text: // Ceres Solver - A fast non-linear least squares minimizer
  // Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
  // http://code.google.com/p/ceres-solver/
  //
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  //   this list of conditions and the following disclaimer in the documentation
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  // * 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
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  //
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  // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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  // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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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"
  
  
  namespace ceres {
  namespace internal {
  
  class UnsymmetricLinearSolverTest : public ::testing::Test {
   protected :
    virtual void SetUp() {
      scoped_ptr<LinearLeastSquaresProblem> problem(
          CreateLinearLeastSquaresProblemFromId(0));
  
      CHECK_NOTNULL(problem.get());
      A_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
      b_.reset(problem->b.release());
      D_.reset(problem->D.release());
      sol_unregularized_.reset(problem->x.release());
      sol_regularized_.reset(problem->x_D.release());
    }
  
    void TestSolver(const LinearSolver::Options& options) {
      scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
  
      LinearSolver::PerSolveOptions per_solve_options;
      LinearSolver::Summary unregularized_solve_summary;
      LinearSolver::Summary regularized_solve_summary;
      Vector x_unregularized(A_->num_cols());
      Vector x_regularized(A_->num_cols());
  
      scoped_ptr<SparseMatrix> transformed_A;
  
      if (options.type == DENSE_QR ||
          options.type == DENSE_NORMAL_CHOLESKY) {
        transformed_A.reset(new DenseSparseMatrix(*A_));
      } else if (options.type == SPARSE_NORMAL_CHOLESKY) {
        CompressedRowSparseMatrix* crsm =  new CompressedRowSparseMatrix(*A_);
        // Add row/column blocks structure.
        for (int i = 0; i < A_->num_rows(); ++i) {
          crsm->mutable_row_blocks()->push_back(1);
        }
  
        for (int i = 0; i < A_->num_cols(); ++i) {
          crsm->mutable_col_blocks()->push_back(1);
        }
        transformed_A.reset(crsm);
      } else {
        LOG(FATAL) << "Unknown linear solver : " << options.type;
      }
      // Unregularized
      unregularized_solve_summary =
          solver->Solve(transformed_A.get(),
                        b_.get(),
                        per_solve_options,
                        x_unregularized.data());
  
      // Regularized solution
      per_solve_options.D = D_.get();
      regularized_solve_summary =
          solver->Solve(transformed_A.get(),
                        b_.get(),
                        per_solve_options,
                        x_regularized.data());
  
      EXPECT_EQ(unregularized_solve_summary.termination_type, TOLERANCE);
  
      for (int i = 0; i < A_->num_cols(); ++i) {
        EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
      }
  
      EXPECT_EQ(regularized_solve_summary.termination_type, TOLERANCE);
      for (int i = 0; i < A_->num_cols(); ++i) {
        EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8);
      }
    }
  
    scoped_ptr<TripletSparseMatrix> A_;
    scoped_array<double> b_;
    scoped_array<double> D_;
    scoped_array<double> sol_unregularized_;
    scoped_array<double> sol_regularized_;
  };
  
  TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
    LinearSolver::Options options;
    options.type = DENSE_QR;
    options.dense_linear_algebra_library_type = EIGEN;
    TestSolver(options);
  }
  
  TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
    LinearSolver::Options options;
    options.dense_linear_algebra_library_type = EIGEN;
    options.type = DENSE_NORMAL_CHOLESKY;
    TestSolver(options);
  }
  
  #ifndef CERES_NO_LAPACK
  TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
    LinearSolver::Options options;
    options.type = DENSE_QR;
    options.dense_linear_algebra_library_type = LAPACK;
    TestSolver(options);
  }
  
  TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
    LinearSolver::Options options;
    options.dense_linear_algebra_library_type = LAPACK;
    options.type = DENSE_NORMAL_CHOLESKY;
    TestSolver(options);
  }
  #endif
  
  #ifndef CERES_NO_SUITESPARSE
  TEST_F(UnsymmetricLinearSolverTest,
         SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
    LinearSolver::Options options;
    options.sparse_linear_algebra_library_type = SUITE_SPARSE;
    options.type = SPARSE_NORMAL_CHOLESKY;
    options.use_postordering = false;
    TestSolver(options);
  }
  
  TEST_F(UnsymmetricLinearSolverTest,
         SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
    LinearSolver::Options options;
    options.sparse_linear_algebra_library_type = SUITE_SPARSE;
    options.type = SPARSE_NORMAL_CHOLESKY;
    options.use_postordering = true;
    TestSolver(options);
  }
  #endif
  
  #ifndef CERES_NO_CXSPARSE
  TEST_F(UnsymmetricLinearSolverTest,
         SparseNormalCholeskyUsingCXSparsePreOrdering) {
    LinearSolver::Options options;
    options.sparse_linear_algebra_library_type = CX_SPARSE;
    options.type = SPARSE_NORMAL_CHOLESKY;
    options.use_postordering = false;
    TestSolver(options);
  }
  
  TEST_F(UnsymmetricLinearSolverTest,
         SparseNormalCholeskyUsingCXSparsePostOrdering) {
    LinearSolver::Options options;
    options.sparse_linear_algebra_library_type = CX_SPARSE;
    options.type = SPARSE_NORMAL_CHOLESKY;
    options.use_postordering = true;
    TestSolver(options);
  }
  #endif
  
  }  // namespace internal
  }  // namespace ceres
  