CSparseMatrix.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://www.mrpt.org/License          |
   +------------------------------------------------------------------------+ */
#pragma once

#include <mrpt/config.h>  //MRPT_HAS_SUITESPARSE
#include <mrpt/math/CMatrixDynamic.h>
#include <mrpt/math/CMatrixFixed.h>
#include <mrpt/math/CSparseMatrixTemplate.h>
#include <mrpt/math/CVectorDynamic.h>
#include <mrpt/math/math_frwds.h>
#include <cstring>  // memcpy
#include <stdexcept>

// Include CSparse lib headers, either from the system or embedded:
extern "C"
{
#if MRPT_HAS_SUITESPARSE
#define NCOMPLEX  // In MRPT we don't need complex numbers, so avoid the
// annoying warning: 'cs_ci_house' has C-linkage specified,
// but returns UDT 'std::complex<double>' which is
// incompatible with C
#include "cs.h"
#else
#include <mrpt/otherlibs/CSparse/cs.h>
#endif
}

namespace mrpt::math
{
/** Used in mrpt::math::CSparseMatrix */
struct CExceptionNotDefPos : public std::runtime_error
{
    CExceptionNotDefPos(const char* s) : std::runtime_error(s) {}
};

/** A sparse matrix structure, wrapping T. Davis' CSparse library (part of
 *suitesparse)
 *  The type of the matrix entries is fixed to "double".
 *
 *  There are two formats for the non-zero entries in this matrix:
 *      - A "triplet" matrix: a set of (r,c)=val triplet entries.
 *      - A column-compressed sparse (CCS) matrix.
 *
 *  The latter is the "normal" format, which is expected by all mathematical
 *operations defined
 *   in this class. There're three ways of initializing and populating a sparse
 *matrix:
 *
 *   <ol>
 *    <li> <b>As a triplet (empty), then add entries, then compress:</b>
 *         \code
 *             CSparseMatrix  SM(100,100);
 *             SM.insert_entry(i,j, val);  // or
 *             SM.insert_submatrix(i,j, MAT); //  ...
 *             SM.compressFromTriplet();
 *         \endcode
 *    </li>
 *    <li> <b>As a triplet from a CSparseMatrixTemplate<double>:</b>
 *         \code
 *             CSparseMatrixTemplate<double>  data;
 *             data(row,col) = val;
 *             ...
 *             CSparseMatrix  SM(data);
 *         \endcode
 *    </li>
 *    <li> <b>From an existing dense matrix:</b>
 *         \code
 *             CMatrixDouble data(100,100); // or
 *             CMatrixFloat  data(100,100); // or
 *             CMatrixFixed<double,4,6>  data; // etc...
 *             CSparseMatrix  SM(data);
 *         \endcode
 *    </li>
 *
 *   </ol>
 *
 *  Due to its practical utility, there is a special inner class
 *CSparseMatrix::CholeskyDecomp to handle Cholesky-related methods and data.
 *
 * \note This class was initially adapted from "robotvision", by Hauke
 *Strasdat, Steven Lovegrove and Andrew J. Davison. See
 *http://www.openslam.org/robotvision.html
 * \note CSparse is maintained by Timothy Davis:
 *http://people.sc.fsu.edu/~jburkardt/c_src/csparse/csparse.html .
 * \note See also his book "Direct methods for sparse linear systems".
 *http://books.google.es/books?id=TvwiyF8vy3EC&pg=PA12&lpg=PA12&dq=cs_compress&source=bl&ots=od9uGJ793j&sig=Wa-fBk4sZkZv3Y0Op8FNH8PvCUs&hl=es&ei=UjA0TJf-EoSmsQay3aXPAw&sa=X&oi=book_result&ct=result&resnum=8&ved=0CEQQ6AEwBw#v=onepage&q&f=false
 *
 * \sa mrpt::math::MatrixBlockSparseCols, mrpt::math::CMatrixFixed,
 *mrpt::math::CMatrixDynamic, etc.
 * \ingroup mrpt_math_grp
 */
class CSparseMatrix
{
   private:
    cs sparse_matrix;

