1 files changed, 144 insertions, 0 deletions
diff --git a/internal/ceres/levenberg_marquardt_strategy.cc b/internal/ceres/levenberg_marquardt_strategy.cc
new file mode 100644
index 0000000..9e6a59e
--- /dev/null
+++ b/internal/ceres/levenberg_marquardt_strategy.cc
@@ -0,0 +1,144 @@
+// 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/levenberg_marquardt_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 {
+
+LevenbergMarquardtStrategy::LevenbergMarquardtStrategy(
+    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),
+      decrease_factor_(2.0),
+      reuse_diagonal_(false) {
+  CHECK_NOTNULL(linear_solver_);
+  CHECK_GT(min_diagonal_, 0.0);
+  CHECK_LE(min_diagonal_, max_diagonal_);
+  CHECK_GT(max_radius_, 0.0);
+}
+
+LevenbergMarquardtStrategy::~LevenbergMarquardtStrategy() {
+}
+
+TrustRegionStrategy::Summary LevenbergMarquardtStrategy::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 num_parameters = jacobian->num_cols();
+  if (!reuse_diagonal_) {
+    if (diagonal_.rows() != num_parameters) {
+      diagonal_.resize(num_parameters, 1);
+    }
+
+    jacobian->SquaredColumnNorm(diagonal_.data());
+    for (int i = 0; i < num_parameters; ++i) {
+      diagonal_[i] = min(max(diagonal_[i], min_diagonal_), max_diagonal_);
+    }
+  }
+
+  lm_diagonal_ = (diagonal_ / radius_).array().sqrt();
+
+  LinearSolver::PerSolveOptions solve_options;
+  solve_options.D = lm_diagonal_.data();
+  solve_options.q_tolerance = per_solve_options.eta;
+  // Disable r_tolerance checking. Since we only care about
+  // termination via the q_tolerance. As Nash and Sofer show,
+  // r_tolerance based termination is essentially useless in
+  // Truncated Newton methods.
+  solve_options.r_tolerance = -1.0;
+
+  // Invalidate the output array lm_step, so that we can detect if
+  // the linear solver generated numerical garbage.  This is known
+  // to happen for the DENSE_QR and then DENSE_SCHUR solver when
+  // the Jacobin is severly rank deficient and mu is too small.
+  InvalidateArray(num_parameters, step);
+
+  // Instead of solving Jx = -r, solve Jy = r.
+  // Then x can be found as x = -y, but the inputs jacobian and residuals
+  // do not need to be modified.
+  LinearSolver::Summary linear_solver_summary =
+      linear_solver_->Solve(jacobian, residuals, solve_options, step);
+  if (linear_solver_summary.termination_type == FAILURE ||
+      !IsArrayValid(num_parameters, step)) {
+    LOG(WARNING) << "Linear solver failure. Failed to compute a finite step.";
+    linear_solver_summary.termination_type = FAILURE;
+  } else {
+    VectorRef(step, num_parameters) *= -1.0;
+  }
+
+  reuse_diagonal_ = true;
+
+  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;
+  return summary;
+}
+
+void LevenbergMarquardtStrategy::StepAccepted(double step_quality) {
+  CHECK_GT(step_quality, 0.0);
+  radius_ = radius_ / std::max(1.0 / 3.0,
+                               1.0 - pow(2.0 * step_quality - 1.0, 3));
+  radius_ = std::min(max_radius_, radius_);
+  decrease_factor_ = 2.0;
+  reuse_diagonal_ = false;
+}
+
+void LevenbergMarquardtStrategy::StepRejected(double step_quality) {
+  radius_ = radius_ / decrease_factor_;
+  decrease_factor_ *= 2.0;
+  reuse_diagonal_ = true;
+}
+
+double LevenbergMarquardtStrategy::Radius() const {
+  return radius_;
+}
+
+}  // namespace internal
+}  // namespace ceres