// 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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// modification, are permitted provided that the following conditions are met:
//
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//   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
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#ifndef CERES_NO_SUITESPARSE

#include "ceres/suitesparse.h"

#include "cholmod.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/triplet_sparse_matrix.h"
namespace ceres {
namespace internal {

cholmod_sparse* SuiteSparse::CreateSparseMatrix(TripletSparseMatrix* A) {
  cholmod_triplet triplet;

  triplet.nrow = A->num_rows();
  triplet.ncol = A->num_cols();
  triplet.nzmax = A->max_num_nonzeros();
  triplet.nnz = A->num_nonzeros();
  triplet.i = reinterpret_cast<void*>(A->mutable_rows());
  triplet.j = reinterpret_cast<void*>(A->mutable_cols());
  triplet.x = reinterpret_cast<void*>(A->mutable_values());
  triplet.stype = 0;  // Matrix is not symmetric.
  triplet.itype = CHOLMOD_INT;
  triplet.xtype = CHOLMOD_REAL;
  triplet.dtype = CHOLMOD_DOUBLE;

  return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
}


cholmod_sparse* SuiteSparse::CreateSparseMatrixTranspose(
    TripletSparseMatrix* A) {
  cholmod_triplet triplet;

  triplet.ncol = A->num_rows();  // swap row and columns
  triplet.nrow = A->num_cols();
  triplet.nzmax = A->max_num_nonzeros();
  triplet.nnz = A->num_nonzeros();

  // swap rows and columns
  triplet.j = reinterpret_cast<void*>(A->mutable_rows());
  triplet.i = reinterpret_cast<void*>(A->mutable_cols());
  triplet.x = reinterpret_cast<void*>(A->mutable_values());
  triplet.stype = 0;  // Matrix is not symmetric.
  triplet.itype = CHOLMOD_INT;
  triplet.xtype = CHOLMOD_REAL;
  triplet.dtype = CHOLMOD_DOUBLE;

  return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
}

cholmod_sparse* SuiteSparse::CreateSparseMatrixTransposeView(
    CompressedRowSparseMatrix* A) {
  cholmod_sparse* m = new cholmod_sparse_struct;
  m->nrow = A->num_cols();
  m->ncol = A->num_rows();
  m->nzmax = A->num_nonzeros();

  m->p = reinterpret_cast<void*>(A->mutable_rows());
  m->i = reinterpret_cast<void*>(A->mutable_cols());
  m->x = reinterpret_cast<void*>(A->mutable_values());

  m->stype = 0;  // Matrix is not symmetric.
  m->itype = CHOLMOD_INT;
  m->xtype = CHOLMOD_REAL;
  m->dtype = CHOLMOD_DOUBLE;
  m->sorted = 1;
  m->packed = 1;

  return m;
}

cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
                                              int in_size,
                                              int out_size) {
    CHECK_LE(in_size, out_size);
    cholmod_dense* v = cholmod_zeros(out_size, 1, CHOLMOD_REAL, &cc_);
    if (x != NULL) {
      memcpy(v->x, x, in_size*sizeof(*x));
    }
    return v;
}

cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A) {
  cholmod_factor* factor = cholmod_analyze(A, &cc_);
  CHECK_EQ(cc_.status, CHOLMOD_OK)
      << "Cholmod symbolic analysis failed " << cc_.status;
  CHECK_NOTNULL(factor);
  return factor;
}

bool SuiteSparse::Cholesky(cholmod_sparse* A, cholmod_factor* L) {
  CHECK_NOTNULL(A);
  CHECK_NOTNULL(L);

  cc_.quick_return_if_not_posdef = 1;
  int status = cholmod_factorize(A, L, &cc_);
  switch (cc_.status) {
    case CHOLMOD_NOT_INSTALLED:
      LOG(WARNING) << "Cholmod failure: method not installed.";
      return false;
    case CHOLMOD_OUT_OF_MEMORY:
      LOG(WARNING) << "Cholmod failure: out of memory.";
      return false;
    case CHOLMOD_TOO_LARGE:
      LOG(WARNING) << "Cholmod failure: integer overflow occured.";
      return false;
    case CHOLMOD_INVALID:
      LOG(WARNING) << "Cholmod failure: invalid input.";
      return false;
    case CHOLMOD_NOT_POSDEF:
      // TODO(sameeragarwal): These two warnings require more
      // sophisticated handling going forward. For now we will be
      // strict and treat them as failures.
      LOG(WARNING) << "Cholmod warning: matrix not positive definite.";
      return false;
    case CHOLMOD_DSMALL:
      LOG(WARNING) << "Cholmod warning: D for LDL' or diag(L) or "
                   << "LL' has tiny absolute value.";
      return false;
    case CHOLMOD_OK:
      if (status != 0) {
        return true;
      }
      LOG(WARNING) << "Cholmod failure: cholmod_factorize returned zero "
                   << "but cholmod_common::status is CHOLMOD_OK."
                   << "Please report this to ceres-solver@googlegroups.com.";
      return false;
    default:
      LOG(WARNING) << "Unknown cholmod return code. "
                   << "Please report this to ceres-solver@googlegroups.com.";
      return false;
  }
  return false;
}

cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
                                  cholmod_dense* b) {
  if (cc_.status != CHOLMOD_OK) {
    LOG(WARNING) << "CHOLMOD status NOT OK";
    return NULL;
  }

  return cholmod_solve(CHOLMOD_A, L, b, &cc_);
}

cholmod_dense* SuiteSparse::SolveCholesky(cholmod_sparse* A,
                                          cholmod_factor* L,
                                          cholmod_dense* b) {
  CHECK_NOTNULL(A);
  CHECK_NOTNULL(L);
  CHECK_NOTNULL(b);

  if (Cholesky(A, L)) {
    return Solve(L, b);
  }

  return NULL;
}

}  // namespace internal
}  // namespace ceres

#endif  // CERES_NO_SUITESPARSE