internal/ceres/sparse_normal_cholesky_solver.cc

// 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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// Author: sameeragarwal@google.com (Sameer Agarwal)

#if !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)

#include "ceres/sparse_normal_cholesky_solver.h"

#include <algorithm>
#include <cstring>
#include <ctime>

#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/cxsparse.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_solver.h"
#include "ceres/suitesparse.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "ceres/wall_time.h"

namespace ceres {
namespace internal {

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

SparseNormalCholeskySolver::~SparseNormalCholeskySolver() {
#ifndef CERES_NO_SUITESPARSE
  if (factor_ != NULL) {
    ss_.Free(factor_);
    factor_ = NULL;
  }
#endif

#ifndef CERES_NO_CXSPARSE
  if (cxsparse_factor_ != NULL) {
    cxsparse_.Free(cxsparse_factor_);
    cxsparse_factor_ = NULL;
  }
#endif  // CERES_NO_CXSPARSE
}

LinearSolver::Summary SparseNormalCholeskySolver::SolveImpl(
    CompressedRowSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double * x) {
  switch (options_.sparse_linear_algebra_library_type) {
    case SUITE_SPARSE:
      return SolveImplUsingSuiteSparse(A, b, per_solve_options, x);
    case CX_SPARSE:
      return SolveImplUsingCXSparse(A, b, per_solve_options, x);
    default:
      LOG(FATAL) << "Unknown sparse linear algebra library : "
                 << options_.sparse_linear_algebra_library_type;
  }

  LOG(FATAL) << "Unknown sparse linear algebra library : "
             << options_.sparse_linear_algebra_library_type;
  return LinearSolver::Summary();
}

#ifndef CERES_NO_CXSPARSE
LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
    CompressedRowSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double * x) {
  EventLogger event_logger("SparseNormalCholeskySolver::CXSparse::Solve");

  LinearSolver::Summary summary;
  summary.num_iterations = 1;
  summary.termination_type = TOLERANCE;
  summary.message = "Success.";

  const int num_cols = A->num_cols();
  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();

  // Wrap the augmented Jacobian in a compressed sparse column matrix.
  cs_di At = cxsparse_.CreateSparseMatrixTransposeView(A);

  // Compute the normal equations. J'J delta = J'f and solve them
  // using a sparse Cholesky factorization. Notice that when compared
  // to SuiteSparse we have to explicitly compute the transpose of Jt,
  // and then the normal equations before they can be
  // factorized. CHOLMOD/SuiteSparse on the other hand can just work
  // off of Jt to compute the Cholesky factorization of the normal
  // equations.
  cs_di* A2 = cxsparse_.TransposeMatrix(&At);
  cs_di* AtA = cxsparse_.MatrixMatrixMultiply(&At, A2);

  cxsparse_.Free(A2);
  if (per_solve_options.D != NULL) {
    A->DeleteRows(num_cols);
  }
  event_logger.AddEvent("Setup");

  // Compute symbolic factorization if not available.
  if (cxsparse_factor_ == NULL) {
    if (options_.use_postordering) {
      cxsparse_factor_ = cxsparse_.BlockAnalyzeCholesky(AtA,
                                                        A->col_blocks(),
                                                        A->col_blocks());
    } else {
      cxsparse_factor_ = cxsparse_.AnalyzeCholeskyWithNaturalOrdering(AtA);
    }
  }
  event_logger.AddEvent("Analysis");

  if (cxsparse_factor_ == NULL) {
    summary.termination_type = FATAL_ERROR;
    summary.message =
        "CXSparse failure. Unable to find symbolic factorization.";
  } else if (cxsparse_.SolveCholesky(AtA, cxsparse_factor_, Atb.data())) {
    VectorRef(x, Atb.rows()) = Atb;
  } else {
    summary.termination_type = FAILURE;
  }
  event_logger.AddEvent("Solve");

  cxsparse_.Free(AtA);
  event_logger.AddEvent("Teardown");
  return summary;
}
#else
LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
    CompressedRowSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double * x) {
  LOG(FATAL) << "No CXSparse support in Ceres.";

  // Unreachable but MSVC does not know this.
  return LinearSolver::Summary();
}
#endif

#ifndef CERES_NO_SUITESPARSE
LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
    CompressedRowSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double * x) {
  EventLogger event_logger("SparseNormalCholeskySolver::SuiteSparse::Solve");
  LinearSolver::Summary summary;
  summary.termination_type = TOLERANCE;
  summary.num_iterations = 1;
  summary.message = "Success.";

  const int num_cols = A->num_cols();
  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();
  cholmod_sparse lhs = ss_.CreateSparseMatrixTransposeView(A);
  event_logger.AddEvent("Setup");

  if (factor_ == NULL) {
    if (options_.use_postordering) {
      factor_ = ss_.BlockAnalyzeCholesky(&lhs,
                                         A->col_blocks(),
                                         A->row_blocks(),
                                         &summary.message);
    } else {
      factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&lhs, &summary.message);
    }
  }
  event_logger.AddEvent("Analysis");

  if (factor_ == NULL) {
    if (per_solve_options.D != NULL) {
      A->DeleteRows(num_cols);
    }
    summary.termination_type = FATAL_ERROR;
    return summary;
  }

  summary.termination_type = ss_.Cholesky(&lhs, factor_, &summary.message);
  if (summary.termination_type != TOLERANCE) {
    if (per_solve_options.D != NULL) {
      A->DeleteRows(num_cols);
    }
    return summary;
  }

  cholmod_dense* rhs = ss_.CreateDenseVector(Atb.data(), num_cols, num_cols);
  cholmod_dense* sol = ss_.Solve(factor_, rhs, &summary.message);
  event_logger.AddEvent("Solve");

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

  if (sol != NULL) {
    memcpy(x, sol->x, num_cols * sizeof(*x));
    ss_.Free(sol);
  } else {
    summary.termination_type = FAILURE;
  }

  event_logger.AddEvent("Teardown");
  return summary;
}
#else
LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
    CompressedRowSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double * x) {
  LOG(FATAL) << "No SuiteSparse support in Ceres.";

  // Unreachable but MSVC does not know this.
  return LinearSolver::Summary();
}
#endif

}   // namespace internal
}   // namespace ceres

#endif  // !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)