// 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

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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/solver.h"

#include <limits>

#include <cmath>

#include <vector>

#include "gtest/gtest.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/autodiff_cost_function.h"

#include "ceres/sized_cost_function.h"

#include "ceres/problem.h"

#include "ceres/problem_impl.h"

namespace ceres {

namespace internal {

using std::string;

TEST(SolverOptions, DefaultTrustRegionOptionsAreValid) {

  Solver::Options options;

  options.minimizer_type = TRUST_REGION;

  string error;

  EXPECT_TRUE(options.IsValid(&error)) << error;

}

TEST(SolverOptions, DefaultLineSearchOptionsAreValid) {

  Solver::Options options;

  options.minimizer_type = LINE_SEARCH;

  string error;

  EXPECT_TRUE(options.IsValid(&error)) << error;

}

struct QuadraticCostFunctor {

  template <typename T> bool operator()(const T* const x,

                                        T* residual) const {

    residual[0] = T(5.0) - *x;

    return true;

  }

  static CostFunction* Create() {

    return new AutoDiffCostFunction<QuadraticCostFunctor, 1, 1>(

        new QuadraticCostFunctor);

  }

};

struct RememberingCallback : public IterationCallback {

  explicit RememberingCallback(double *x) : calls(0), x(x) {}

  virtual ~RememberingCallback() {}

  virtual CallbackReturnType operator()(const IterationSummary& summary) {

    x_values.push_back(*x);

    return SOLVER_CONTINUE;

  }

  int calls;

  double *x;

  std::vector<double> x_values;

};

TEST(Solver, UpdateStateEveryIterationOption) {

  double x = 50.0;

  const double original_x = x;

  scoped_ptr<CostFunction> cost_function(QuadraticCostFunctor::Create());

  Problem::Options problem_options;

  problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP;

  Problem problem(problem_options);

  problem.AddResidualBlock(cost_function.get(), NULL, &x);

  Solver::Options options;

  options.linear_solver_type = DENSE_QR;

  RememberingCallback callback(&x);

  options.callbacks.push_back(&callback);

  Solver::Summary summary;

  int num_iterations;

  // First try: no updating.

  Solve(options, &problem, &summary);

  num_iterations = summary.num_successful_steps +

                   summary.num_unsuccessful_steps;

  EXPECT_GT(num_iterations, 1);

  for (int i = 0; i < callback.x_values.size(); ++i) {

    EXPECT_EQ(50.0, callback.x_values[i]);

  }

  // Second try: with updating

  x = 50.0;

  options.update_state_every_iteration = true;

  callback.x_values.clear();

  Solve(options, &problem, &summary);

  num_iterations = summary.num_successful_steps +

                   summary.num_unsuccessful_steps;

  EXPECT_GT(num_iterations, 1);

  EXPECT_EQ(original_x, callback.x_values[0]);

  EXPECT_NE(original_x, callback.x_values[1]);

}

// The parameters must be in separate blocks so that they can be individually

// set constant or not.

struct Quadratic4DCostFunction {

  template <typename T> bool operator()(const T* const x,

                                        const T* const y,

                                        const T* const z,

                                        const T* const w,

                                        T* residual) const {

    // A 4-dimension axis-aligned quadratic.

    residual[0] = T(10.0) - *x +

                  T(20.0) - *y +

                  T(30.0) - *z +

                  T(40.0) - *w;

    return true;

  }

  static CostFunction* Create() {

    return new AutoDiffCostFunction<Quadratic4DCostFunction, 1, 1, 1, 1, 1>(

        new Quadratic4DCostFunction);

  }

};

// A cost function that simply returns its argument.

class UnaryIdentityCostFunction : public SizedCostFunction<1, 1> {

 public:

  virtual bool Evaluate(double const* const* parameters,

                        double* residuals,

                        double** jacobians) const {

    residuals[0] = parameters[0][0];

    if (jacobians != NULL && jacobians[0] != NULL) {

      jacobians[0][0] = 1.0;

    }

    return true;

  }

};

TEST(Solver, TrustRegionProblemHasNoParameterBlocks) {

  Problem problem;

  Solver::Options options;

  options.minimizer_type = TRUST_REGION;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_EQ(summary.termination_type, CONVERGENCE);

  EXPECT_EQ(summary.message,

            "Function tolerance reached. "

            "No non-constant parameter blocks found.");

}

TEST(Solver, LineSearchProblemHasNoParameterBlocks) {

  Problem problem;

  Solver::Options options;

  options.minimizer_type = LINE_SEARCH;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_EQ(summary.termination_type, CONVERGENCE);

  EXPECT_EQ(summary.message,

            "Function tolerance reached. "

            "No non-constant parameter blocks found.");

}

TEST(Solver, TrustRegionProblemHasZeroResiduals) {

  Problem problem;

  double x = 1;

  problem.AddParameterBlock(&x, 1);

  Solver::Options options;

  options.minimizer_type = TRUST_REGION;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_EQ(summary.termination_type, CONVERGENCE);

  EXPECT_EQ(summary.message,

            "Function tolerance reached. "

            "No non-constant parameter blocks found.");

}

TEST(Solver, LineSearchProblemHasZeroResiduals) {

  Problem problem;

  double x = 1;

  problem.AddParameterBlock(&x, 1);

  Solver::Options options;

  options.minimizer_type = LINE_SEARCH;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_EQ(summary.termination_type, CONVERGENCE);

  EXPECT_EQ(summary.message,

            "Function tolerance reached. "

