%* glpk02.tex *%

 \chapter{Basic API Routines}

 This chapter describes GLPK API routines intended for using in

 application programs.

 \subsubsection*{Library header}

 All GLPK API data types and routines are defined in the header file

 \verb|glpk.h|. It should be included in all source files which use

 GLPK API, either directly or indirectly through some other header file

 as follows:

 \begin{verbatim}

    #include <glpk.h>

 \end{verbatim}

 \subsubsection*{Error handling}

 If some GLPK API routine detects erroneous or incorrect data passed by

 the application program, it writes appropriate diagnostic messages to

 the terminal and then abnormally terminates the application program.

 In most practical cases this allows to simplify programming by avoiding

 numerous checks of return codes. Thus, in order to prevent crashing the

 application program should check all data, which are suspected to be

 incorrect, before calling GLPK API routines.

 Should note that this kind of error handling is used only in cases of

 incorrect data passed by the application program. If, for example, the

 application program calls some GLPK API routine to read data from an

 input file and these data are incorrect, the GLPK API routine reports

 about error in the usual way by means of the return code.

 \subsubsection*{Thread safety}

 Currently GLPK API routines are non-reentrant and therefore cannot be

 used in multi-threaded programs.

 \subsubsection*{Array indexing}

 Normally all GLPK API routines start array indexing from 1, not from 0

 (except the specially stipulated cases). This means, for example, that

 if some vector $x$ of the length $n$ is passed as an array to some GLPK

 API routine, the latter expects vector components to be placed in

 locations \verb|x[1]|, \verb|x[2]|, \dots, \verb|x[n]|, and the location

 \verb|x[0]| normally is not used.

 In order to avoid indexing errors it is most convenient and most

 reliable to declare the array \verb|x| as follows:

 \begin{verbatim}

    double x[1+n];

 \end{verbatim}

 \noindent

 or to allocate it as follows:

 \begin{verbatim}

    double *x;

    . . .

    x = calloc(1+n, sizeof(double));

 \end{verbatim}

 \noindent

 In both cases one extra location \verb|x[0]| is reserved that allows

 passing the array to GLPK routines in a usual way.

 \section{Problem object}

 All GLPK API routines deal with so called {\it problem object}, which

 is a program object of type \verb|glp_prob| and intended to represent

 a particular LP or MIP instance.

 The type \verb|glp_prob| is a data structure declared in the header

 file \verb|glpk.h| as follows:

 \begin{verbatim}

    typedef struct { ... } glp_prob;

 \end{verbatim}

 Problem objects (i.e. program objects of the \verb|glp_prob| type) are

 allocated and managed internally by the GLPK API routines. The

 application program should never use any members of the \verb|glp_prob|

 structure directly and should deal only with pointers to these objects

 (that is, \verb|glp_prob *| values).

 \pagebreak

 The problem object consists of five segments, which are:

 $\bullet$ problem segment,

 $\bullet$ basis segment,

 $\bullet$ interior point segment,

 $\bullet$ MIP segment, and

 $\bullet$ control parameters and statistics segment.

 \subsubsection*{Problem segment}

 The {\it problem segment} contains original LP/MIP data, which

 corresponds to the problem formulation (1.1)---(1.3) (see Section

 \ref{seclp}, page \pageref{seclp}). It includes the following

 components:

 $\bullet$ rows (auxiliary variables),

 $\bullet$ columns (structural variables),

 $\bullet$ objective function, and

 $\bullet$ constraint matrix.

 Rows and columns have the same set of the following attributes:

 $\bullet$ ordinal number,

 $\bullet$ symbolic name (1 up to 255 arbitrary graphic characters),

 $\bullet$ type (free, lower bound, upper bound, double bound, fixed),

 $\bullet$ numerical values of lower and upper bounds,

 $\bullet$ scale factor.

 {\it Ordinal numbers} are intended for referencing rows and columns.

 Row ordinal numbers are integers $1, 2, \dots, m$, and column ordinal

 numbers are integers $1, 2, \dots, n$, where $m$ and $n$ are,

 respectively, the current number of rows and columns in the problem

 object.

 {\it Symbolic names} are intended for informational purposes. They also

 can be used for referencing rows and columns.

 {\it Types and bounds} of rows (auxiliary variables) and columns

 (structural variables) are explained above (see Section \ref{seclp},

 page \pageref{seclp}).

 {\it Scale factors} are used internally for scaling rows and columns of

 the constraint matrix.

