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

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2015 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.
 //
 // 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: keir@google.com (Keir Mierle)
 //
 // Tests shared across evaluators. The tests try all combinations of linear
 // solver and num_eliminate_blocks (for schur-based solvers).

 #include "ceres/evaluator.h"

 #include "ceres/casts.h"
 #include "ceres/cost_function.h"
 #include "ceres/crs_matrix.h"
 #include "ceres/evaluator_test_utils.h"
 #include "ceres/internal/eigen.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/local_parameterization.h"
 #include "ceres/problem_impl.h"
 #include "ceres/program.h"
 #include "ceres/sized_cost_function.h"
 #include "ceres/sparse_matrix.h"
 #include "ceres/stringprintf.h"
 #include "ceres/types.h"
 #include "gtest/gtest.h"

 namespace ceres {
 namespace internal {

 using std::string;
 using std::vector;

 // TODO(keir): Consider pushing this into a common test utils file.
 template<int kFactor, int kNumResiduals,
          int N0 = 0, int N1 = 0, int N2 = 0, bool kSucceeds = true>
 class ParameterIgnoringCostFunction
     : public SizedCostFunction<kNumResiduals, N0, N1, N2> {
   typedef SizedCostFunction<kNumResiduals, N0, N1, N2> Base;
  public:
   virtual bool Evaluate(double const* const* parameters,
                         double* residuals,
                         double** jacobians) const {
     for (int i = 0; i < Base::num_residuals(); ++i) {
       residuals[i] = i + 1;
     }
     if (jacobians) {
       for (int k = 0; k < Base::parameter_block_sizes().size(); ++k) {
         // The jacobians here are full sized, but they are transformed in the
         // evaluator into the "local" jacobian. In the tests, the "subset
         // constant" parameterization is used, which should pick out columns
         // from these jacobians. Put values in the jacobian that make this
         // obvious; in particular, make the jacobians like this:
         //
         //   1 2 3 4 ...
         //   1 2 3 4 ...   .*  kFactor
         //   1 2 3 4 ...
         //
         // where the multiplication by kFactor makes it easier to distinguish
         // between Jacobians of different residuals for the same parameter.
         if (jacobians[k] != NULL) {
           MatrixRef jacobian(jacobians[k],
                              Base::num_residuals(),
                              Base::parameter_block_sizes()[k]);
           for (int j = 0; j < Base::parameter_block_sizes()[k]; ++j) {
             jacobian.col(j).setConstant(kFactor * (j + 1));
           }
         }
       }
     }
     return kSucceeds;
   }
 };

 struct EvaluatorTestOptions {
   EvaluatorTestOptions(LinearSolverType linear_solver_type,
                        int num_eliminate_blocks,
                        bool dynamic_sparsity = false)
     : linear_solver_type(linear_solver_type),
       num_eliminate_blocks(num_eliminate_blocks),
       dynamic_sparsity(dynamic_sparsity) {}

   LinearSolverType linear_solver_type;
   int num_eliminate_blocks;
   bool dynamic_sparsity;
 };

 struct EvaluatorTest
     : public ::testing::TestWithParam<EvaluatorTestOptions> {
   Evaluator* CreateEvaluator(Program* program) {
     // This program is straight from the ProblemImpl, and so has no index/offset
     // yet; compute it here as required by the evalutor implementations.
     program->SetParameterOffsetsAndIndex();

     if (VLOG_IS_ON(1)) {
       string report;
       StringAppendF(&report, "Creating evaluator with type: %d",
                     GetParam().linear_solver_type);
       if (GetParam().linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
         StringAppendF(&report, ", dynamic_sparsity: %d",
                       GetParam().dynamic_sparsity);
       }
       StringAppendF(&report, " and num_eliminate_blocks: %d",
                     GetParam().num_eliminate_blocks);
       VLOG(1) << report;
     }
     Evaluator::Options options;
     options.linear_solver_type = GetParam().linear_solver_type;
     options.num_eliminate_blocks = GetParam().num_eliminate_blocks;
     options.dynamic_sparsity = GetParam().dynamic_sparsity;
     options.context = problem.context();
     string error;
     return Evaluator::Create(options, program, &error);
   }

   void EvaluateAndCompare(ProblemImpl *problem,
                           int expected_num_rows,
                           int expected_num_cols,
                           double expected_cost,
                           const double* expected_residuals,
                           const double* expected_gradient,
                           const double* expected_jacobian) {
     scoped_ptr<Evaluator> evaluator(
         CreateEvaluator(problem->mutable_program()));
     int num_residuals = expected_num_rows;
     int num_parameters = expected_num_cols;

     double cost = -1;

     Vector residuals(num_residuals);
     residuals.setConstant(-2000);

     Vector gradient(num_parameters);
     gradient.setConstant(-3000);

     scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());

     ASSERT_EQ(expected_num_rows, evaluator->NumResiduals());
     ASSERT_EQ(expected_num_cols, evaluator->NumEffectiveParameters());
     ASSERT_EQ(expected_num_rows, jacobian->num_rows());
     ASSERT_EQ(expected_num_cols, jacobian->num_cols());

     vector<double> state(evaluator->NumParameters());

     ASSERT_TRUE(evaluator->Evaluate(
           &state[0],
           &cost,
           expected_residuals != NULL ? &residuals[0]  : NULL,
           expected_gradient  != NULL ? &gradient[0]   : NULL,
           expected_jacobian  != NULL ? jacobian.get() : NULL));

