// 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/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
//   this list of conditions and the following disclaimer in the documentation
//   and/or other materials provided with the distribution.
// * 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
//   specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#ifndef CERES_NO_SUITESPARSE

#include "ceres/sparse_normal_cholesky_solver.h"

#include <algorithm>
#include <cstring>
#include <ctime>
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/linear_solver.h"
#include "ceres/suitesparse.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/types.h"

namespace ceres {
namespace internal {

SparseNormalCholeskySolver::SparseNormalCholeskySolver(
    const LinearSolver::Options& options)
    : options_(options), symbolic_factor_(NULL) {}

SparseNormalCholeskySolver::~SparseNormalCholeskySolver() {
  if (symbolic_factor_ != NULL) {
    ss_.Free(symbolic_factor_);
    symbolic_factor_ = NULL;
  }
}

LinearSolver::Summary SparseNormalCholeskySolver::SolveImpl(
    CompressedRowSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double * x) {
  const time_t start_time = time(NULL);
  const int num_cols = A->num_cols();

  LinearSolver::Summary summary;
  Vector Atb = Vector::Zero(num_cols);
  A->LeftMultiply(b, Atb.data());

  if (per_solve_options.D != NULL) {
    // Temporarily append a diagonal block to the A matrix, but undo it before
    // returning the matrix to the user.
    CompressedRowSparseMatrix D(per_solve_options.D, num_cols);
    A->AppendRows(D);
  }

  VectorRef(x, num_cols).setZero();

  scoped_ptr<cholmod_sparse> lhs(ss_.CreateSparseMatrixTransposeView(A));
  CHECK_NOTNULL(lhs.get());

  cholmod_dense* rhs = ss_.CreateDenseVector(Atb.data(), num_cols, num_cols);
  const time_t init_time = time(NULL);

  if (symbolic_factor_ == NULL) {
    symbolic_factor_ = CHECK_NOTNULL(ss_.AnalyzeCholesky(lhs.get()));
  }

  const time_t symbolic_time = time(NULL);

  cholmod_dense* sol = ss_.SolveCholesky(lhs.get(), symbolic_factor_, rhs);
  const time_t solve_time = time(NULL);

  ss_.Free(rhs);
  rhs = NULL;

  if (per_solve_options.D != NULL) {
    A->DeleteRows(num_cols);
  }

  if (!options_.constant_sparsity) {
    ss_.Free(symbolic_factor_);
    symbolic_factor_ = NULL;
  }

  summary.num_iterations = 1;
  if (sol != NULL) {
    memcpy(x, sol->x, num_cols * sizeof(*x));

    ss_.Free(sol);
    sol = NULL;
    summary.termination_type = TOLERANCE;
  }

  const time_t cleanup_time = time(NULL);
  VLOG(2) << "time (sec) total: " << cleanup_time - start_time
          << " init: " << init_time - start_time
          << " symbolic: " << symbolic_time - init_time
          << " solve: " << solve_time - symbolic_time
          << " cleanup: " << cleanup_time - solve_time;
  return summary;
}

}   // namespace internal
}   // namespace ceres

#endif  // CERES_NO_SUITESPARSE