 Information about the {\it objective function} includes numerical

 values of objective coefficients and a flag, which defines the

 optimization direction (i.e. minimization or maximization).

 The {\it constraint matrix} is a $m \times n$ rectangular matrix built

 of constraint coefficients $a_{ij}$, which defines the system of linear

 constraints (1.2) (see Section \ref{seclp}, page \pageref{seclp}). This

 matrix is stored in the problem object in both row-wise and column-wise

 sparse formats.

 Once the problem object has been created, the application program can

 access and modify any components of the problem segment in arbitrary

 order.

 \subsubsection*{Basis segment}

 The {\it basis segment} of the problem object keeps information related

 to the current basic solution. It includes:

 $\bullet$ row and column statuses,

 $\bullet$ basic solution statuses,

 $\bullet$ factorization of the current basis matrix, and

 $\bullet$ basic solution components.

 The {\it row and column statuses} define which rows and columns are

 basic and which are non-basic. These statuses may be assigned either by

 the application program of by some API routines. Note that these

 statuses are always defined independently on whether the corresponding

 basis is valid or not.

 The {\it basic solution statuses} include the {\it primal status} and

 the {\it dual status}, which are set by the simplex-based solver once

 the problem has been solved. The primal status shows whether a primal

 basic solution is feasible, infeasible, or undefined. The dual status

 shows the same for a dual basic solution.

 The {\it factorization of the basis matrix} is some factorized form

 (like LU-factorization) of the current basis matrix (defined by the

 current row and column statuses). The factorization is used by the

 simplex-based solver and kept when the solver terminates the search.

 This feature allows efficiently reoptimizing the problem after some

 modifications (for example, after changing some bounds or objective

 coefficients). It also allows performing the post-optimal analysis (for

 example, computing components of the simplex table, etc.).

 The {\it basic solution components} include primal and dual values of

 all auxiliary and structural variables for the most recently obtained

 basic solution.

 \subsubsection*{Interior point segment}

 The {\it interior point segment} is automatically allocated after the

 problem has been solved using the interior point solver. It contains

 interior point solution components, which include the solution status,

 and primal and dual values of all auxiliary and structural variables.

 \subsubsection*{MIP segment}

 The {\it MIP segment} is used only for MIP problems. This segment

 includes:

 $\bullet$ column kinds,

 $\bullet$ MIP solution status, and

 $\bullet$ MIP solution components.

 The {\it column kinds} define which columns (i.e. structural variables)

 are integer and which are continuous.

 The {\it MIP solution status} is set by the MIP solver and shows whether

 a MIP solution is integer optimal, integer feasible (non-optimal), or

 undefined.

 The {\it MIP solution components} are computed by the MIP solver and

 include primal values of all auxiliary and structural variables for the

 most recently obtained MIP solution.

 Note that in case of MIP problem the basis segment corresponds to

 the optimal solution of LP relaxation, which is also available to the

 application program.

 Currently the search tree is not kept in the MIP segment. Therefore if

 the search has been terminated, it cannot be continued.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Problem creating and modifying routines}

 \subsection{glp\_create\_prob---create problem object}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 glp_prob *glp_create_prob(void);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_create_prob| creates a new problem object, which

 initially is ``empty'', i.e. has no rows and columns.

 \subsubsection*{Returns}

 The routine returns a pointer to the created object, which should be

 used in any subsequent operations on this object.

 \subsection{glp\_set\_prob\_name---assign (change) problem name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_prob_name(glp_prob *lp, const char *name);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_prob_name| assigns a given symbolic

 \verb|name| (1 up to 255 characters) to the specified problem object.

 If the parameter \verb|name| is \verb|NULL| or empty string, the routine

 erases an existing symbolic name of the problem object.

 \subsection{glp\_set\_obj\_name---assign (change) objective function

 name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_obj_name(glp_prob *lp, const char *name);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_obj_name| assigns a given symbolic

 \verb|name| (1 up to 255 characters) to the objective function of the

 specified problem object.

 If the parameter \verb|name| is \verb|NULL| or empty string, the routine

 erases an existing symbolic name of the objective function.

 \subsection{glp\_set\_obj\_dir---set (change) optimization direction\\

 flag}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_obj_dir(glp_prob *lp, int dir);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_obj_dir| sets (changes) the optimization

 direction flag (i.e. ``sense'' of the objective function) as specified

 by the parameter \verb|dir|:

 \begin{tabular}{@{}ll}

 \verb|GLP_MIN| & minimization; \\

 \verb|GLP_MAX| & maximization. \\

 \end{tabular}

 \noindent

 (Note that by default the problem is minimization.)

