// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// SPDX-License-Identifier: MPL-2.0

// SparseLU solve does not accept column major matrices for the destination.
// However, as expected, the generic check_sparse_square_solving routines produces row-major
// rhs and destination matrices when compiled with EIGEN_DEFAULT_TO_ROW_MAJOR

#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
#undef EIGEN_DEFAULT_TO_ROW_MAJOR
#endif

#include "sparse_solver.h"
#include "fp_control.h"
#include <Eigen/SparseLU>

template <typename T>
void test_sparselu_T() {
  SparseLU<SparseMatrix<T, ColMajor> /*, COLAMDOrdering<int>*/> sparselu_colamd;  // COLAMDOrdering is the default
  SparseLU<SparseMatrix<T, ColMajor>, AMDOrdering<int> > sparselu_amd;
  SparseLU<SparseMatrix<T, ColMajor, long int>, NaturalOrdering<long int> > sparselu_natural;

  check_sparse_square_solving(sparselu_colamd, 300, 100000, true);
  check_sparse_square_solving(sparselu_amd, 300, 10000, true);
  check_sparse_square_solving(sparselu_natural, 300, 2000, true);

  check_sparse_square_abs_determinant(sparselu_colamd);
  check_sparse_square_abs_determinant(sparselu_amd);

  check_sparse_square_determinant(sparselu_colamd);
  check_sparse_square_determinant(sparselu_amd);
}

template <typename T>
void test_sparselu_rowmajor_compressed_input() {
  typedef SparseMatrix<T, RowMajor> RowMajorSparseMatrix;
  typedef Matrix<T, Dynamic, 1> Vector;

  Vector b(2);
  b << T(1.1), T(3.14);

  Vector expected(2);
  expected << T(1.1 - 0.0001 * 3.14), T(3.14);

  RowMajorSparseMatrix compressed(2, 2);
  compressed.insert(0, 0) = T(1.0);
  compressed.insert(0, 1) = T(0.0001);
  compressed.insert(1, 1) = T(1.0);
  compressed.makeCompressed();

  RowMajorSparseMatrix uncompressed(2, 2);
  uncompressed.insert(0, 0) = T(1.0);
  uncompressed.insert(0, 1) = T(0.0001);
  uncompressed.insert(1, 1) = T(1.0);

  SparseLU<RowMajorSparseMatrix> compressed_solver;
  compressed_solver.compute(compressed);
  VERIFY_IS_EQUAL(compressed_solver.info(), Success);
  VERIFY_IS_APPROX(compressed_solver.solve(b), expected);

  SparseLU<RowMajorSparseMatrix> two_step_solver;
  two_step_solver.analyzePattern(compressed);
  two_step_solver.factorize(compressed);
  VERIFY_IS_EQUAL(two_step_solver.info(), Success);
  VERIFY_IS_APPROX(two_step_solver.solve(b), expected);

  SparseLU<RowMajorSparseMatrix> uncompressed_solver;
  uncompressed_solver.compute(uncompressed);
  VERIFY_IS_EQUAL(uncompressed_solver.info(), Success);
  VERIFY_IS_APPROX(uncompressed_solver.solve(b), expected);
}

template <typename T>
void test_sparselu_colmajor_uncompressed_input() {
  typedef SparseMatrix<T, ColMajor> ColMajorSparseMatrix;
  typedef Matrix<T, Dynamic, 1> Vector;

  Vector b(2);
  b << T(1.1), T(3.14);

  Vector expected(2);
  expected << T(1.1 - 0.0001 * 3.14), T(3.14);

  ColMajorSparseMatrix uncompressed(2, 2);
  uncompressed.insert(0, 0) = T(1.0);
  uncompressed.insert(0, 1) = T(0.0001);
  uncompressed.insert(1, 1) = T(1.0);

  SparseLU<ColMajorSparseMatrix> uncompressed_solver;
  uncompressed_solver.compute(uncompressed);
  VERIFY_IS_EQUAL(uncompressed_solver.info(), Success);
  VERIFY_IS_APPROX(uncompressed_solver.solve(b), expected);
}

// Regression for issue #1908: lastErrorMessage() must not carry over from a
// previous failed factorize() once a subsequent factorize() succeeds.
template <typename T>
void test_sparselu_clear_error_state() {
  typedef SparseMatrix<T, ColMajor> ColMajorSparseMatrix;

  ColMajorSparseMatrix singular(3, 3);  // structurally singular: no nonzeros

  ColMajorSparseMatrix non_singular(3, 3);
  non_singular.insert(0, 0) = T(1);
  non_singular.insert(1, 1) = T(1);
  non_singular.insert(2, 2) = T(1);
  non_singular.makeCompressed();

  SparseLU<ColMajorSparseMatrix> solver;
  solver.compute(singular);
  VERIFY(solver.info() != Success);
  VERIFY(!solver.lastErrorMessage().empty());

  solver.compute(non_singular);
  VERIFY_IS_EQUAL(solver.info(), Success);
  VERIFY(solver.lastErrorMessage().empty());
}

