/* glpmat.h (linear algebra routines) */

/***********************************************************************

*  This code is part of GLPK (GNU Linear Programming Kit).

*

*  Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,

*  2009, 2010 Andrew Makhorin, Department for Applied Informatics,

*  Moscow Aviation Institute, Moscow, Russia. All rights reserved.

*  E-mail: <mao@gnu.org>.

*

*  GLPK is free software: you can redistribute it and/or modify it

*  under the terms of the GNU General Public License as published by

*  the Free Software Foundation, either version 3 of the License, or

*  (at your option) any later version.

*

*  GLPK is distributed in the hope that it will be useful, but WITHOUT

*  ANY WARRANTY; without even the implied warranty of MERCHANTABILITY

*  or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public

*  License for more details.

*

*  You should have received a copy of the GNU General Public License

*  along with GLPK. If not, see <http://www.gnu.org/licenses/>.

***********************************************************************/

#ifndef GLPMAT_H

#define GLPMAT_H

/***********************************************************************

*  FULL-VECTOR STORAGE

* 

*  For a sparse vector x having n elements, ne of which are non-zero,

*  the full-vector storage format uses two arrays x_ind and x_vec, which

*  are set up as follows:

* 

*  x_ind is an integer array of length [1+ne]. Location x_ind[0] is

*  not used, and locations x_ind[1], ..., x_ind[ne] contain indices of

*  non-zero elements in vector x.

* 

*  x_vec is a floating-point array of length [1+n]. Location x_vec[0]

*  is not used, and locations x_vec[1], ..., x_vec[n] contain numeric

*  values of ALL elements in vector x, including its zero elements.

* 

*  Let, for example, the following sparse vector x be given:

* 

*     (0, 1, 0, 0, 2, 3, 0, 4)

* 

*  Then the arrays are:

* 

*     x_ind = { X; 2, 5, 6, 8 }

* 

*     x_vec = { X; 0, 1, 0, 0, 2, 3, 0, 4 }

* 

*  COMPRESSED-VECTOR STORAGE

* 

*  For a sparse vector x having n elements, ne of which are non-zero,

*  the compressed-vector storage format uses two arrays x_ind and x_vec,

*  which are set up as follows:

* 

*  x_ind is an integer array of length [1+ne]. Location x_ind[0] is

*  not used, and locations x_ind[1], ..., x_ind[ne] contain indices of

*  non-zero elements in vector x.

* 

*  x_vec is a floating-point array of length [1+ne]. Location x_vec[0]

*  is not used, and locations x_vec[1], ..., x_vec[ne] contain numeric

*  values of corresponding non-zero elements in vector x.

* 

*  Let, for example, the following sparse vector x be given:

* 

*     (0, 1, 0, 0, 2, 3, 0, 4)

* 

*  Then the arrays are:

*

*     x_ind = { X; 2, 5, 6, 8 }

* 

*     x_vec = { X; 1, 2, 3, 4 }

* 

*  STORAGE-BY-ROWS

* 

*  For a sparse matrix A, which has m rows, n columns, and ne non-zero

*  elements the storage-by-rows format uses three arrays A_ptr, A_ind,

*  and A_val, which are set up as follows:

* 

*  A_ptr is an integer array of length [1+m+1] also called "row pointer

*  array". It contains the relative starting positions of each row of A

*  in the arrays A_ind and A_val, i.e. element A_ptr[i], 1 <= i <= m,

*  indicates where row i begins in the arrays A_ind and A_val. If all

*  elements in row i are zero, then A_ptr[i] = A_ptr[i+1]. Location

*  A_ptr[0] is not used, location A_ptr[1] must contain 1, and location

*  A_ptr[m+1] must contain ne+1 that indicates the position after the

*  last element in the arrays A_ind and A_val.

* 

*  A_ind is an integer array of length [1+ne]. Location A_ind[0] is not

*  used, and locations A_ind[1], ..., A_ind[ne] contain column indices

*  of (non-zero) elements in matrix A.

*

*  A_val is a floating-point array of length [1+ne]. Location A_val[0]

*  is not used, and locations A_val[1], ..., A_val[ne] contain numeric

*  values of non-zero elements in matrix A.