    /** Initialization from a dense matrix of any kind existing in MRPT. */
    template <class MATRIX>
    void construct_from_mrpt_mat(const MATRIX& C)
    {
        std::vector<int> row_list, col_list;  // Use "int" for convenience so we
        // can do a memcpy below...
        std::vector<double> content_list;
        const int nCol = C.cols();
        const int nRow = C.rows();
        for (int c = 0; c < nCol; ++c)
        {
            col_list.push_back(row_list.size());
            for (int r = 0; r < nRow; ++r)
                if (C(r, c) != 0)
                {
                    row_list.push_back(r);
                    content_list.push_back(C(r, c));
                }
        }
        col_list.push_back(row_list.size());

        sparse_matrix.m = nRow;
        sparse_matrix.n = nCol;
        sparse_matrix.nzmax = content_list.size();
        sparse_matrix.i = (int*)malloc(sizeof(int) * row_list.size());
        sparse_matrix.p = (int*)malloc(sizeof(int) * col_list.size());
        sparse_matrix.x = (double*)malloc(sizeof(double) * content_list.size());

        ::memcpy(
            sparse_matrix.i, &row_list[0],
            sizeof(row_list[0]) * row_list.size());
        ::memcpy(
            sparse_matrix.p, &col_list[0],
            sizeof(col_list[0]) * col_list.size());
        ::memcpy(
            sparse_matrix.x, &content_list[0],
            sizeof(content_list[0]) * content_list.size());

        sparse_matrix.nz =
            -1;  // <0 means "column compressed", ">=0" means triplet.
    }

    /** Initialization from a triplet "cs", which is first compressed */
    void construct_from_triplet(const cs& triplet);

    /** To be called by constructors only, assume previous pointers are trash
     * and overwrite them */
    void construct_from_existing_cs(const cs& sm);

    /** free buffers (deallocate the memory of the i,p,x buffers) */
    void internal_free_mem();

    /** Copy the data from an existing "cs" CSparse data structure */
    void copy(const cs* const sm);

    /** Fast copy the data from an existing "cs" CSparse data structure, copying
     * the pointers and leaving NULLs in the source structure. */
    void copy_fast(cs* const sm);

   public:
    /** @name Constructors, destructor & copy operations
        @{  */

    /** Create an initially empty sparse matrix, in the "triplet" form.
     *  Notice that you must call "compressFromTriplet" after populating the
     * matrix and before using the math operatons on this matrix.
     *  The initial size can be later on extended with insert_entry() or
     * setRowCount() & setColCount().
     * \sa insert_entry, setRowCount, setColCount
     */
    CSparseMatrix(const size_t nRows = 0, const size_t nCols = 0);

    /** A good way to initialize a sparse matrix from a list of non nullptr
     * elements.
     *  This constructor takes all the non-zero entries in "data" and builds a
     * column-compressed sparse representation.
     */
    template <typename T>
    inline CSparseMatrix(const CSparseMatrixTemplate<T>& data)
    {
        ASSERTMSG_(
            !data.empty(),
            "Input data must contain at least one non-zero element.");
        sparse_matrix.i =
            nullptr;  // This is to know they shouldn't be tried to free()
        sparse_matrix.p = nullptr;
        sparse_matrix.x = nullptr;

        // 1) Create triplet matrix
        CSparseMatrix triplet(data.rows(), data.cols());
        // 2) Put data in:
        for (auto it = data.begin(); it != data.end(); ++it)
            triplet.insert_entry_fast(
                it->first.first, it->first.second, it->second);

        // 3) Compress:
        construct_from_triplet(triplet.sparse_matrix);
    }

    // We can't do a simple "template <class ANYMATRIX>" since it would be tried
    // to match against "cs*"...

    /** Constructor from a dense matrix of any kind existing in MRPT, creating a
     * "column-compressed" sparse matrix. */
    template <typename T, size_t N, size_t M>
    inline explicit CSparseMatrix(const CMatrixFixed<T, N, M>& MAT)
    {
        construct_from_mrpt_mat(MAT);
    }

    /** Constructor from a dense matrix of any kind existing in MRPT, creating a
     * "column-compressed" sparse matrix. */
    template <typename T>
    inline explicit CSparseMatrix(const CMatrixDynamic<T>& MAT)
    {
        construct_from_mrpt_mat(MAT);
    }

    /** Copy constructor */
    CSparseMatrix(const CSparseMatrix& other);

    /** Copy constructor from an existing "cs" CSparse data structure */
    explicit CSparseMatrix(const cs* const sm);

    /** Destructor */
    virtual ~CSparseMatrix();

    /** Copy operator from another existing object */
    void operator=(const CSparseMatrix& other);

    /** Fast swap contents with another sparse matrix */
    void swap(CSparseMatrix& other);

    /** Erase all previous contents and leave the matrix as a "triplet" ROWS x
     * COLS matrix without any nonzero entry. */
    void clear(const size_t nRows = 1, const size_t nCols = 1);