     Matrix actual_jacobian;
     if (expected_jacobian != NULL) {
       jacobian->ToDenseMatrix(&actual_jacobian);
     }

     CompareEvaluations(expected_num_rows,
                        expected_num_cols,
                        expected_cost,
                        expected_residuals,
                        expected_gradient,
                        expected_jacobian,
                        cost,
                        &residuals[0],
                        &gradient[0],
                        actual_jacobian.data());
   }

   // Try all combinations of parameters for the evaluator.
   void CheckAllEvaluationCombinations(const ExpectedEvaluation &expected) {
     for (int i = 0; i < 8; ++i) {
       EvaluateAndCompare(&problem,
                          expected.num_rows,
                          expected.num_cols,
                          expected.cost,
                          (i & 1) ? expected.residuals : NULL,
                          (i & 2) ? expected.gradient  : NULL,
                          (i & 4) ? expected.jacobian  : NULL);
     }
   }

   // The values are ignored completely by the cost function.
   double x[2];
   double y[3];
   double z[4];

   ProblemImpl problem;
 };

 void SetSparseMatrixConstant(SparseMatrix* sparse_matrix, double value) {
   VectorRef(sparse_matrix->mutable_values(),
             sparse_matrix->num_nonzeros()).setConstant(value);
 }

 TEST_P(EvaluatorTest, SingleResidualProblem) {
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>,
                            NULL,
                            x, y, z);

   ExpectedEvaluation expected = {
     // Rows/columns
     3, 9,
     // Cost
     7.0,
     // Residuals
     { 1.0, 2.0, 3.0 },
     // Gradient
     { 6.0, 12.0,              // x
       6.0, 12.0, 18.0,        // y
       6.0, 12.0, 18.0, 24.0,  // z
     },
     // Jacobian
     //   x          y             z
     { 1, 2,   1, 2, 3,   1, 2, 3, 4,
       1, 2,   1, 2, 3,   1, 2, 3, 4,
       1, 2,   1, 2, 3,   1, 2, 3, 4
     }
   };
   CheckAllEvaluationCombinations(expected);
 }

 TEST_P(EvaluatorTest, SingleResidualProblemWithPermutedParameters) {
   // Add the parameters in explicit order to force the ordering in the program.
   problem.AddParameterBlock(x,  2);
   problem.AddParameterBlock(y,  3);
   problem.AddParameterBlock(z,  4);

   // Then use a cost function which is similar to the others, but swap around
   // the ordering of the parameters to the cost function. This shouldn't affect
   // the jacobian evaluation, but requires explicit handling in the evaluators.
   // At one point the compressed row evaluator had a bug that went undetected
   // for a long time, since by chance most users added parameters to the problem
   // in the same order that they occurred as parameters to a cost function.
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 4, 3, 2>,
                            NULL,
                            z, y, x);

   ExpectedEvaluation expected = {
     // Rows/columns
     3, 9,
     // Cost
     7.0,
     // Residuals
     { 1.0, 2.0, 3.0 },
     // Gradient
     { 6.0, 12.0,              // x
       6.0, 12.0, 18.0,        // y
       6.0, 12.0, 18.0, 24.0,  // z
     },
     // Jacobian
     //   x          y             z
     { 1, 2,   1, 2, 3,   1, 2, 3, 4,
       1, 2,   1, 2, 3,   1, 2, 3, 4,
       1, 2,   1, 2, 3,   1, 2, 3, 4
     }
   };
   CheckAllEvaluationCombinations(expected);
 }

 TEST_P(EvaluatorTest, SingleResidualProblemWithNuisanceParameters) {
   // These parameters are not used.
   double a[2];
   double b[1];
   double c[1];
   double d[3];

   // Add the parameters in a mixed order so the Jacobian is "checkered" with the
   // values from the other parameters.
   problem.AddParameterBlock(a, 2);
   problem.AddParameterBlock(x, 2);
   problem.AddParameterBlock(b, 1);
   problem.AddParameterBlock(y, 3);
   problem.AddParameterBlock(c, 1);
   problem.AddParameterBlock(z, 4);
   problem.AddParameterBlock(d, 3);

   problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>,
                            NULL,
                            x, y, z);

   ExpectedEvaluation expected = {
     // Rows/columns
     3, 16,
     // Cost
     7.0,
     // Residuals
     { 1.0, 2.0, 3.0 },
     // Gradient
     { 0.0, 0.0,               // a
       6.0, 12.0,              // x
       0.0,                    // b
       6.0, 12.0, 18.0,        // y
       0.0,                    // c
       6.0, 12.0, 18.0, 24.0,  // z
       0.0, 0.0, 0.0,          // d
     },
     // Jacobian
     //   a        x     b           y     c              z           d
     { 0, 0,    1, 2,    0,    1, 2, 3,    0,    1, 2, 3, 4,    0, 0, 0,
       0, 0,    1, 2,    0,    1, 2, 3,    0,    1, 2, 3, 4,    0, 0, 0,
       0, 0,    1, 2,    0,    1, 2, 3,    0,    1, 2, 3, 4,    0, 0, 0
     }
   };
   CheckAllEvaluationCombinations(expected);
 }

 TEST_P(EvaluatorTest, MultipleResidualProblem) {
   // Add the parameters in explicit order to force the ordering in the program.
   problem.AddParameterBlock(x,  2);
   problem.AddParameterBlock(y,  3);
   problem.AddParameterBlock(z,  4);

   // f(x, y) in R^2
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
                            NULL,
                            x, y);

   // g(x, z) in R^3
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
                            NULL,
                            x, z);