 \subsection{glp\_add\_rows---add new rows to problem object}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_add_rows(glp_prob *lp, int nrs);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_add_rows| adds \verb|nrs| rows (constraints) to

 the specified problem object. New rows are always added to the end of

 the row list, so the ordinal numbers of existing rows are not changed.

 Being added each new row is initially free (unbounded) and has empty

 list of the constraint coefficients.

 \subsubsection*{Returns}

 The routine \verb|glp_add_rows| returns the ordinal number of the first

 new row added to the problem object.

 \newpage

 \subsection{glp\_add\_cols---add new columns to problem object}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_add_cols(glp_prob *lp, int ncs);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_add_cols| adds \verb|ncs| columns (structural

 variables) to the specified problem object. New columns are always added

 to the end of the column list, so the ordinal numbers of existing

 columns are not changed.

 Being added each new column is initially fixed at zero and has empty

 list of the constraint coefficients.

 \subsubsection*{Returns}

 The routine \verb|glp_add_cols| returns the ordinal number of the first

 new column added to the problem object.

 \subsection{glp\_set\_row\_name---assign (change) row name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_row_name(glp_prob *lp, int i, const char *name);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_row_name| assigns a given symbolic

 \verb|name| (1 up to 255 characters) to \verb|i|-th row (auxiliary

 variable) of the specified problem object.

 If the parameter \verb|name| is \verb|NULL| or empty string, the routine

 erases an existing name of $i$-th row.

 \subsection{glp\_set\_col\_name---assign (change) column name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_col_name(glp_prob *lp, int j, const char *name);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_col_name| assigns a given symbolic

 \verb|name| (1 up to 255 characters) to \verb|j|-th column (structural

 variable) of the specified problem object.

 If the parameter \verb|name| is \verb|NULL| or empty string, the routine

 erases an existing name of $j$-th column.

 \subsection{glp\_set\_row\_bnds---set (change) row bounds}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_row_bnds(glp_prob *lp, int i, int type,

       double lb, double ub);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_row_bnds| sets (changes) the type and bounds

 of \verb|i|-th row (auxiliary variable) of the specified problem object.

 The parameters \verb|type|, \verb|lb|, and \verb|ub| specify the type,

 lower bound, and upper bound, respectively, as follows:

 \begin{center}

 \begin{tabular}{cr@{}c@{}ll}

 Type & \multicolumn{3}{c}{Bounds} & Comment \\

 \hline

 \verb|GLP_FR| & $-\infty <$ &$\ x\ $& $< +\infty$

    & Free (unbounded) variable \\

 \verb|GLP_LO| & $lb \leq$ &$\ x\ $& $< +\infty$

    & Variable with lower bound \\

 \verb|GLP_UP| & $-\infty <$ &$\ x\ $& $\leq ub$

    & Variable with upper bound \\

 \verb|GLP_DB| & $lb \leq$ &$\ x\ $& $\leq ub$

    & Double-bounded variable \\

 \verb|GLP_FX| & $lb =$ &$\ x\ $& $= ub$

    & Fixed variable \\

 \end{tabular}

 \end{center}

 \noindent

 where $x$ is the auxiliary variable associated with $i$-th row.

 If the row has no lower bound, the parameter \verb|lb| is ignored. If

 the row has no upper bound, the parameter \verb|ub| is ignored. If the

 row is an equality constraint (i.e. the corresponding auxiliary variable

 is of fixed type), only the parameter \verb|lb| is used while the

 parameter \verb|ub| is ignored.

 Being added to the problem object each row is initially free, i.e. its

 type is \verb|GLP_FR|.

 \newpage

 \subsection{glp\_set\_col\_bnds---set (change) column bounds}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_col_bnds(glp_prob *lp, int j, int type,

       double lb, double ub);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_col_bnds| sets (changes) the type and bounds

 of \verb|j|-th column (structural variable) of the specified problem

 object.

