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diff --git a/internal/ceres/levenberg_marquardt_strategy_test.cc b/internal/ceres/levenberg_marquardt_strategy_test.cc
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+// 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/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.lm_min_diagonal = 1e-8;
+  options.lm_max_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.lm_min_diagonal = 1e-2;
+  options.lm_max_diagonal = 1e2;
+
+  double diagonal[3];
+  diagonal[0] = options.lm_min_diagonal;
+  diagonal[1] = 2.0;
+  diagonal[2] = options.lm_max_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, FAILURE);
+  }
+}
+
+}  // namespace internal
+}  // namespace ceres