   // h(y, z) in R^4
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
                            NULL,
                            y, z);

   ExpectedEvaluation expected = {
     // Rows/columns
     9, 9,
     // Cost
     // f       g           h
     (  1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0,
     // Residuals
     { 1.0, 2.0,           // f
       1.0, 2.0, 3.0,      // g
       1.0, 2.0, 3.0, 4.0  // h
     },
     // Gradient
     { 15.0, 30.0,               // x
       33.0, 66.0, 99.0,         // y
       42.0, 84.0, 126.0, 168.0  // z
     },
     // Jacobian
     //                x        y           z
     {   /* f(x, y) */ 1, 2,    1, 2, 3,    0, 0, 0, 0,
                       1, 2,    1, 2, 3,    0, 0, 0, 0,

         /* g(x, z) */ 2, 4,    0, 0, 0,    2, 4, 6, 8,
                       2, 4,    0, 0, 0,    2, 4, 6, 8,
                       2, 4,    0, 0, 0,    2, 4, 6, 8,

         /* h(y, z) */ 0, 0,    3, 6, 9,    3, 6, 9, 12,
                       0, 0,    3, 6, 9,    3, 6, 9, 12,
                       0, 0,    3, 6, 9,    3, 6, 9, 12,
                       0, 0,    3, 6, 9,    3, 6, 9, 12
     }
   };
   CheckAllEvaluationCombinations(expected);
 }

 TEST_P(EvaluatorTest, MultipleResidualsWithLocalParameterizations) {
   // Add the parameters in explicit order to force the ordering in the program.
   problem.AddParameterBlock(x,  2);

   // Fix y's first dimension.
   vector<int> y_fixed;
   y_fixed.push_back(0);
   problem.AddParameterBlock(y, 3, new SubsetParameterization(3, y_fixed));

   // Fix z's second dimension.
   vector<int> z_fixed;
   z_fixed.push_back(1);
   problem.AddParameterBlock(z, 4, new SubsetParameterization(4, z_fixed));

   // f(x, y) in R^2
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
                            NULL,
                            x, y);

   // g(x, z) in R^3
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
                            NULL,
                            x, z);

   // h(y, z) in R^4
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
                            NULL,
                            y, z);

   ExpectedEvaluation expected = {
     // Rows/columns
     9, 7,
     // Cost
     // f       g           h
     (  1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0,
     // Residuals
     { 1.0, 2.0,           // f
       1.0, 2.0, 3.0,      // g
       1.0, 2.0, 3.0, 4.0  // h
     },
     // Gradient
     { 15.0, 30.0,         // x
       66.0, 99.0,         // y
       42.0, 126.0, 168.0  // z
     },
     // Jacobian
     //                x        y           z
     {   /* f(x, y) */ 1, 2,    2, 3,    0, 0, 0,
                       1, 2,    2, 3,    0, 0, 0,

         /* g(x, z) */ 2, 4,    0, 0,    2, 6, 8,
                       2, 4,    0, 0,    2, 6, 8,
                       2, 4,    0, 0,    2, 6, 8,

         /* h(y, z) */ 0, 0,    6, 9,    3, 9, 12,
                       0, 0,    6, 9,    3, 9, 12,
                       0, 0,    6, 9,    3, 9, 12,
                       0, 0,    6, 9,    3, 9, 12
     }
   };
   CheckAllEvaluationCombinations(expected);
 }

 TEST_P(EvaluatorTest, MultipleResidualProblemWithSomeConstantParameters) {
   // The values are ignored completely by the cost function.
   double x[2];
   double y[3];
   double z[4];

   // Add the parameters in explicit order to force the ordering in the program.
   problem.AddParameterBlock(x,  2);
   problem.AddParameterBlock(y,  3);
   problem.AddParameterBlock(z,  4);

   // f(x, y) in R^2
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
                            NULL,
                            x, y);

   // g(x, z) in R^3
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
                            NULL,
                            x, z);

   // h(y, z) in R^4
   problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
                            NULL,
                            y, z);

   // For this test, "z" is constant.
   problem.SetParameterBlockConstant(z);

   // Create the reduced program which is missing the fixed "z" variable.
   // Normally, the preprocessing of the program that happens in solver_impl
   // takes care of this, but we don't want to invoke the solver here.
   Program reduced_program;
   vector<ParameterBlock*>* parameter_blocks =
       problem.mutable_program()->mutable_parameter_blocks();

   // "z" is the last parameter; save it for later and pop it off temporarily.
   // Note that "z" will still get read during evaluation, so it cannot be
   // deleted at this point.
   ParameterBlock* parameter_block_z = parameter_blocks->back();
   parameter_blocks->pop_back();

   ExpectedEvaluation expected = {
     // Rows/columns
     9, 5,
     // Cost
     // f       g           h
     (  1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0,
     // Residuals
     { 1.0, 2.0,           // f
       1.0, 2.0, 3.0,      // g
       1.0, 2.0, 3.0, 4.0  // h
     },
     // Gradient
     { 15.0, 30.0,        // x
       33.0, 66.0, 99.0,  // y
     },
     // Jacobian
     //                x        y
     {   /* f(x, y) */ 1, 2,    1, 2, 3,
                       1, 2,    1, 2, 3,

         /* g(x, z) */ 2, 4,    0, 0, 0,
                       2, 4,    0, 0, 0,
                       2, 4,    0, 0, 0,