 The parameters \verb|type|, \verb|lb|, and \verb|ub| specify the type,

 lower bound, and upper bound, respectively, as follows:

 \begin{center}

 \begin{tabular}{cr@{}c@{}ll}

 Type & \multicolumn{3}{c}{Bounds} & Comment \\

 \hline

 \verb|GLP_FR| & $-\infty <$ &$\ x\ $& $< +\infty$

    & Free (unbounded) variable \\

 \verb|GLP_LO| & $lb \leq$ &$\ x\ $& $< +\infty$

    & Variable with lower bound \\

 \verb|GLP_UP| & $-\infty <$ &$\ x\ $& $\leq ub$

    & Variable with upper bound \\

 \verb|GLP_DB| & $lb \leq$ &$\ x\ $& $\leq ub$

    & Double-bounded variable \\

 \verb|GLP_FX| & $lb =$ &$\ x\ $& $= ub$

    & Fixed variable \\

 \end{tabular}

 \end{center}

 \noindent

 where $x$ is the structural variable associated with $j$-th column.

 If the column has no lower bound, the parameter \verb|lb| is ignored.

 If the column has no upper bound, the parameter \verb|ub| is ignored.

 If the column is of fixed type, only the parameter \verb|lb| is used

 while the parameter \verb|ub| is ignored.

 Being added to the problem object each column is initially fixed at

 zero, i.e. its type is \verb|GLP_FX| and both bounds are 0.

 \subsection{glp\_set\_obj\_coef---set (change) objective coefficient

 or constant term}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_obj_coef(glp_prob *lp, int j, double coef);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_obj_coef| sets (changes) the objective

 coefficient at \verb|j|-th column (structural variable). A new value of

 the objective coefficient is specified by the parameter \verb|coef|.

 If the parameter \verb|j| is 0, the routine sets (changes) the constant

 term (``shift'') of the objective function.

 \subsection{glp\_set\_mat\_row---set (replace) row of the constraint

 matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_mat_row(glp_prob *lp, int i, int len,

       const int ind[], const double val[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_mat_row| stores (replaces) the contents of

 \verb|i|-th row of the constraint matrix of the specified problem

 object.

 Column indices and numerical values of new row elements must be placed

 in locations \verb|ind[1]|, \dots, \verb|ind[len]| and \verb|val[1]|,

 \dots, \verb|val[len]|, respectively, where $0 \leq$ \verb|len| $\leq n$

 is the new length of $i$-th row, $n$ is the current number of columns in

 the problem object. Elements with identical column indices are not

 allowed. Zero elements are allowed, but they are not stored in the

 constraint matrix.

 If the parameter \verb|len| is 0, the parameters \verb|ind| and/or

 \verb|val| can be specified as \verb|NULL|.

 \subsection{glp\_set\_mat\_col---set (replace) column of the

 constr\-aint matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_mat_col(glp_prob *lp, int j, int len,

       const int ind[], const double val[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_mat_col| stores (replaces) the contents of

 \verb|j|-th column of the constraint matrix of the specified problem

 object.

 Row indices and numerical values of new column elements must be placed

 in locations \verb|ind[1]|, \dots, \verb|ind[len]| and \verb|val[1]|,

 \dots, \verb|val[len]|, respectively, where $0 \leq$ \verb|len| $\leq m$

 is the new length of $j$-th column, $m$ is the current number of rows in

 the problem object. Elements with identical row indices are not allowed.

 Zero elements are allowed, but they are not stored in the constraint

 matrix.

 If the parameter \verb|len| is 0, the parameters \verb|ind| and/or

 \verb|val| can be specified as \verb|NULL|.

 \subsection{glp\_load\_matrix---load (replace) the whole constraint

 matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_load_matrix(glp_prob *lp, int ne, const int ia[],

       const int ja[], const double ar[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_load_matrix| loads the constraint matrix passed

 in  the arrays \verb|ia|, \verb|ja|, and \verb|ar| into the specified

 problem object. Before loading the current contents of the constraint

 matrix is destroyed.

 Constraint coefficients (elements of the constraint matrix) must be

 specified as triplets (\verb|ia[k]|, \verb|ja[k]|, \verb|ar[k]|) for

 $k=1,\dots,ne$, where \verb|ia[k]| is the row index, \verb|ja[k]| is

 the column index, and \verb|ar[k]| is a numeric value of corresponding

 constraint coefficient. The parameter \verb|ne| specifies the total

 number of (non-zero) elements in the matrix to be loaded. Coefficients

 with identical indices are not allowed. Zero coefficients are allowed,

 however, they are not stored in the constraint matrix.

 If the parameter \verb|ne| is 0, the parameters \verb|ia|, \verb|ja|,

 and/or \verb|ar| can be specified as \verb|NULL|.

