xref: /external/ceres-solver/internal/ceres/iterative_schur_complement_solver_test.cc

// 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.
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// TODO(sameeragarwal): Add support for larger, more complicated and
// poorly conditioned problems both for correctness testing as well as
// benchmarking.

#include "ceres/iterative_schur_complement_solver.h"

#include <cstddef>
#include "Eigen/Dense"
#include "ceres/block_random_access_dense_matrix.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/casts.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/linear_solver.h"
#include "ceres/schur_eliminator.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"
#include "gtest/gtest.h"

namespace ceres {
namespace internal {

using testing::AssertionResult;

const double kEpsilon = 1e-14;

class IterativeSchurComplementSolverTest : public ::testing::Test {
 protected :
  void SetUpProblem(int problem_id) {
    scoped_ptr<LinearLeastSquaresProblem> problem(
        CreateLinearLeastSquaresProblemFromId(problem_id));

    CHECK_NOTNULL(problem.get());
    A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
    b_.reset(problem->b.release());
    D_.reset(problem->D.release());

    num_cols_ = A_->num_cols();
    num_rows_ = A_->num_rows();
    num_eliminate_blocks_ = problem->num_eliminate_blocks;
  }

  AssertionResult TestSolver(double* D) {
    TripletSparseMatrix triplet_A(A_->num_rows(),
                                  A_->num_cols(),
                                  A_->num_nonzeros());
    A_->ToTripletSparseMatrix(&triplet_A);

    DenseSparseMatrix dense_A(triplet_A);

    LinearSolver::Options options;
    options.type = DENSE_QR;
    scoped_ptr<LinearSolver> qr(LinearSolver::Create(options));

    LinearSolver::PerSolveOptions per_solve_options;
    per_solve_options.D = D;
    Vector reference_solution(num_cols_);
    qr->Solve(&dense_A, b_.get(), per_solve_options, reference_solution.data());

    options.elimination_groups.push_back(num_eliminate_blocks_);
    options.elimination_groups.push_back(0);
    options.max_num_iterations = num_cols_;
    options.preconditioner_type = SCHUR_JACOBI;
    IterativeSchurComplementSolver isc(options);

    Vector isc_sol(num_cols_);
    per_solve_options.r_tolerance  = 1e-12;
    isc.Solve(A_.get(), b_.get(), per_solve_options, isc_sol.data());
    double diff = (isc_sol - reference_solution).norm();
    if (diff < kEpsilon) {
      return testing::AssertionSuccess();
    } else {
      return testing::AssertionFailure()
          << "The reference solution differs from the ITERATIVE_SCHUR"
          << " solution by " << diff << " which is more than " << kEpsilon;
    }
  }

  int num_rows_;
  int num_cols_;
  int num_eliminate_blocks_;
  scoped_ptr<BlockSparseMatrix> A_;
  scoped_array<double> b_;
  scoped_array<double> D_;
};

TEST_F(IterativeSchurComplementSolverTest, NormalProblem) {
  SetUpProblem(2);
  EXPECT_TRUE(TestSolver(NULL));
  EXPECT_TRUE(TestSolver(D_.get()));
}

TEST_F(IterativeSchurComplementSolverTest, ProblemWithNoFBlocks) {
  SetUpProblem(3);
  EXPECT_TRUE(TestSolver(NULL));
  EXPECT_TRUE(TestSolver(D_.get()));
}

}  // namespace internal
}  // namespace ceres