         /* h(y, z) */ 0, 0,    3, 6, 9,
                       0, 0,    3, 6, 9,
                       0, 0,    3, 6, 9,
                       0, 0,    3, 6, 9
     }
   };
   CheckAllEvaluationCombinations(expected);

   // Restore parameter block z, so it will get freed in a consistent way.
   parameter_blocks->push_back(parameter_block_z);
 }

 TEST_P(EvaluatorTest, EvaluatorAbortsForResidualsThatFailToEvaluate) {
   // Switch the return value to failure.
   problem.AddResidualBlock(
       new ParameterIgnoringCostFunction<20, 3, 2, 3, 4, false>, NULL, x, y, z);

   // The values are ignored.
   double state[9];

   scoped_ptr<Evaluator> evaluator(CreateEvaluator(problem.mutable_program()));
   scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());
   double cost;
   EXPECT_FALSE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL));
 }

 // In the pairs, the first argument is the linear solver type, and the second
 // argument is num_eliminate_blocks. Changing the num_eliminate_blocks only
 // makes sense for the schur-based solvers.
 //
 // Try all values of num_eliminate_blocks that make sense given that in the
 // tests a maximum of 4 parameter blocks are present.
 INSTANTIATE_TEST_CASE_P(
     LinearSolvers,
     EvaluatorTest,
     ::testing::Values(
       EvaluatorTestOptions(DENSE_QR, 0),
       EvaluatorTestOptions(DENSE_SCHUR, 0),
       EvaluatorTestOptions(DENSE_SCHUR, 1),
       EvaluatorTestOptions(DENSE_SCHUR, 2),
       EvaluatorTestOptions(DENSE_SCHUR, 3),
       EvaluatorTestOptions(DENSE_SCHUR, 4),
       EvaluatorTestOptions(SPARSE_SCHUR, 0),
       EvaluatorTestOptions(SPARSE_SCHUR, 1),
       EvaluatorTestOptions(SPARSE_SCHUR, 2),
       EvaluatorTestOptions(SPARSE_SCHUR, 3),
       EvaluatorTestOptions(SPARSE_SCHUR, 4),
       EvaluatorTestOptions(ITERATIVE_SCHUR, 0),
       EvaluatorTestOptions(ITERATIVE_SCHUR, 1),
       EvaluatorTestOptions(ITERATIVE_SCHUR, 2),
       EvaluatorTestOptions(ITERATIVE_SCHUR, 3),
       EvaluatorTestOptions(ITERATIVE_SCHUR, 4),
       EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, false),
       EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, true)));

 // Simple cost function used to check if the evaluator is sensitive to
 // state changes.
 class ParameterSensitiveCostFunction : public SizedCostFunction<2, 2> {
  public:
   virtual bool Evaluate(double const* const* parameters,
                         double* residuals,
                         double** jacobians) const {
     double x1 = parameters[0][0];
     double x2 = parameters[0][1];
     residuals[0] = x1 * x1;
     residuals[1] = x2 * x2;

     if (jacobians != NULL) {
       double* jacobian = jacobians[0];
       if (jacobian != NULL) {
         jacobian[0] = 2.0 * x1;
         jacobian[1] = 0.0;
         jacobian[2] = 0.0;
         jacobian[3] = 2.0 * x2;
       }
     }
     return true;
   }
 };

 TEST(Evaluator, EvaluatorRespectsParameterChanges) {
   ProblemImpl problem;

   double x[2];
   x[0] = 1.0;
   x[1] = 1.0;

   problem.AddResidualBlock(new ParameterSensitiveCostFunction(), NULL, x);
   Program* program = problem.mutable_program();
   program->SetParameterOffsetsAndIndex();

   Evaluator::Options options;
   options.linear_solver_type = DENSE_QR;
   options.num_eliminate_blocks = 0;
   options.context = problem.context();
   string error;
   scoped_ptr<Evaluator> evaluator(Evaluator::Create(options, program, &error));
   scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());

   ASSERT_EQ(2, jacobian->num_rows());
   ASSERT_EQ(2, jacobian->num_cols());

   double state[2];
   state[0] = 2.0;
   state[1] = 3.0;

   // The original state of a residual block comes from the user's
   // state. So the original state is 1.0, 1.0, and the only way we get
   // the 2.0, 3.0 results in the following tests is if it respects the
   // values in the state vector.

   // Cost only; no residuals and no jacobian.
   {
     double cost = -1;
     ASSERT_TRUE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL));
     EXPECT_EQ(48.5, cost);
   }

   // Cost and residuals, no jacobian.
   {
     double cost = -1;
     double residuals[2] = { -2, -2 };
     ASSERT_TRUE(evaluator->Evaluate(state, &cost, residuals, NULL, NULL));
     EXPECT_EQ(48.5, cost);
     EXPECT_EQ(4, residuals[0]);
     EXPECT_EQ(9, residuals[1]);
   }

   // Cost, residuals, and jacobian.
   {
     double cost = -1;
     double residuals[2] = { -2, -2};
     SetSparseMatrixConstant(jacobian.get(), -1);
     ASSERT_TRUE(evaluator->Evaluate(state,
                                     &cost,
                                     residuals,
                                     NULL,
                                     jacobian.get()));
     EXPECT_EQ(48.5, cost);
     EXPECT_EQ(4, residuals[0]);
     EXPECT_EQ(9, residuals[1]);
     Matrix actual_jacobian;
     jacobian->ToDenseMatrix(&actual_jacobian);

     Matrix expected_jacobian(2, 2);
     expected_jacobian
         << 2 * state[0], 0,
            0, 2 * state[1];

     EXPECT_TRUE((actual_jacobian.array() == expected_jacobian.array()).all())
         << "Actual:\n" << actual_jacobian
         << "\nExpected:\n" << expected_jacobian;
   }
 }

 }  // namespace internal
 }  // namespace ceres
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2015 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.
	//
	// 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: keir@google.com (Keir Mierle)
	//
	// Tests shared across evaluators. The tests try all combinations of linear
	// solver and num_eliminate_blocks (for schur-based solvers).