 \subsection{glp\_check\_dup---check for duplicate elements in sparse

 matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_check_dup(int m, int n, int ne, const int ia[],

    const int ja[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_check_dup checks| for duplicate elements (that

 is, elements with identical indices) in a sparse matrix specified in

 the coordinate format.

 The parameters $m$ and $n$ specifies, respectively, the number of rows

 and columns in the matrix, $m\geq 0$, $n\geq 0$.

 The parameter {\it ne} specifies the number of (structurally) non-zero

 elements in the matrix, {\it ne} $\geq 0$.

 Elements of the matrix are specified as doublets $(ia[k],ja[k])$ for

 $k=1,\dots,ne$, where $ia[k]$ is a row index, $ja[k]$ is a column index.

 The routine \verb|glp_check_dup| can be used prior to a call to the

 routine \verb|glp_load_matrix| to check that the constraint matrix to

 be loaded has no duplicate elements.

 \subsubsection*{Returns}

 The routine \verb|glp_check_dup| returns one of the following values:

 \noindent

 \begin{tabular}{@{}r@{\ }c@{\ }l@{}}

 0&---&the matrix has no duplicate elements;\\

 $-k$&---&indices $ia[k]$ or/and $ja[k]$ are out of range;\\

 $+k$&---&element $(ia[k],ja[k])$ is duplicate.\\

 \end{tabular}

 \subsection{glp\_sort\_matrix---sort elements of the constraint matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_sort_matrix(glp_prob *P);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_sort_matrix| sorts elements of the constraint

 matrix rebuilding its row and column linked lists. On exit from the

 routine the constraint matrix is not changed, however, elements in the

 row linked lists become ordered by ascending column indices, and the

 elements in the column linked lists become ordered by ascending row

 indices.

 \subsection{glp\_del\_rows---delete rows from problem object}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_del_rows(glp_prob *lp, int nrs, const int num[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_del_rows| deletes rows from the specified problem

 ob-\linebreak ject. Ordinal numbers of rows to be deleted should be

 placed in locations \verb|num[1]|, \dots, \verb|num[nrs]|, where

 ${\tt nrs}>0$.

 Note that deleting rows involves changing ordinal numbers of other

 rows remaining in the problem object. New ordinal numbers of the

 remaining rows are assigned under the assumption that the original

 order of rows is not changed. Let, for example, before deletion there

 be five rows $a$, $b$, $c$, $d$, $e$ with ordinal numbers 1, 2, 3, 4, 5,

 and let rows $b$ and $d$ have been deleted. Then after deletion the

 remaining rows $a$, $c$, $e$ are assigned new oridinal numbers 1, 2, 3.

 \subsection{glp\_del\_cols---delete columns from problem object}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_del_cols(glp_prob *lp, int ncs, const int num[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_del_cols| deletes columns from the specified

 problem object. Ordinal numbers of columns to be deleted should be

 placed in locations \verb|num[1]|, \dots, \verb|num[ncs]|, where

 ${\tt ncs}>0$.

 Note that deleting columns involves changing ordinal numbers of other

 columns remaining in the problem object. New ordinal numbers of the

 remaining columns are assigned under the assumption that the original

 order of columns is not changed. Let, for example, before deletion there

 be six columns $p$, $q$, $r$, $s$, $t$, $u$ with ordinal numbers 1, 2,

 3, 4, 5, 6, and let columns $p$, $q$, $s$ have been deleted. Then after

 deletion the remaining columns $r$, $t$, $u$ are assigned new ordinal

 numbers 1, 2, 3.

 \subsection{glp\_copy\_prob---copy problem object content}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_copy_prob(glp_prob *dest, glp_prob *prob, int names);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_copy_prob| copies the content of the problem

 object \verb|prob| to the problem object \verb|dest|.

 The parameter \verb|names| is a flag. If it is \verb|GLP_ON|,

 the routine also copies all symbolic names; otherwise, if it is

 \verb|GLP_OFF|, no symbolic names are copied.

 \newpage

 \subsection{glp\_erase\_prob---erase problem object content}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_erase_prob(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_erase_prob| erases the content of the specified

 problem object. The effect of this operation is the same as if the

 problem object would be deleted with the routine \verb|glp_delete_prob|

 and then created anew with the routine \verb|glp_create_prob|, with the

 only exception that the handle (pointer) to the problem object remains

 valid.

