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

// Copyright 2015 Google Inc. All rights reserved.

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

//

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// modification, are permitted provided that the following conditions are met:

//

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// Author: strandmark@google.com (Petter Strandmark)

#ifndef CERES_INTERNAL_CXSPARSE_H_

#define CERES_INTERNAL_CXSPARSE_H_

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

#include "ceres/internal/port.h"

#ifndef CERES_NO_CXSPARSE

#include <string>

#include <vector>

#include "ceres/linear_solver.h"

#include "ceres/sparse_cholesky.h"

#include "cs.h"

namespace ceres {

namespace internal {

class CompressedRowSparseMatrix;

class TripletSparseMatrix;

// This object provides access to solving linear systems using Cholesky

// factorization with a known symbolic factorization. This features does not

// explicity exist in CXSparse. The methods in the class are nonstatic because

// the class manages internal scratch space.

class CXSparse {

 public:

  CXSparse();

  ~CXSparse();

  // Solve the system lhs * solution = rhs in place by using an

  // approximate minimum degree fill reducing ordering.

  bool SolveCholesky(cs_di* lhs, double* rhs_and_solution);

  // Solves a linear system given its symbolic and numeric factorization.

  void Solve(cs_dis* symbolic_factor,

             csn* numeric_factor,

             double* rhs_and_solution);

  // Compute the numeric Cholesky factorization of A, given its

  // symbolic factorization.

  //

  // Caller owns the result.

  csn* Cholesky(cs_di* A, cs_dis* symbolic_factor);

  // Creates a sparse matrix from a compressed-column form. No memory is

  // allocated or copied; the structure A is filled out with info from the

  // argument.

  cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);

  // Creates a new matrix from a triplet form. Deallocate the returned matrix

  // with Free. May return NULL if the compression or allocation fails.

  cs_di* CreateSparseMatrix(TripletSparseMatrix* A);

  // B = A'

  //

  // The returned matrix should be deallocated with Free when not used

  // anymore.

  cs_di* TransposeMatrix(cs_di* A);

  // C = A * B

  //

  // The returned matrix should be deallocated with Free when not used

  // anymore.

  cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B);

  // Computes a symbolic factorization of A that can be used in SolveCholesky.

  //

  // The returned matrix should be deallocated with Free when not used anymore.

  cs_dis* AnalyzeCholesky(cs_di* A);

  // Computes a symbolic factorization of A that can be used in

  // SolveCholesky, but does not compute a fill-reducing ordering.

  //

  // The returned matrix should be deallocated with Free when not used anymore.

  cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A);

  // Computes a symbolic factorization of A that can be used in

  // SolveCholesky. The difference from AnalyzeCholesky is that this

  // function first detects the block sparsity of the matrix using

  // information about the row and column blocks and uses this block

  // sparse matrix to find a fill-reducing ordering. This ordering is

  // then used to find a symbolic factorization. This can result in a

  // significant performance improvement AnalyzeCholesky on block

  // sparse matrices.

  //

  // The returned matrix should be deallocated with Free when not used

  // anymore.

  cs_dis* BlockAnalyzeCholesky(cs_di* A,

                               const std::vector<int>& row_blocks,

                               const std::vector<int>& col_blocks);

  // Compute an fill-reducing approximate minimum degree ordering of

  // the matrix A. ordering should be non-NULL and should point to

  // enough memory to hold the ordering for the rows of A.

  void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering);

  void Free(cs_di* sparse_matrix);

  void Free(cs_dis* symbolic_factorization);

  void Free(csn* numeric_factorization);

 private:

  // Cached scratch space

  CS_ENTRY* scratch_;

  int scratch_size_;

};

// An implementation of SparseCholesky interface using the CXSparse

// library.

class CXSparseCholesky : public SparseCholesky {

 public:

  // Factory

  static CXSparseCholesky* Create(const OrderingType ordering_type);

  // SparseCholesky interface.

  virtual ~CXSparseCholesky();

  virtual CompressedRowSparseMatrix::StorageType StorageType() const;

  virtual LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,

                                                std::string* message);

  virtual LinearSolverTerminationType Solve(const double* rhs,

                                            double* solution,

                                            std::string* message);

 private:

  CXSparseCholesky(const OrderingType ordering_type);

  void FreeSymbolicFactorization();

  void FreeNumericFactorization();

  const OrderingType ordering_type_;

  CXSparse cs_;

  cs_dis* symbolic_factor_;

  csn* numeric_factor_;

};

}  // namespace internal

}  // namespace ceres

#else   // CERES_NO_CXSPARSE

typedef void cs_dis;

class CXSparse {

 public:

  void Free(void* arg) {}

};

#endif  // CERES_NO_CXSPARSE

#endif  // CERES_INTERNAL_CXSPARSE_H_