	#include "ceres/evaluator.h"

	#include "ceres/casts.h"
	#include "ceres/cost_function.h"
	#include "ceres/crs_matrix.h"
	#include "ceres/evaluator_test_utils.h"
	#include "ceres/internal/eigen.h"
	#include "ceres/internal/scoped_ptr.h"
	#include "ceres/local_parameterization.h"
	#include "ceres/problem_impl.h"
	#include "ceres/program.h"
	#include "ceres/sized_cost_function.h"
	#include "ceres/sparse_matrix.h"
	#include "ceres/stringprintf.h"
	#include "ceres/types.h"
	#include "gtest/gtest.h"

	namespace ceres {
	namespace internal {

	using std::string;
	using std::vector;

	// TODO(keir): Consider pushing this into a common test utils file.
	template<int kFactor, int kNumResiduals,
	int N0 = 0, int N1 = 0, int N2 = 0, bool kSucceeds = true>
	class ParameterIgnoringCostFunction
	: public SizedCostFunction<kNumResiduals, N0, N1, N2> {
	typedef SizedCostFunction<kNumResiduals, N0, N1, N2> Base;
	public:
	virtual bool Evaluate(double const* const* parameters,
	double* residuals,
	double** jacobians) const {
	for (int i = 0; i < Base::num_residuals(); ++i) {
	residuals[i] = i + 1;
	}
	if (jacobians) {
	for (int k = 0; k < Base::parameter_block_sizes().size(); ++k) {
	// The jacobians here are full sized, but they are transformed in the
	// evaluator into the "local" jacobian. In the tests, the "subset
	// constant" parameterization is used, which should pick out columns
	// from these jacobians. Put values in the jacobian that make this
	// obvious; in particular, make the jacobians like this:
	//
	// 1 2 3 4 ...
	// 1 2 3 4 ... .* kFactor
	// 1 2 3 4 ...
	//
	// where the multiplication by kFactor makes it easier to distinguish
	// between Jacobians of different residuals for the same parameter.
	if (jacobians[k] != NULL) {
	MatrixRef jacobian(jacobians[k],
	Base::num_residuals(),
	Base::parameter_block_sizes()[k]);
	for (int j = 0; j < Base::parameter_block_sizes()[k]; ++j) {
	jacobian.col(j).setConstant(kFactor * (j + 1));
	}
	}
	}
	}
	return kSucceeds;
	}
	};

	struct EvaluatorTestOptions {
	EvaluatorTestOptions(LinearSolverType linear_solver_type,
	int num_eliminate_blocks,
	bool dynamic_sparsity = false)
	: linear_solver_type(linear_solver_type),
	num_eliminate_blocks(num_eliminate_blocks),
	dynamic_sparsity(dynamic_sparsity) {}

	LinearSolverType linear_solver_type;
	int num_eliminate_blocks;
	bool dynamic_sparsity;
	};

	struct EvaluatorTest
	: public ::testing::TestWithParam<EvaluatorTestOptions> {
	Evaluator* CreateEvaluator(Program* program) {
	// This program is straight from the ProblemImpl, and so has no index/offset
	// yet; compute it here as required by the evalutor implementations.
	program->SetParameterOffsetsAndIndex();

	if (VLOG_IS_ON(1)) {
	string report;
	StringAppendF(&report, "Creating evaluator with type: %d",
	GetParam().linear_solver_type);
	if (GetParam().linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
	StringAppendF(&report, ", dynamic_sparsity: %d",
	GetParam().dynamic_sparsity);
	}
	StringAppendF(&report, " and num_eliminate_blocks: %d",
	GetParam().num_eliminate_blocks);
	VLOG(1) << report;
	}
	Evaluator::Options options;
	options.linear_solver_type = GetParam().linear_solver_type;
	options.num_eliminate_blocks = GetParam().num_eliminate_blocks;
	options.dynamic_sparsity = GetParam().dynamic_sparsity;
	options.context = problem.context();
	string error;
	return Evaluator::Create(options, program, &error);
	}

	void EvaluateAndCompare(ProblemImpl *problem,
	int expected_num_rows,
	int expected_num_cols,
	double expected_cost,
	const double* expected_residuals,
	const double* expected_gradient,
	const double* expected_jacobian) {
	scoped_ptr<Evaluator> evaluator(
	CreateEvaluator(problem->mutable_program()));
	int num_residuals = expected_num_rows;
	int num_parameters = expected_num_cols;

	double cost = -1;

	Vector residuals(num_residuals);
	residuals.setConstant(-2000);

	Vector gradient(num_parameters);
	gradient.setConstant(-3000);

	scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());

	ASSERT_EQ(expected_num_rows, evaluator->NumResiduals());
	ASSERT_EQ(expected_num_cols, evaluator->NumEffectiveParameters());
	ASSERT_EQ(expected_num_rows, jacobian->num_rows());
	ASSERT_EQ(expected_num_cols, jacobian->num_cols());

	vector<double> state(evaluator->NumParameters());