    /** @}  */

    /** @name Math operations (the interesting stuff...)
        @{ */

    /** this = A+B */
    void add_AB(const CSparseMatrix& A, const CSparseMatrix& B);
    /** this = A*B */
    void multiply_AB(const CSparseMatrix& A, const CSparseMatrix& B);
    /** out_res = this * b */
    void matProductOf_Ab(
        const mrpt::math::CVectorDouble& b,
        mrpt::math::CVectorDouble& out_res) const;

    inline CSparseMatrix operator+(const CSparseMatrix& other) const
    {
        CSparseMatrix RES;
        RES.add_AB(*this, other);
        return RES;
    }
    inline CSparseMatrix operator*(const CSparseMatrix& other) const
    {
        CSparseMatrix RES;
        RES.multiply_AB(*this, other);
        return RES;
    }
    inline mrpt::math::CVectorDouble operator*(
        const mrpt::math::CVectorDouble& other) const
    {
        mrpt::math::CVectorDouble res;
        matProductOf_Ab(other, res);
        return res;
    }
    inline void operator+=(const CSparseMatrix& other)
    {
        this->add_AB(*this, other);
    }
    inline void operator*=(const CSparseMatrix& other)
    {
        this->multiply_AB(*this, other);
    }
    CSparseMatrix transpose() const;

    /** @}  */

    /** @ Access the matrix, get, set elements, etc.
        @{ */

    /** ONLY for TRIPLET matrices: insert a new non-zero entry in the matrix.
     *  This method cannot be used once the matrix is in column-compressed
     * form.
     *  The size of the matrix will be automatically extended if the indices
     * are out of the current limits.
     * \sa isTriplet, compressFromTriplet
     */
    void insert_entry(const size_t row, const size_t col, const double val);

    /** This was an optimized version, but is now equivalent to insert_entry()
     * due to the need to be compatible with unmodified CSparse system
     * libraries. */
    inline void insert_entry_fast(
        const size_t row, const size_t col, const double val)
    {
        insert_entry(row, col, val);
    }

    /** ONLY for TRIPLET matrices: insert a given matrix (in any of the MRPT
     * formats) at a given location of the sparse matrix.
     *  This method cannot be used once the matrix is in column-compressed
     * form.
     *  The size of the matrix will be automatically extended if the indices
     * are out of the current limits.
     *  Since this is inline, it can be very efficient for fixed-size MRPT
     * matrices.
     * \sa isTriplet, compressFromTriplet, insert_entry
     */
    template <class MATRIX>
    inline void insert_submatrix(
        const size_t row, const size_t col, const MATRIX& M)
    {
        if (!isTriplet())
            THROW_EXCEPTION(
                "insert_entry() is only available for sparse matrix in "
                "'triplet' format.");
        const size_t nR = M.rows();
        const size_t nC = M.cols();
        for (size_t r = 0; r < nR; r++)
            for (size_t c = 0; c < nC; c++)
                insert_entry_fast(row + r, col + c, M(r, c));
        // If needed, extend the size of the matrix:
        sparse_matrix.m = std::max(sparse_matrix.m, int(row + nR));
        sparse_matrix.n = std::max(sparse_matrix.n, int(col + nC));
    }

    /** ONLY for TRIPLET matrices: convert the matrix in a column-compressed
     * form.
     * \sa insert_entry
     */
    void compressFromTriplet();

    /** Return a dense representation of the sparse matrix.
     * \sa saveToTextFile_dense
     */
    void get_dense(CMatrixDouble& outMat) const;

    /** Static method to convert a "cs" structure into a dense representation of
     * the sparse matrix.
     */
    static void cs2dense(const cs& SM, CMatrixDouble& outMat);

    /** save as a dense matrix to a text file \return False on any error.
     */
    bool saveToTextFile_dense(const std::string& filName);

    /** Save sparse structure to a text file loadable from MATLAB (can be called
     * on triplet or CCS matrices).
     *
     *  The format of the text file is:
     *  \code
     *   NUM_ROWS   NUM_COLS   NUM_NON_ZERO_MAX
     *   row_1   col_1   value_1
     *   row_2   col_2   value_2
     *   ...
     *  \endcode
     *
     *  Instructions for loading from MATLAB in triplet form will be
     * automatically writen to the
     *  output file as comments in th first lines:
     *
     *   \code
     *      D=load('file.txt');
     *      SM=spconvert(D(2:end,:));
     *        or, to always preserve the actual matrix size m x n:
     *      m=D(1,1); n=D(1,2); nzmax=D(1,3);
     *      Di=D(2:end,1); Dj=D(2:end,2); Ds=D(2:end,3);
     *      M=sparse(Di,Dj,Ds, m,n, nzmax);
     *   \endcode
     * \return False on any error.
     */
    bool saveToTextFile_sparse(const std::string& filName);