 \subsection{glp\_delete\_prob---delete problem object}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_delete_prob(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_delete_prob| deletes a problem object, which the

 parameter \verb|lp| points to, freeing all the memory allocated to this

 object.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Problem retrieving routines}

 \subsection{glp\_get\_prob\_name---retrieve problem name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 const char *glp_get_prob_name(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_prob_name| returns a pointer to an internal

 buffer, which contains symbolic name of the problem. However, if the

 problem has no assigned name, the routine returns \verb|NULL|.

 \subsection{glp\_get\_obj\_name---retrieve objective function name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 const char *glp_get_obj_name(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_obj_name| returns a pointer to an internal

 buffer, which contains symbolic name assigned to the objective

 function. However, if the objective function has no assigned name, the

 routine returns \verb|NULL|.

 \subsection{glp\_get\_obj\_dir---retrieve optimization direction flag}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_obj_dir(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_obj_dir| returns the optimization direction

 flag (i.e. ``sense'' of the objective function):

 \begin{tabular}{@{}ll}

 \verb|GLP_MIN| & minimization; \\

 \verb|GLP_MAX| & maximization. \\

 \end{tabular}

 \pagebreak

 \subsection{glp\_get\_num\_rows---retrieve number of rows}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_num_rows(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_num_rows| returns the current number of rows

 in the specified problem object.

 \subsection{glp\_get\_num\_cols---retrieve number of columns}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_num_cols(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_num_cols| returns the current number of

 columns the specified problem object.

 \subsection{glp\_get\_row\_name---retrieve row name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 const char *glp_get_row_name(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_name| returns a pointer to an internal

 buffer, which contains a symbolic name assigned to \verb|i|-th row.

 However, if the row has no assigned name, the routine returns

 \verb|NULL|.

 \subsection{glp\_get\_col\_name---retrieve column name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 const char *glp_get_col_name(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_name| returns a pointer to an internal

 buffer, which contains a symbolic name assigned to \verb|j|-th column.

 However, if the column has no assigned name, the routine returns

 \verb|NULL|.

 \subsection{glp\_get\_row\_type---retrieve row type}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_row_type(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_type| returns the type of \verb|i|-th

 row, i.e. the type of corresponding auxiliary variable, as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_FR| & free (unbounded) variable; \\

 \verb|GLP_LO| & variable with lower bound; \\

 \verb|GLP_UP| & variable with upper bound; \\

 \verb|GLP_DB| & double-bounded variable; \\

 \verb|GLP_FX| & fixed variable. \\

 \end{tabular}

 \subsection{glp\_get\_row\_lb---retrieve row lower bound}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_row_lb(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_lb| returns the lower bound of

 \verb|i|-th row, i.e. the lower bound of corresponding auxiliary

 variable. However, if the row has no lower bound, the routine returns

 \verb|-DBL_MAX|.

 \subsection{glp\_get\_row\_ub---retrieve row upper bound}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_row_ub(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_ub| returns the upper bound of

 \verb|i|-th row, i.e. the upper bound of corresponding auxiliary

 variable. However, if the row has no upper bound, the routine returns

 \verb|+DBL_MAX|.

 \subsection{glp\_get\_col\_type---retrieve column type}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_col_type(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_type| returns the type of \verb|j|-th

 column, i.e. the type of corresponding structural variable, as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_FR| & free (unbounded) variable; \\

 \verb|GLP_LO| & variable with lower bound; \\

 \verb|GLP_UP| & variable with upper bound; \\

 \verb|GLP_DB| & double-bounded variable; \\

 \verb|GLP_FX| & fixed variable. \\

 \end{tabular}

 \subsection{glp\_get\_col\_lb---retrieve column lower bound}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_col_lb(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_lb| returns the lower bound of

 \verb|j|-th column, i.e. the lower bound of corresponding structural

 variable. However, if the column has no lower bound, the routine returns

 \verb|-DBL_MAX|.

 \subsection{glp\_get\_col\_ub---retrieve column upper bound}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_col_ub(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_ub| returns the upper bound of

 \verb|j|-th column, i.e. the upper bound of corresponding structural

 variable. However, if the column has no upper bound, the routine returns

 \verb|+DBL_MAX|.

 \subsection{glp\_get\_obj\_coef---retrieve objective coefficient or\\

 constant term}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_obj_coef(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_obj_coef| returns the objective coefficient

 at \verb|j|-th structural variable (column).

 If the parameter \verb|j| is 0, the routine returns the constant term

 (``shift'') of the objective function.