	ASSERT_TRUE(evaluator->Evaluate(
	&state[0],
	&cost,
	expected_residuals != NULL ? &residuals[0] : NULL,
	expected_gradient != NULL ? &gradient[0] : NULL,
	expected_jacobian != NULL ? jacobian.get() : NULL));

	Matrix actual_jacobian;
	if (expected_jacobian != NULL) {
	jacobian->ToDenseMatrix(&actual_jacobian);
	}

	CompareEvaluations(expected_num_rows,
	expected_num_cols,
	expected_cost,
	expected_residuals,
	expected_gradient,
	expected_jacobian,
	cost,
	&residuals[0],
	&gradient[0],
	actual_jacobian.data());
	}

	// Try all combinations of parameters for the evaluator.
	void CheckAllEvaluationCombinations(const ExpectedEvaluation &expected) {
	for (int i = 0; i < 8; ++i) {
	EvaluateAndCompare(&problem,
	expected.num_rows,
	expected.num_cols,
	expected.cost,
	(i & 1) ? expected.residuals : NULL,
	(i & 2) ? expected.gradient : NULL,
	(i & 4) ? expected.jacobian : NULL);
	}
	}

	// The values are ignored completely by the cost function.
	double x[2];
	double y[3];
	double z[4];

	ProblemImpl problem;
	};

	void SetSparseMatrixConstant(SparseMatrix* sparse_matrix, double value) {
	VectorRef(sparse_matrix->mutable_values(),
	sparse_matrix->num_nonzeros()).setConstant(value);
	}

	TEST_P(EvaluatorTest, SingleResidualProblem) {
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>,
	NULL,
	x, y, z);

	ExpectedEvaluation expected = {
	// Rows/columns
	3, 9,
	// Cost
	7.0,
	// Residuals
	{ 1.0, 2.0, 3.0 },
	// Gradient
	{ 6.0, 12.0, // x
	6.0, 12.0, 18.0, // y
	6.0, 12.0, 18.0, 24.0, // z
	},
	// Jacobian
	// x y z
	{ 1, 2, 1, 2, 3, 1, 2, 3, 4,
	1, 2, 1, 2, 3, 1, 2, 3, 4,
	1, 2, 1, 2, 3, 1, 2, 3, 4
	}
	};
	CheckAllEvaluationCombinations(expected);
	}

	TEST_P(EvaluatorTest, SingleResidualProblemWithPermutedParameters) {
	// Add the parameters in explicit order to force the ordering in the program.
	problem.AddParameterBlock(x, 2);
	problem.AddParameterBlock(y, 3);
	problem.AddParameterBlock(z, 4);

	// Then use a cost function which is similar to the others, but swap around
	// the ordering of the parameters to the cost function. This shouldn't affect
	// the jacobian evaluation, but requires explicit handling in the evaluators.
	// At one point the compressed row evaluator had a bug that went undetected
	// for a long time, since by chance most users added parameters to the problem
	// in the same order that they occurred as parameters to a cost function.
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 4, 3, 2>,
	NULL,
	z, y, x);

	ExpectedEvaluation expected = {
	// Rows/columns
	3, 9,
	// Cost
	7.0,
	// Residuals
	{ 1.0, 2.0, 3.0 },
	// Gradient
	{ 6.0, 12.0, // x
	6.0, 12.0, 18.0, // y
	6.0, 12.0, 18.0, 24.0, // z
	},
	// Jacobian
	// x y z
	{ 1, 2, 1, 2, 3, 1, 2, 3, 4,
	1, 2, 1, 2, 3, 1, 2, 3, 4,
	1, 2, 1, 2, 3, 1, 2, 3, 4
	}
	};
	CheckAllEvaluationCombinations(expected);
	}

	TEST_P(EvaluatorTest, SingleResidualProblemWithNuisanceParameters) {
	// These parameters are not used.
	double a[2];
	double b[1];
	double c[1];
	double d[3];

	// Add the parameters in a mixed order so the Jacobian is "checkered" with the
	// values from the other parameters.
	problem.AddParameterBlock(a, 2);
	problem.AddParameterBlock(x, 2);
	problem.AddParameterBlock(b, 1);
	problem.AddParameterBlock(y, 3);
	problem.AddParameterBlock(c, 1);
	problem.AddParameterBlock(z, 4);
	problem.AddParameterBlock(d, 3);

	problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>,
	NULL,
	x, y, z);

	ExpectedEvaluation expected = {
	// Rows/columns
	3, 16,
	// Cost
	7.0,
	// Residuals
	{ 1.0, 2.0, 3.0 },
	// Gradient
	{ 0.0, 0.0, // a
	6.0, 12.0, // x
	0.0, // b
	6.0, 12.0, 18.0, // y
	0.0, // c
	6.0, 12.0, 18.0, 24.0, // z
	0.0, 0.0, 0.0, // d
	},
	// Jacobian
	// a x b y c z d
	{ 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 0,
	0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 0,
	0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 0
	}
	};
	CheckAllEvaluationCombinations(expected);
	}

	TEST_P(EvaluatorTest, MultipleResidualProblem) {
	// Add the parameters in explicit order to force the ordering in the program.
	problem.AddParameterBlock(x, 2);
	problem.AddParameterBlock(y, 3);
	problem.AddParameterBlock(z, 4);

	// f(x, y) in R^2
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
	NULL,
	x, y);

	// g(x, z) in R^3
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
	NULL,
	x, z);

	// h(y, z) in R^4
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
	NULL,
	y, z);