* 

*  Non-zero elements of matrix A are stored contiguously, and the rows

*  of matrix A are stored consecutively from 1 to m in the arrays A_ind

*  and A_val. The elements in each row of A may be stored in any order

*  in A_ind and A_val. Note that elements with duplicate column indices

*  are not allowed.

* 

*  Let, for example, the following sparse matrix A be given:

* 

*     | 11  . 13  .  .  . |

*     | 21 22  . 24  .  . |

*     |  . 32 33  .  .  . |

*     |  .  . 43 44  . 46 |

*     |  .  .  .  .  .  . |

*     | 61 62  .  .  . 66 |

* 

*  Then the arrays are:

* 

*     A_ptr = { X; 1, 3, 6, 8, 11, 11; 14 }

*

*     A_ind = { X;  1,  3;  4,  2,  1;  2,  3;  4,  3,  6;  1,  2,  6 }

* 

*     A_val = { X; 11, 13; 24, 22, 21; 32, 33; 44, 43, 46; 61, 62, 66 }

* 

*  PERMUTATION MATRICES

* 

*  Let P be a permutation matrix of the order n. It is represented as

*  an integer array P_per of length [1+n+n] as follows: if p[i,j] = 1,

*  then P_per[i] = j and P_per[n+j] = i. Location P_per[0] is not used.

* 

*  Let A' = P*A. If i-th row of A corresponds to i'-th row of A', then

*  P_per[i'] = i and P_per[n+i] = i'.

* 

*  References:

* 

*  1. Gustavson F.G. Some basic techniques for solving sparse systems of

*     linear equations. In Rose and Willoughby (1972), pp. 41-52.

* 

*  2. Basic Linear Algebra Subprograms Technical (BLAST) Forum Standard.

*     University of Tennessee (2001). */

#define check_fvs _glp_mat_check_fvs

int check_fvs(int n, int nnz, int ind[], double vec[]);

/* check sparse vector in full-vector storage format */

#define check_pattern _glp_mat_check_pattern

int check_pattern(int m, int n, int A_ptr[], int A_ind[]);

/* check pattern of sparse matrix */

#define transpose _glp_mat_transpose

void transpose(int m, int n, int A_ptr[], int A_ind[], double A_val[],

      int AT_ptr[], int AT_ind[], double AT_val[]);

/* transpose sparse matrix */

#define adat_symbolic _glp_mat_adat_symbolic

int *adat_symbolic(int m, int n, int P_per[], int A_ptr[], int A_ind[],

      int S_ptr[]);

/* compute S = P*A*D*A'*P' (symbolic phase) */

#define adat_numeric _glp_mat_adat_numeric

void adat_numeric(int m, int n, int P_per[],

      int A_ptr[], int A_ind[], double A_val[], double D_diag[],

      int S_ptr[], int S_ind[], double S_val[], double S_diag[]);

/* compute S = P*A*D*A'*P' (numeric phase) */

#define min_degree _glp_mat_min_degree

void min_degree(int n, int A_ptr[], int A_ind[], int P_per[]);

/* minimum degree ordering */

#define amd_order1 _glp_mat_amd_order1

void amd_order1(int n, int A_ptr[], int A_ind[], int P_per[]);

/* approximate minimum degree ordering (AMD) */

#define symamd_ord _glp_mat_symamd_ord

void symamd_ord(int n, int A_ptr[], int A_ind[], int P_per[]);

/* approximate minimum degree ordering (SYMAMD) */

#define chol_symbolic _glp_mat_chol_symbolic

int *chol_symbolic(int n, int A_ptr[], int A_ind[], int U_ptr[]);

/* compute Cholesky factorization (symbolic phase) */

#define chol_numeric _glp_mat_chol_numeric

int chol_numeric(int n,

      int A_ptr[], int A_ind[], double A_val[], double A_diag[],

      int U_ptr[], int U_ind[], double U_val[], double U_diag[]);

/* compute Cholesky factorization (numeric phase) */

#define u_solve _glp_mat_u_solve

void u_solve(int n, int U_ptr[], int U_ind[], double U_val[],

      double U_diag[], double x[]);

/* solve upper triangular system U*x = b */

#define ut_solve _glp_mat_ut_solve

void ut_solve(int n, int U_ptr[], int U_ind[], double U_val[],

      double U_diag[], double x[]);

/* solve lower triangular system U'*x = b */

#endif

/* eof */