// A = M diag(1, .., s, .., 1), M = n I + ones, with s and the pivot of the scaled column subnormal: A is well
// conditioned up to that column scaling, but 1 / pivot overflows, which used to fill the column of L with Inf
// while info() reported Success. det A = +-2 n^n s, and the solve is judged by its componentwise backward error
// max_i |b - A x|_i / (|A| |x| + |b|)_i, which a column scaling leaves unchanged.
template <typename T, typename Ordering>
void test_sparselu_subnormal_pivot(Index n, Index scaledColumn, const T& phase) {
  using Real = typename NumTraits<T>::Real;
  using DenseMatrix = Matrix<T, Dynamic, Dynamic>;
  using DenseVector = Matrix<T, Dynamic, 1>;
  using RealVector = Matrix<Real, Dynamic, 1>;

  if (!subnormalDivisionIsExact<Real>() || underflowProbe<Real>() == Real(0)) {
    std::cout << "SKIP: test_sparselu_subnormal_pivot needs gradual underflow and IEEE 754 division by subnormals."
              << std::endl;
    return;
  }

  // 1 / pivot overflows for |pivot| < min / 4, and the pivot is about (n + 1) s. A subnormal near s carries
  // only log2(s / denorm_min) bits, hence the unit roundoff u.
  const Real s = (std::numeric_limits<Real>::min)() / Real(32);
  const Real u = numext::maxi(NumTraits<Real>::epsilon(), (std::numeric_limits<Real>::denorm_min)() / s);
  const Real tolerance = Real(16 * n) * u;
  Real detM = Real(2);
  for (Index i = 0; i < n; ++i) detM *= Real(n);

  for (int swap = 0; swap < 2; ++swap) {
    DenseMatrix dense = DenseMatrix::Ones(n, n);
    dense.diagonal().array() += T(Real(n));
    dense.col(scaledColumn) *= T(s) * phase;
    if (swap) dense.row(0).swap(dense.row(1));
    // Inserted explicitly: sparseView() prunes on a squared magnitude, which underflows here.
    SparseMatrix<T> sparse(n, n);
    sparse.reserve(VectorXi::Constant(n, int(n)));
    for (Index j = 0; j < n; ++j)
      for (Index i = 0; i < n; ++i) sparse.insert(i, j) = dense(i, j);
    sparse.makeCompressed();

    SparseLU<SparseMatrix<T>, Ordering> lu(sparse);
    VERIFY_IS_EQUAL(lu.info(), Success);

    const T expectedDet = T(swap ? -detM : detM) * T(s) * phase;
    VERIFY(numext::abs(lu.determinant() - expectedDet) <= tolerance * numext::abs(expectedDet));

    // x = (.., 2^-10 / s, ..) keeps both A x and the solution representable.
    DenseVector x = DenseVector::Ones(n);
    x(scaledColumn) = T(Real(1) / (Real(1024) * s));
    const DenseVector b = dense * x;
    x = lu.solve(b);
    VERIFY(x.allFinite());
    const RealVector residual = (b - dense * x).cwiseAbs();
    const RealVector scale = dense.cwiseAbs() * x.cwiseAbs() + b.cwiseAbs();
    VERIFY((residual.array() <= tolerance * scale.array()).all());
  }
}

EIGEN_DECLARE_TEST(sparselu) {
  CALL_SUBTEST_1(test_sparselu_T<float>());
  CALL_SUBTEST_2(test_sparselu_T<double>());
  CALL_SUBTEST_3(test_sparselu_T<std::complex<float> >());
  CALL_SUBTEST_4(test_sparselu_T<std::complex<double> >());
  CALL_SUBTEST_5(test_sparselu_rowmajor_compressed_input<float>());
  CALL_SUBTEST_6(test_sparselu_rowmajor_compressed_input<double>());
  CALL_SUBTEST_7(test_sparselu_colmajor_uncompressed_input<float>());
  CALL_SUBTEST_8(test_sparselu_colmajor_uncompressed_input<double>());
  CALL_SUBTEST_9(test_sparselu_clear_error_state<float>());
  CALL_SUBTEST_10(test_sparselu_clear_error_state<double>());
  // The scaled column leads a supernode (0) or lies inside one (1); COLAMD is free to move it.
  for (Index scaledColumn = 0; scaledColumn < 2; ++scaledColumn) {
    CALL_SUBTEST_9((test_sparselu_subnormal_pivot<float, NaturalOrdering<int> >(4, scaledColumn, 1.0f)));
    CALL_SUBTEST_9((test_sparselu_subnormal_pivot<float, COLAMDOrdering<int> >(2, scaledColumn, 1.0f)));
    CALL_SUBTEST_10((test_sparselu_subnormal_pivot<double, NaturalOrdering<int> >(4, scaledColumn, 1.0)));
    CALL_SUBTEST_10((test_sparselu_subnormal_pivot<double, COLAMDOrdering<int> >(2, scaledColumn, 1.0)));
    CALL_SUBTEST_10((test_sparselu_subnormal_pivot<std::complex<double>, NaturalOrdering<int> >(
        4, scaledColumn, std::complex<double>(0, 1))));
  }
}