	ExpectedEvaluation expected = {
	// Rows/columns
	9, 9,
	// Cost
	// f g h
	( 1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0,
	// Residuals
	{ 1.0, 2.0, // f
	1.0, 2.0, 3.0, // g
	1.0, 2.0, 3.0, 4.0 // h
	},
	// Gradient
	{ 15.0, 30.0, // x
	33.0, 66.0, 99.0, // y
	42.0, 84.0, 126.0, 168.0 // z
	},
	// Jacobian
	// x y z
	{ /* f(x, y) */ 1, 2, 1, 2, 3, 0, 0, 0, 0,
	1, 2, 1, 2, 3, 0, 0, 0, 0,

	/* g(x, z) */ 2, 4, 0, 0, 0, 2, 4, 6, 8,
	2, 4, 0, 0, 0, 2, 4, 6, 8,
	2, 4, 0, 0, 0, 2, 4, 6, 8,

	/* h(y, z) */ 0, 0, 3, 6, 9, 3, 6, 9, 12,
	0, 0, 3, 6, 9, 3, 6, 9, 12,
	0, 0, 3, 6, 9, 3, 6, 9, 12,
	0, 0, 3, 6, 9, 3, 6, 9, 12
	}
	};
	CheckAllEvaluationCombinations(expected);
	}

	TEST_P(EvaluatorTest, MultipleResidualsWithLocalParameterizations) {
	// Add the parameters in explicit order to force the ordering in the program.
	problem.AddParameterBlock(x, 2);

	// Fix y's first dimension.
	vector<int> y_fixed;
	y_fixed.push_back(0);
	problem.AddParameterBlock(y, 3, new SubsetParameterization(3, y_fixed));

	// Fix z's second dimension.
	vector<int> z_fixed;
	z_fixed.push_back(1);
	problem.AddParameterBlock(z, 4, new SubsetParameterization(4, z_fixed));

	// f(x, y) in R^2
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
	NULL,
	x, y);

	// g(x, z) in R^3
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
	NULL,
	x, z);

	// h(y, z) in R^4
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
	NULL,
	y, z);

	ExpectedEvaluation expected = {
	// Rows/columns
	9, 7,
	// Cost
	// f g h
	( 1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0,
	// Residuals
	{ 1.0, 2.0, // f
	1.0, 2.0, 3.0, // g
	1.0, 2.0, 3.0, 4.0 // h
	},
	// Gradient
	{ 15.0, 30.0, // x
	66.0, 99.0, // y
	42.0, 126.0, 168.0 // z
	},
	// Jacobian
	// x y z
	{ /* f(x, y) */ 1, 2, 2, 3, 0, 0, 0,
	1, 2, 2, 3, 0, 0, 0,

	/* g(x, z) */ 2, 4, 0, 0, 2, 6, 8,
	2, 4, 0, 0, 2, 6, 8,
	2, 4, 0, 0, 2, 6, 8,

	/* h(y, z) */ 0, 0, 6, 9, 3, 9, 12,
	0, 0, 6, 9, 3, 9, 12,
	0, 0, 6, 9, 3, 9, 12,
	0, 0, 6, 9, 3, 9, 12
	}
	};
	CheckAllEvaluationCombinations(expected);
	}

	TEST_P(EvaluatorTest, MultipleResidualProblemWithSomeConstantParameters) {
	// The values are ignored completely by the cost function.
	double x[2];
	double y[3];
	double z[4];

	// Add the parameters in explicit order to force the ordering in the program.
	problem.AddParameterBlock(x, 2);
	problem.AddParameterBlock(y, 3);
	problem.AddParameterBlock(z, 4);

	// f(x, y) in R^2
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
	NULL,
	x, y);

	// g(x, z) in R^3
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
	NULL,
	x, z);

	// h(y, z) in R^4
	problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
	NULL,
	y, z);

	// For this test, "z" is constant.
	problem.SetParameterBlockConstant(z);

	// Create the reduced program which is missing the fixed "z" variable.
	// Normally, the preprocessing of the program that happens in solver_impl
	// takes care of this, but we don't want to invoke the solver here.
	Program reduced_program;
	vector<ParameterBlock> parameter_blocks =
	problem.mutable_program()->mutable_parameter_blocks();

	// "z" is the last parameter; save it for later and pop it off temporarily.
	// Note that "z" will still get read during evaluation, so it cannot be
	// deleted at this point.
	ParameterBlock* parameter_block_z = parameter_blocks->back();
	parameter_blocks->pop_back();

	ExpectedEvaluation expected = {
	// Rows/columns
	9, 5,
	// Cost
	// f g h
	( 1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0,
	// Residuals
	{ 1.0, 2.0, // f
	1.0, 2.0, 3.0, // g
	1.0, 2.0, 3.0, 4.0 // h
	},
	// Gradient
	{ 15.0, 30.0, // x
	33.0, 66.0, 99.0, // y
	},
	// Jacobian
	// x y
	{ /* f(x, y) */ 1, 2, 1, 2, 3,
	1, 2, 1, 2, 3,

	/* g(x, z) */ 2, 4, 0, 0, 0,
	2, 4, 0, 0, 0,
	2, 4, 0, 0, 0,

	/* h(y, z) */ 0, 0, 3, 6, 9,
	0, 0, 3, 6, 9,
	0, 0, 3, 6, 9,
	0, 0, 3, 6, 9
	}
	};
	CheckAllEvaluationCombinations(expected);

	// Restore parameter block z, so it will get freed in a consistent way.
	parameter_blocks->push_back(parameter_block_z);
	}

