// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
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// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)

#include <string>

#include "ceres/dense_cholesky.h"
#include "ceres/internal/eigen.h"

#include "glog/logging.h"
#include "gtest/gtest.h"

namespace ceres {
namespace internal {

#ifndef CERES_NO_CUDA

TEST(CUDADenseCholesky, InvalidOptionOnCreate) {
  LinearSolver::Options options;
  ContextImpl context;
  options.context = &context;
  auto dense_cuda_solver = CUDADenseCholesky::Create(options);
  EXPECT_EQ(dense_cuda_solver, nullptr);
}

// Tests the CUDA Cholesky solver with a simple 4x4 matrix.
TEST(CUDADenseCholesky, Cholesky4x4Matrix) {
  Eigen::Matrix4d A;
  A <<  4,  12, -16, 0,
       12,  37, -43, 0,
      -16, -43,  98, 0,
        0,   0,   0, 1;
  const Eigen::Vector4d b = Eigen::Vector4d::Ones();
  LinearSolver::Options options;
  ContextImpl context;
  options.context = &context;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  std::string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS);
  Eigen::Vector4d x = Eigen::Vector4d::Zero();
  ASSERT_EQ(dense_cuda_solver->Solve(b.data(), x.data(), &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS);
  EXPECT_NEAR(x(0), 113.75 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(1), -31.0 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(2), 5.0 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(3), 1.0000, std::numeric_limits<double>::epsilon() * 10);
}

TEST(CUDADenseCholesky, SingularMatrix) {
  Eigen::Matrix3d A;
  A <<  1, 0, 0,
        0, 1, 0,
        0, 0, 0;
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  ContextImpl context;
  options.context = &context;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  std::string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FAILURE);
}

TEST(CUDADenseCholesky, NegativeMatrix) {
  Eigen::Matrix3d A;
  A <<  1, 0, 0,
        0, 1, 0,
        0, 0, -1;
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  ContextImpl context;
  options.context = &context;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  std::string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FAILURE);
}

TEST(CUDADenseCholesky, MustFactorizeBeforeSolve) {
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  ContextImpl context;
  options.context = &context;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  std::string error_string;
  ASSERT_EQ(dense_cuda_solver->Solve(b.data(), nullptr, &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR);
}

#endif  // CERES_NO_CUDA

}  // namespace internal
}  // namespace ceres