// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2017 Google Inc. All rights reserved.

// http://ceres-solver.org/

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

#ifndef CERES_INTERNAL_SPARSE_CHOLESKY_H_

#define CERES_INTERNAL_SPARSE_CHOLESKY_H_

// This include must come before any #ifndef check on Ceres compile options.

#include "ceres/internal/port.h"

#include "ceres/linear_solver.h"

#include "glog/logging.h"

namespace ceres {

namespace internal {

// An interface that abstracts away the internal details of various

// sparse linear algebra libraries and offers a simple API for solving

// symmetric positive definite linear systems using a sparse Cholesky

// factorization.

//

// Instances of SparseCholesky are expected to cache the symbolic

// factorization of the linear system. They do this on the first call

// to Factorize or FactorAndSolve. Subsequent calls to Factorize and

// FactorAndSolve are expected to have the same sparsity structure.

//

// Example usage:

//

//  scoped_ptr<SparseCholesky>

//  sparse_cholesky(SparseCholesky::Create(SUITE_SPARSE, AMD));

//

//  CompressedRowSparseMatrix lhs = ...;

//  std::string message;

//  CHECK_EQ(sparse_cholesky->Factorize(&lhs, &message), LINEAR_SOLVER_SUCCESS);

//  Vector rhs = ...;

//  Vector solution = ...;

//  CHECK_EQ(sparse_cholesky->Solve(rhs.data(), solution.data(), &message),

//           LINEAR_SOLVER_SUCCESS);

class SparseCholesky {

 public:

  // Factory which returns an instance of SparseCholesky for the given

  // sparse linear algebra library and fill reducing ordering

  // strategy.

  //

  // Caller owns the result.

  static SparseCholesky* Create(

      SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,

      OrderingType ordering_type);

  virtual ~SparseCholesky();

  // Due to the symmetry of the linear system, sparse linear algebra

  // libraries only use one half of the input matrix. Whether it is

  // the upper or the lower triangular part of the matrix depends on

  // the library and the re-ordering strategy being used. This

  // function tells the user the storage type expected of the input

  // matrix for the sparse linear algebra library and reordering

  // strategy used.

  virtual CompressedRowSparseMatrix::StorageType StorageType() const = 0;

  // Computes the numeric factorization of the given matrix.  If this

  // is the first call to Factorize, first the symbolic factorization

  // will be computed and cached and the numeric factorization will be

  // computed based on that.

  //

  // Subsequent calls to Factorize will use that symbolic

  // factorization assuming that the sparsity of the matrix has

  // remained constant.

  virtual LinearSolverTerminationType Factorize(

      CompressedRowSparseMatrix* lhs, std::string* message) = 0;

  // Computes the solution to the equation

  //

  // lhs * solution = rhs

  //

  // rhs and solution can point to the same memory location.

  virtual LinearSolverTerminationType Solve(const double* rhs,

                                            double* solution,

                                            std::string* message) = 0;

  // Convenience method which combines a call to Factorize and

  // Solve. Solve is only called if Factorize returns

  // LINEAR_SOLVER_SUCCESS.

  virtual LinearSolverTerminationType FactorAndSolve(

      CompressedRowSparseMatrix* lhs,

      const double* rhs,

      double* solution,

      std::string* message);

};

}  // namespace internal

}  // namespace ceres

#endif  // CERES_INTERNAL_SPARSE_CHOLESKY_H_