    // Very basic, standard methods that MRPT methods expect for any matrix:
    inline size_t rows() const { return sparse_matrix.m; }
    inline size_t cols() const { return sparse_matrix.n; }
    /** Change the number of rows in the matrix (can't be lower than current
     * size) */
    inline void setRowCount(const size_t nRows)
    {
        ASSERT_(nRows >= (size_t)sparse_matrix.m);
        sparse_matrix.m = nRows;
    }
    inline void setColCount(const size_t nCols)
    {
        ASSERT_(nCols >= (size_t)sparse_matrix.n);
        sparse_matrix.n = nCols;
    }

    /** Returns true if this sparse matrix is in "triplet" form. \sa
     * isColumnCompressed */
    inline bool isTriplet() const
    {
        return sparse_matrix.nz >= 0;
    }  // <0 means "column compressed", ">=0" means triplet.

    /** Returns true if this sparse matrix is in "column compressed" form. \sa
     * isTriplet */
    inline bool isColumnCompressed() const
    {
        return sparse_matrix.nz < 0;
    }  // <0 means "column compressed", ">=0" means triplet.

    /** @} */

    /** @name Cholesky factorization
        @{  */

    /** Auxiliary class to hold the results of a Cholesky factorization of a
     *sparse matrix.
     *  This implementation does not allow updating/downdating.
     *
     *  Usage example:
     *   \code
     *     CSparseMatrix  SM(100,100);
     *     SM.insert_entry(i,j, val); ...
     *     SM.compressFromTriplet();
     *
     *     // Do Cholesky decomposition:
     *      CSparseMatrix::CholeskyDecomp  CD(SM);
     *     CD.get_inverse();
     *     ...
     *   \endcode
     *
     * \note Only the upper triangular part of the input matrix is accessed.
     * \note This class was initially adapted from "robotvision", by Hauke
     *Strasdat, Steven Lovegrove and Andrew J. Davison. See
     *http://www.openslam.org/robotvision.html
     * \note This class designed to be "uncopiable".
     * \sa The main class: CSparseMatrix
     */
    class CholeskyDecomp
    {
       private:
        css* m_symbolic_structure;
        csn* m_numeric_structure;
        /** A const reference to the original matrix used to build this
         * decomposition. */
        const CSparseMatrix* m_originalSM;

       public:
        /** Constructor from a square definite-positive sparse matrix A, which
         * can be use to solve Ax=b
         *   The actual Cholesky decomposition takes places in this
         * constructor.
         *  \note Only the upper triangular part of the matrix is accessed.
         *  \exception std::runtime_error On non-square input matrix.
         *  \exception mrpt::math::CExceptionNotDefPos On non-definite-positive
         * matrix as input.
         */
        CholeskyDecomp(const CSparseMatrix& A);
        CholeskyDecomp(const CholeskyDecomp& A) = delete;

        CholeskyDecomp& operator=(const CholeskyDecomp&) = delete;

        /** Destructor */
        virtual ~CholeskyDecomp();

        /** Return the L matrix (L*L' = M), as a dense matrix. */
        inline CMatrixDouble get_L() const
        {
            CMatrixDouble L;
            get_L(L);
            return L;
        }

        /** Return the L matrix (L*L' = M), as a dense matrix. */
        void get_L(CMatrixDouble& out_L) const;

        /** Return the vector from a back-substitution step that solves: Ux=b */
        template <class VECTOR>
        inline VECTOR backsub(const VECTOR& b) const
        {
            VECTOR res;
            backsub(b, res);
            return res;
        }

        /** Return the vector from a back-substitution step that solves: Ux=b.
         * Vectors can be Eigen::VectorXd or mrpt::math::CVectorDouble   */
        void backsub(const CVectorDouble& b, CVectorDouble& result_x) const;

        /** overload for double pointers which assume the user has reserved the
         * output memory for \a result */
        void backsub(const double* b, double* result, const size_t N) const;

        /** Update the Cholesky factorization from an updated vesion of the
         * original input, square definite-positive sparse matrix.
         *  NOTE: This new matrix MUST HAVE exactly the same sparse structure
         * than the original one.
         */
        void update(const CSparseMatrix& new_SM);
    };
    static_assert(
        !std::is_copy_constructible_v<CholeskyDecomp> &&
            !std::is_copy_assignable_v<CholeskyDecomp>,
        "Copy Check");

    /** @} */

};  // end class CSparseMatrix
}  // namespace mrpt::math