 \subsection{glp\_get\_num\_nz---retrieve number of constraint

 coefficients}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_num_nz(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_num_nz| returns the number of non-zero

 elements in the constraint matrix of the specified problem object.

 \subsection{glp\_get\_mat\_row---retrieve row of the constraint

 matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_mat_row(glp_prob *lp, int i, int ind[],

       double val[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_get_mat_row| scans (non-zero) elements of

 \verb|i|-th row of the constraint matrix of the specified problem object

 and stores their column indices and numeric values to locations

 \verb|ind[1]|, \dots, \verb|ind[len]| and \verb|val[1]|, \dots,

 \verb|val[len]|, respectively, where $0\leq{\tt len}\leq n$ is the

 number of elements in $i$-th row, $n$ is the number of columns.

 The parameter \verb|ind| and/or \verb|val| can be specified as

 \verb|NULL|, in which case corresponding information is not stored.

 \subsubsection*{Returns}

 The routine \verb|glp_get_mat_row| returns the length \verb|len|, i.e.

 the number of (non-zero) elements in \verb|i|-th row.

 \subsection{glp\_get\_mat\_col---retrieve column of the constraint\\

 matrix}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_mat_col(glp_prob *lp, int j, int ind[],

       double val[]);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_get_mat_col| scans (non-zero) elements of

 \verb|j|-th column of the constraint matrix of the specified problem

 object and stores their row indices and numeric values to locations

 \verb|ind[1]|, \dots, \verb|ind[len]| and \verb|val[1]|, \dots,

 \verb|val[len]|, respectively, where $0\leq{\tt len}\leq m$ is the

 number of elements in $j$-th column, $m$ is the number of rows.

 The parameter \verb|ind| and/or \verb|val| can be specified as

 \verb|NULL|, in which case corresponding information is not stored.

 \subsubsection*{Returns}

 The routine \verb|glp_get_mat_col| returns the length \verb|len|, i.e.

 the number of (non-zero) elements in \verb|j|-th column.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Row and column searching routines}

 \subsection{glp\_create\_index---create the name index}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_create_index(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_create_index| creates the name index for the

 specified problem object. The name index is an auxiliary data structure,

 which is intended to quickly (i.e. for logarithmic time) find rows and

 columns by their names.

 This routine can be called at any time. If the name index already

 exists, the routine does nothing.

 \subsection{glp\_find\_row---find row by its name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_find_row(glp_prob *lp, const char *name);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_find_row| returns the ordinal number of a row,

 which is assigned (by the routine \verb|glp_set_row_name|) the specified

 symbolic \verb|name|. If no such row exists, the routine returns 0.

 \subsection{glp\_find\_col---find column by its name}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_find_col(glp_prob *lp, const char *name);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_find_col| returns the ordinal number of a column,

 which is assigned (by the routine \verb|glp_set_col_name|) the specified

 symbolic \verb|name|. If no such column exists, the routine returns 0.

 \subsection{glp\_delete\_index---delete the name index}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_delete_index(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_delete_index| deletes the name index previously

 created by the routine \verb|glp_create_index| and frees the memory

 allocated to this auxiliary data structure.

 This routine can be called at any time. If the name index does not

 exist, the routine does nothing.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Problem scaling routines}

 \subsection{Background}

 In GLPK the {\it scaling} means a linear transformation applied to the

 constraint matrix to improve its numerical properties.\footnote{In many

 cases a proper scaling allows making the constraint matrix to be better

 conditioned, i.e. decreasing its condition number, that makes

 computations numerically more stable.}

 The main equality is the following:

 $$\widetilde{A}=RAS,\eqno(2.1)$$

 where $A=(a_{ij})$ is the original constraint matrix, $R=(r_{ii})>0$ is

 a diagonal matrix used to scale rows (constraints), $S=(s_{jj})>0$ is a

 diagonal matrix used to scale columns (variables), $\widetilde{A}$ is

 the scaled constraint matrix.

 From (2.1) it follows that in the {\it scaled} problem instance each

 original constraint coefficient $a_{ij}$ is replaced by corresponding

 scaled constraint coefficient:

 $$\widetilde{a}_{ij}=r_{ii}a_{ij}s_{jj}.\eqno(2.2)$$

 Note that the scaling is performed internally and therefore

 transparently to the user. This means that on API level the user always

 deal with unscaled data.