            "No non-constant parameter blocks found.");

}

TEST(Solver, TrustRegionProblemIsConstant) {

  Problem problem;

  double x = 1;

  problem.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x);

  problem.SetParameterBlockConstant(&x);

  Solver::Options options;

  options.minimizer_type = TRUST_REGION;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_EQ(summary.termination_type, CONVERGENCE);

  EXPECT_EQ(summary.initial_cost, 1.0 / 2.0);

  EXPECT_EQ(summary.final_cost, 1.0 / 2.0);

}

TEST(Solver, LineSearchProblemIsConstant) {

  Problem problem;

  double x = 1;

  problem.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x);

  problem.SetParameterBlockConstant(&x);

  Solver::Options options;

  options.minimizer_type = LINE_SEARCH;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_EQ(summary.termination_type, CONVERGENCE);

  EXPECT_EQ(summary.initial_cost, 1.0 / 2.0);

  EXPECT_EQ(summary.final_cost, 1.0 / 2.0);

}

#if defined(CERES_NO_SUITESPARSE)

TEST(Solver, SparseNormalCholeskyNoSuiteSparse) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = SUITE_SPARSE;

  options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, SparseSchurNoSuiteSparse) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = SUITE_SPARSE;

  options.linear_solver_type = SPARSE_SCHUR;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

#endif

#if defined(CERES_NO_CXSPARSE)

TEST(Solver, SparseNormalCholeskyNoCXSparse) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = CX_SPARSE;

  options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, SparseSchurNoCXSparse) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = CX_SPARSE;

  options.linear_solver_type = SPARSE_SCHUR;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

#endif

#if !defined(CERES_USE_EIGEN_SPARSE)

TEST(Solver, SparseNormalCholeskyNoEigenSparse) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;

  options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, SparseSchurNoEigenSparse) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;

  options.linear_solver_type = SPARSE_SCHUR;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

#endif

TEST(Solver, SparseNormalCholeskyNoSparseLibrary) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = NO_SPARSE;

  options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, SparseSchurNoSparseLibrary) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = NO_SPARSE;

  options.linear_solver_type = SPARSE_SCHUR;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, IterativeSchurWithClusterJacobiPerconditionerNoSparseLibrary) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = NO_SPARSE;

  options.linear_solver_type = ITERATIVE_SCHUR;

  // Requires SuiteSparse.

  options.preconditioner_type = CLUSTER_JACOBI;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, IterativeSchurWithClusterTridiagonalPerconditionerNoSparseLibrary) {

  Solver::Options options;

  options.sparse_linear_algebra_library_type = NO_SPARSE;

  options.linear_solver_type = ITERATIVE_SCHUR;

  // Requires SuiteSparse.

  options.preconditioner_type = CLUSTER_TRIDIAGONAL;

  string message;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, IterativeLinearSolverForDogleg) {

  Solver::Options options;

  options.trust_region_strategy_type = DOGLEG;

  string message;

  options.linear_solver_type = ITERATIVE_SCHUR;

  EXPECT_FALSE(options.IsValid(&message));

  options.linear_solver_type = CGNR;

  EXPECT_FALSE(options.IsValid(&message));

}

TEST(Solver, LinearSolverTypeNormalOperation) {

  Solver::Options options;

  options.linear_solver_type = DENSE_QR;

  string message;

  EXPECT_TRUE(options.IsValid(&message));

  options.linear_solver_type = DENSE_NORMAL_CHOLESKY;

  EXPECT_TRUE(options.IsValid(&message));

  options.linear_solver_type = DENSE_SCHUR;

  EXPECT_TRUE(options.IsValid(&message));

  options.linear_solver_type = SPARSE_SCHUR;

#if defined(CERES_NO_SUITESPARSE) &&            \

    defined(CERES_NO_CXSPARSE) &&               \

   !defined(CERES_USE_EIGEN_SPARSE)

  EXPECT_FALSE(options.IsValid(&message));

#else

  EXPECT_TRUE(options.IsValid(&message));

#endif

  options.linear_solver_type = ITERATIVE_SCHUR;

  EXPECT_TRUE(options.IsValid(&message));

}

template<int kNumResiduals, int N1 = 0, int N2 = 0, int N3 = 0>

class DummyCostFunction : public SizedCostFunction<kNumResiduals, N1, N2, N3> {

 public:

  bool Evaluate(double const* const* parameters,

                double* residuals,

                double** jacobians) const {

    for (int i = 0; i < kNumResiduals; ++i) {

      residuals[i] = kNumResiduals * kNumResiduals + i;

    }

    return true;

  }

};

TEST(Solver, FixedCostForConstantProblem) {

  double x = 1.0;

  Problem problem;

  problem.AddResidualBlock(new DummyCostFunction<2, 1>(), NULL, &x);

  problem.SetParameterBlockConstant(&x);

  const double expected_cost = 41.0 / 2.0;  // 1/2 * ((4 + 0)^2 + (4 + 1)^2)

  Solver::Options options;

  Solver::Summary summary;

  Solve(options, &problem, &summary);

  EXPECT_TRUE(summary.IsSolutionUsable());

  EXPECT_EQ(summary.fixed_cost, expected_cost);

  EXPECT_EQ(summary.initial_cost, expected_cost);

  EXPECT_EQ(summary.final_cost, expected_cost);

  EXPECT_EQ(summary.iterations.size(), 0);

}

}  // namespace internal

}  // namespace ceres