internal/ceres/cxsparse.cc

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 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.
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//   and/or other materials provided with the distribution.
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//
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// Author: strandmark@google.com (Petter Strandmark)

#ifndef CERES_NO_CXSPARSE

#include "ceres/cxsparse.h"

#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"

namespace ceres {
namespace internal {

CXSparse::CXSparse() : scratch_(NULL), scratch_size_(0) {
}

CXSparse::~CXSparse() {
  if (scratch_size_ > 0) {
    cs_free(scratch_);
  }
}

bool CXSparse::SolveCholesky(cs_di* A,
                             cs_dis* symbolic_factorization,
                             double* b) {
  // Make sure we have enough scratch space available.
  if (scratch_size_ < A->n) {
    if (scratch_size_ > 0) {
      cs_free(scratch_);
    }
    scratch_ = reinterpret_cast<CS_ENTRY*>(cs_malloc(A->n, sizeof(CS_ENTRY)));
  }

  // Solve using Cholesky factorization
  csn* numeric_factorization = cs_chol(A, symbolic_factorization);
  if (numeric_factorization == NULL) {
    LOG(WARNING) << "Cholesky factorization failed.";
    return false;
  }

  // When the Cholesky factorization succeeded, these methods are guaranteed to
  // succeeded as well. In the comments below, "x" refers to the scratch space.
  //
  // Set x = P * b.
  cs_ipvec(symbolic_factorization->pinv, b, scratch_, A->n);

  // Set x = L \ x.
  cs_lsolve(numeric_factorization->L, scratch_);

  // Set x = L' \ x.
  cs_ltsolve(numeric_factorization->L, scratch_);

  // Set b = P' * x.
  cs_pvec(symbolic_factorization->pinv, scratch_, b, A->n);

  // Free Cholesky factorization.
  cs_nfree(numeric_factorization);
  return true;
}

cs_dis* CXSparse::AnalyzeCholesky(cs_di* A) {
  // order = 1 for Cholesky factorization.
  return cs_schol(1, A);
}

cs_di CXSparse::CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A) {
  cs_di At;
  At.m = A->num_cols();
  At.n = A->num_rows();
  At.nz = -1;
  At.nzmax = A->num_nonzeros();
  At.p = A->mutable_rows();
  At.i = A->mutable_cols();
  At.x = A->mutable_values();
  return At;
}

cs_di* CXSparse::CreateSparseMatrix(TripletSparseMatrix* tsm) {
  cs_di_sparse tsm_wrapper;
  tsm_wrapper.nzmax = tsm->num_nonzeros();;
  tsm_wrapper.nz = tsm->num_nonzeros();;
  tsm_wrapper.m = tsm->num_rows();
  tsm_wrapper.n = tsm->num_cols();
  tsm_wrapper.p = tsm->mutable_cols();
  tsm_wrapper.i = tsm->mutable_rows();
  tsm_wrapper.x = tsm->mutable_values();

  return cs_compress(&tsm_wrapper);
}

void CXSparse::Free(cs_di* sparse_matrix) {
  cs_di_spfree(sparse_matrix);
}

void CXSparse::Free(cs_dis* symbolic_factorization) {
  cs_di_sfree(symbolic_factorization);
}

}  // namespace internal
}  // namespace ceres

#endif  // CERES_NO_CXSPARSE