text: // Ceres Solver - A fast non-linear least squares minimizer
  // Copyright 2012 Google Inc. All rights reserved.
  // http://code.google.com/p/ceres-solver/
  //
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  //
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  //   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
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  // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
  // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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  // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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  // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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  //
  // Author: sameeragarwal@google.com (Sameer Agarwal)
  
  #include "ceres/internal/eigen.h"
  #include "ceres/internal/scoped_ptr.h"
  #include "ceres/levenberg_marquardt_strategy.h"
  #include "ceres/linear_solver.h"
  #include "ceres/trust_region_strategy.h"
  #include "glog/logging.h"
  #include "gmock/gmock.h"
  #include "gmock/mock-log.h"
  #include "gtest/gtest.h"
  
  using testing::AllOf;
  using testing::AnyNumber;
  using testing::HasSubstr;
  using testing::ScopedMockLog;
  using testing::_;
  
  namespace ceres {
  namespace internal {
  
  const double kTolerance = 1e-16;
  
  // Linear solver that takes as input a vector and checks that the
  // caller passes the same vector as LinearSolver::PerSolveOptions.D.
  class RegularizationCheckingLinearSolver : public DenseSparseMatrixSolver {
   public:
    RegularizationCheckingLinearSolver(const int num_cols, const double* diagonal)
        : num_cols_(num_cols),
          diagonal_(diagonal) {
    }
  
    virtual ~RegularizationCheckingLinearSolver() {}
  
   private:
    virtual LinearSolver::Summary SolveImpl(
        DenseSparseMatrix* A,
        const double* b,
        const LinearSolver::PerSolveOptions& per_solve_options,
        double* x) {
      CHECK_NOTNULL(per_solve_options.D);
      for (int i = 0; i < num_cols_; ++i) {
        EXPECT_NEAR(per_solve_options.D[i], diagonal_[i], kTolerance)
            << i << " " << per_solve_options.D[i] << " " << diagonal_[i];
      }
      return LinearSolver::Summary();
    }
  
    const int num_cols_;
    const double* diagonal_;
  };
  
  TEST(LevenbergMarquardtStrategy, AcceptRejectStepRadiusScaling) {
    TrustRegionStrategy::Options options;
    options.initial_radius = 2.0;
    options.max_radius = 20.0;
    options.min_lm_diagonal = 1e-8;
    options.max_lm_diagonal = 1e8;
  
    // We need a non-null pointer here, so anything should do.
    scoped_ptr<LinearSolver> linear_solver(
        new RegularizationCheckingLinearSolver(0, NULL));
    options.linear_solver = linear_solver.get();
  
    LevenbergMarquardtStrategy lms(options);
    EXPECT_EQ(lms.Radius(), options.initial_radius);
    lms.StepRejected(0.0);
    EXPECT_EQ(lms.Radius(), 1.0);
    lms.StepRejected(-1.0);
    EXPECT_EQ(lms.Radius(), 0.25);
    lms.StepAccepted(1.0);
    EXPECT_EQ(lms.Radius(), 0.25 * 3.0);
    lms.StepAccepted(1.0);
    EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0);
    lms.StepAccepted(0.25);
    EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125);
    lms.StepAccepted(1.0);
    EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0);
    lms.StepAccepted(1.0);
    EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0 * 3.0);
    lms.StepAccepted(1.0);
    EXPECT_EQ(lms.Radius(), options.max_radius);
  }
  
  TEST(LevenbergMarquardtStrategy, CorrectDiagonalToLinearSolver) {
    Matrix jacobian(2, 3);
    jacobian.setZero();
    jacobian(0, 0) = 0.0;
    jacobian(0, 1) = 1.0;
    jacobian(1, 1) = 1.0;
    jacobian(0, 2) = 100.0;
  
    double residual = 1.0;
    double x[3];
    DenseSparseMatrix dsm(jacobian);
  
    TrustRegionStrategy::Options options;
    options.initial_radius = 2.0;
    options.max_radius = 20.0;
    options.min_lm_diagonal = 1e-2;
    options.max_lm_diagonal = 1e2;
  
    double diagonal[3];
    diagonal[0] = options.min_lm_diagonal;
    diagonal[1] = 2.0;
    diagonal[2] = options.max_lm_diagonal;
    for (int i = 0; i < 3; ++i) {
      diagonal[i] = sqrt(diagonal[i] / options.initial_radius);
    }
  
    RegularizationCheckingLinearSolver linear_solver(3, diagonal);
    options.linear_solver = &linear_solver;
  
    LevenbergMarquardtStrategy lms(options);
    TrustRegionStrategy::PerSolveOptions pso;
  
    {
      ScopedMockLog log;
      EXPECT_CALL(log, Log(_, _, _)).Times(AnyNumber());
      EXPECT_CALL(log, Log(WARNING, _,
                           HasSubstr("Failed to compute a finite step.")));
  
      TrustRegionStrategy::Summary summary =
          lms.ComputeStep(pso, &dsm, &residual, x);
      EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_FAILURE);
    }
  }
  
  }  // namespace internal
  }  // namespace ceres
  