	TEST_P(EvaluatorTest, EvaluatorAbortsForResidualsThatFailToEvaluate) {
	// Switch the return value to failure.
	problem.AddResidualBlock(
	new ParameterIgnoringCostFunction<20, 3, 2, 3, 4, false>, NULL, x, y, z);

	// The values are ignored.
	double state[9];

	scoped_ptr<Evaluator> evaluator(CreateEvaluator(problem.mutable_program()));
	scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());
	double cost;
	EXPECT_FALSE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL));
	}

	// In the pairs, the first argument is the linear solver type, and the second
	// argument is num_eliminate_blocks. Changing the num_eliminate_blocks only
	// makes sense for the schur-based solvers.
	//
	// Try all values of num_eliminate_blocks that make sense given that in the
	// tests a maximum of 4 parameter blocks are present.
	INSTANTIATE_TEST_CASE_P(
	LinearSolvers,
	EvaluatorTest,
	::testing::Values(
	EvaluatorTestOptions(DENSE_QR, 0),
	EvaluatorTestOptions(DENSE_SCHUR, 0),
	EvaluatorTestOptions(DENSE_SCHUR, 1),
	EvaluatorTestOptions(DENSE_SCHUR, 2),
	EvaluatorTestOptions(DENSE_SCHUR, 3),
	EvaluatorTestOptions(DENSE_SCHUR, 4),
	EvaluatorTestOptions(SPARSE_SCHUR, 0),
	EvaluatorTestOptions(SPARSE_SCHUR, 1),
	EvaluatorTestOptions(SPARSE_SCHUR, 2),
	EvaluatorTestOptions(SPARSE_SCHUR, 3),
	EvaluatorTestOptions(SPARSE_SCHUR, 4),
	EvaluatorTestOptions(ITERATIVE_SCHUR, 0),
	EvaluatorTestOptions(ITERATIVE_SCHUR, 1),
	EvaluatorTestOptions(ITERATIVE_SCHUR, 2),
	EvaluatorTestOptions(ITERATIVE_SCHUR, 3),
	EvaluatorTestOptions(ITERATIVE_SCHUR, 4),
	EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, false),
	EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, true)));

	// Simple cost function used to check if the evaluator is sensitive to
	// state changes.
	class ParameterSensitiveCostFunction : public SizedCostFunction<2, 2> {
	public:
	virtual bool Evaluate(double const* const* parameters,
	double* residuals,
	double** jacobians) const {
	double x1 = parameters[0][0];
	double x2 = parameters[0][1];
	residuals[0] = x1 * x1;
	residuals[1] = x2 * x2;

	if (jacobians != NULL) {
	double* jacobian = jacobians[0];
	if (jacobian != NULL) {
	jacobian[0] = 2.0 * x1;
	jacobian[1] = 0.0;
	jacobian[2] = 0.0;
	jacobian[3] = 2.0 * x2;
	}
	}
	return true;
	}
	};

	TEST(Evaluator, EvaluatorRespectsParameterChanges) {
	ProblemImpl problem;

	double x[2];
	x[0] = 1.0;
	x[1] = 1.0;

	problem.AddResidualBlock(new ParameterSensitiveCostFunction(), NULL, x);
	Program* program = problem.mutable_program();
	program->SetParameterOffsetsAndIndex();

	Evaluator::Options options;
	options.linear_solver_type = DENSE_QR;
	options.num_eliminate_blocks = 0;
	options.context = problem.context();
	string error;
	scoped_ptr<Evaluator> evaluator(Evaluator::Create(options, program, &error));
	scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());

	ASSERT_EQ(2, jacobian->num_rows());
	ASSERT_EQ(2, jacobian->num_cols());

	double state[2];
	state[0] = 2.0;
	state[1] = 3.0;

	// The original state of a residual block comes from the user's
	// state. So the original state is 1.0, 1.0, and the only way we get
	// the 2.0, 3.0 results in the following tests is if it respects the
	// values in the state vector.

	// Cost only; no residuals and no jacobian.
	{
	double cost = -1;
	ASSERT_TRUE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL));
	EXPECT_EQ(48.5, cost);
	}

	// Cost and residuals, no jacobian.
	{
	double cost = -1;
	double residuals[2] = { -2, -2 };
	ASSERT_TRUE(evaluator->Evaluate(state, &cost, residuals, NULL, NULL));
	EXPECT_EQ(48.5, cost);
	EXPECT_EQ(4, residuals[0]);
	EXPECT_EQ(9, residuals[1]);
	}

	// Cost, residuals, and jacobian.
	{
	double cost = -1;
	double residuals[2] = { -2, -2};
	SetSparseMatrixConstant(jacobian.get(), -1);
	ASSERT_TRUE(evaluator->Evaluate(state,
	&cost,
	residuals,
	NULL,
	jacobian.get()));
	EXPECT_EQ(48.5, cost);
	EXPECT_EQ(4, residuals[0]);
	EXPECT_EQ(9, residuals[1]);
	Matrix actual_jacobian;
	jacobian->ToDenseMatrix(&actual_jacobian);

	Matrix expected_jacobian(2, 2);
	expected_jacobian
	<< 2 * state[0], 0,
	0, 2 * state[1];

	EXPECT_TRUE((actual_jacobian.array() == expected_jacobian.array()).all())
	<< "Actual:\n" << actual_jacobian
	<< "\nExpected:\n" << expected_jacobian;
	}
	}

	} // namespace internal
	} // namespace ceres