton_step_.data());

	if (linear_solver_summary.termination_type == FAILURE \|\|
	!IsArrayValid(n, gauss_newton_step_.data())) {
	mu_ *= mu_increase_factor_;
	VLOG(2) << "Increasing mu " << mu_;
	linear_solver_summary.termination_type = FAILURE;
	continue;
	}
	break;
	}

	return linear_solver_summary;
	}

	void DoglegStrategy::StepAccepted(double step_quality) {
	CHECK_GT(step_quality, 0.0);
	if (step_quality < decrease_threshold_) {
	radius_ *= 0.5;
	return;
	}

	if (step_quality > increase_threshold_) {
	radius_ = 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_ = 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_;
	}

	} // namespace internal
	} // namespace ceres