// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2017 Google Inc. All rights reserved.

// http://ceres-solver.org/

//

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

#include "ceres/casts.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/linear_least_squares_problems.h"

#include "ceres/linear_solver.h"

#include "ceres/triplet_sparse_matrix.h"

#include "ceres/types.h"

#include "glog/logging.h"

#include "gtest/gtest.h"

namespace ceres {

namespace internal {

typedef ::testing::

    tuple<LinearSolverType, DenseLinearAlgebraLibraryType, bool, int>

        Param;

std::string ParamInfoToString(testing::TestParamInfo<Param> info) {

  Param param = info.param;

  std::stringstream ss;

  ss << LinearSolverTypeToString(::testing::get<0>(param)) << "_"

     << DenseLinearAlgebraLibraryTypeToString(::testing::get<1>(param)) << "_"

     << (::testing::get<2>(param) ? "Regularized" : "Unregularized") << "_"

     << ::testing::get<3>(param);

  return ss.str();

}

class DenseLinearSolverTest : public ::testing::TestWithParam<Param> {};

TEST_P(DenseLinearSolverTest, _) {

  Param param = GetParam();

  const bool regularized = testing::get<2>(param);

  scoped_ptr<LinearLeastSquaresProblem> problem(

      CreateLinearLeastSquaresProblemFromId(testing::get<3>(param)));

  DenseSparseMatrix lhs(*down_cast<TripletSparseMatrix*>(problem->A.get()));

  const int num_cols = lhs.num_cols();

  const int num_rows = lhs.num_rows();

  Vector rhs = Vector::Zero(num_rows + num_cols);

  rhs.head(num_rows) = ConstVectorRef(problem->b.get(), num_rows);

  LinearSolver::Options options;

  options.type = ::testing::get<0>(param);

  options.dense_linear_algebra_library_type = ::testing::get<1>(param);

  scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));

  LinearSolver::PerSolveOptions per_solve_options;

  if (regularized) {

    per_solve_options.D = problem->D.get();

  }

  Vector solution(num_cols);

  LinearSolver::Summary summary =

      solver->Solve(&lhs, rhs.data(), per_solve_options, solution.data());

  EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);

  // If solving for the regularized solution, add the diagonal to the

  // matrix. This makes subsequent computations simpler.

  if (testing::get<2>(param)) {

    lhs.AppendDiagonal(problem->D.get());

  };

  Vector tmp = Vector::Zero(num_rows + num_cols);

  lhs.RightMultiply(solution.data(), tmp.data());

  Vector actual_normal_rhs = Vector::Zero(num_cols);

  lhs.LeftMultiply(tmp.data(), actual_normal_rhs.data());

  Vector expected_normal_rhs = Vector::Zero(num_cols);

  lhs.LeftMultiply(rhs.data(), expected_normal_rhs.data());

  const double residual = (expected_normal_rhs - actual_normal_rhs).norm() /

                          expected_normal_rhs.norm();

  EXPECT_NEAR(residual, 0.0, 10 * std::numeric_limits<double>::epsilon());

}

// TODO(sameeragarwal): Should we move away from hard coded linear

// least squares problem to randomly generated ones?

#ifndef CERES_NO_LAPACK

INSTANTIATE_TEST_CASE_P(

    DenseLinearSolver,

    DenseLinearSolverTest,

    ::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY),

                       ::testing::Values(EIGEN, LAPACK),

                       ::testing::Values(true, false),

                       ::testing::Values(0, 1)),

    ParamInfoToString);

#else

INSTANTIATE_TEST_CASE_P(

    DenseLinearSolver,

    DenseLinearSolverTest,

    ::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY),

                       ::testing::Values(EIGEN),

                       ::testing::Values(true, false),

                       ::testing::Values(0, 1)),

    ParamInfoToString);

#endif

}  // namespace internal

}  // namespace ceres