// 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/compressed_row_sparse_matrix.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"

#include "Eigen/Cholesky"

namespace ceres {

namespace internal {

// TODO(sameeragarwal): These tests needs to be re-written to be more

// thorough, they do not really test the dynamic nature of the

// sparsity.

class DynamicSparseNormalCholeskySolverTest : public ::testing::Test {

 protected:

  virtual void SetUp() {

    scoped_ptr<LinearLeastSquaresProblem> problem(

        CreateLinearLeastSquaresProblemFromId(1));

    A_.reset(CompressedRowSparseMatrix::FromTripletSparseMatrix(

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

    b_.reset(problem->b.release());

    D_.reset(problem->D.release());

  }

  void TestSolver(const LinearSolver::Options& options, double* D) {

    Matrix dense_A;

    A_->ToDenseMatrix(&dense_A);

    Matrix lhs = dense_A.transpose() * dense_A;

    if (D != NULL) {

      lhs += (ConstVectorRef(D, A_->num_cols()).array() *

              ConstVectorRef(D, A_->num_cols()).array())

                 .matrix()

                 .asDiagonal();

    }

    Vector rhs(A_->num_cols());

    rhs.setZero();

    A_->LeftMultiply(b_.get(), rhs.data());

    Vector expected_solution = lhs.llt().solve(rhs);

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

    LinearSolver::PerSolveOptions per_solve_options;

    per_solve_options.D = D;

    Vector actual_solution(A_->num_cols());

    LinearSolver::Summary summary;

    summary = solver->Solve(

        A_.get(), b_.get(), per_solve_options, actual_solution.data());

    EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);

    for (int i = 0; i < A_->num_cols(); ++i) {

      EXPECT_NEAR(expected_solution(i), actual_solution(i), 1e-8)

          << "\nExpected: " << expected_solution.transpose()

          << "\nActual: " << actual_solution.transpose();

    }

  }

  void TestSolver(

      const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type) {

    LinearSolver::Options options;

    options.type = SPARSE_NORMAL_CHOLESKY;

    options.dynamic_sparsity = true;

    options.sparse_linear_algebra_library_type =

        sparse_linear_algebra_library_type;

    TestSolver(options, NULL);

    TestSolver(options, D_.get());

  }

  scoped_ptr<CompressedRowSparseMatrix> A_;

  scoped_array<double> b_;

  scoped_array<double> D_;

};

#ifndef CERES_NO_SUITESPARSE

TEST_F(DynamicSparseNormalCholeskySolverTest, SuiteSparse) {

  TestSolver(SUITE_SPARSE);

}

#endif

#ifndef CERES_NO_CXSPARSE

TEST_F(DynamicSparseNormalCholeskySolverTest, CXSparse) {

  TestSolver(CX_SPARSE);

}

#endif

#ifdef CERES_USE_EIGEN_SPARSE

TEST_F(DynamicSparseNormalCholeskySolverTest, Eigen) {

  TestSolver(EIGEN_SPARSE);

}

#endif  // CERES_USE_EIGEN_SPARSE

}  // namespace internal

}  // namespace ceres