 Scale factors $r_{ii}$ and $s_{jj}$ can be set or changed at any time

 either directly by the application program in a problem specific way

 (with the routines \verb|glp_set_rii| and \verb|glp_set_sjj|), or by

 some API routines intended for automatic scaling.

 \subsection{glp\_set\_rii---set (change) row scale factor}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_rii(glp_prob *lp, int i, double rii);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_rii| sets (changes) the scale factor $r_{ii}$

 for $i$-th row of the specified problem object.

 \subsection{glp\_set\_sjj---set (change) column scale factor}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_sjj(glp_prob *lp, int j, double sjj);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_sjj| sets (changes) the scale factor $s_{jj}$

 for $j$-th column of the specified problem object.

 \subsection{glp\_get\_rii---retrieve row scale factor}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_rii(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_rii| returns current scale factor $r_{ii}$ for

 $i$-th row of the specified problem object.

 \subsection{glp\_get\_sjj---retrieve column scale factor}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_sjj(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_sjj| returns current scale factor $s_{jj}$ for

 $j$-th column of the specified problem object.

 \subsection{glp\_scale\_prob---scale problem data}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_scale_prob(glp_prob *lp, int flags);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_scale_prob| performs automatic scaling of problem

 data for the specified problem object.

 The parameter \verb|flags| specifies scaling options used by the

 routine. The options can be combined with the bitwise OR operator and

 may be the following:

 \begin{tabular}{@{}ll}

 \verb|GLP_SF_GM| & perform geometric mean scaling;\\

 \verb|GLP_SF_EQ| & perform equilibration scaling;\\

 \verb|GLP_SF_2N| & round scale factors to nearest power of two;\\

 \verb|GLP_SF_SKIP| & skip scaling, if the problem is well scaled.\\

 \end{tabular}

 The parameter \verb|flags| may be specified as \verb|GLP_SF_AUTO|, in

 which case the routine chooses the scaling options automatically.

 \subsection{glp\_unscale\_prob---unscale problem data}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_unscale_prob(glp_prob *lp);

 \end{verbatim}

 The routine \verb|glp_unscale_prob| performs unscaling of problem data

 for the specified problem object.

 ``Unscaling'' means replacing the current scaling matrices $R$ and $S$

 by unity matrices that cancels the scaling effect.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{LP basis constructing routines}

 \subsection{Background}

 To start the search the simplex method needs a valid initial basis. In

 GLPK the basis is completely defined by a set of {\it statuses} assigned

 to {\it all} (auxiliary and structural) variables, where the status may

 be one of the following:

 \begin{tabular}{@{}ll}

 \verb|GLP_BS| & basic variable;\\

 \verb|GLP_NL| & non-basic variable having active lower bound;\\

 \verb|GLP_NU| & non-basic variable having active upper bound;\\

 \verb|GLP_NF| & non-basic free variable;\\

 \verb|GLP_NS| & non-basic fixed variable.\\

 \end{tabular}

 The basis is {\it valid}, if the basis matrix, which is a matrix built

 of columns of the augmented constraint matrix $(I\:|-A)$ corresponding

 to basic variables, is non-singular. This, in particular, means that

 the number of basic variables must be the same as the number of rows in

 the problem object. (For more details see Section \ref{lpbasis}, page

 \pageref{lpbasis}.)

 Any initial basis may be constructed (or restored) with the API

 routines \verb|glp_set_row_stat| and \verb|glp_set_col_stat| by

 assigning appropriate statuses to auxiliary and structural variables.

 Another way to construct an initial basis is to use API routines like

 \verb|glp_adv_basis|, which implement so called

 {\it crashing}.\footnote{This term is from early linear programming

 systems and means a heuristic to construct a valid initial basis.} Note

 that on normal exit the simplex solver remains the basis valid, so in

 case of reoptimization there is no need to construct an initial basis

 from scratch.

 \subsection{glp\_set\_row\_stat---set (change) row status}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_row_stat(glp_prob *lp, int i, int stat);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_row_stat| sets (changes) the current status

 of \verb|i|-th row (auxiliary variable) as specified by the parameter

 \verb|stat|:

 \begin{tabular}{@{}lp{104.2mm}@{}}

 \verb|GLP_BS| & make the row basic (make the constraint inactive); \\

 \verb|GLP_NL| & make the row non-basic (make the constraint active); \\

 \end{tabular}

 \newpage

 \begin{tabular}{@{}lp{104.2mm}@{}}

 \verb|GLP_NU| & make the row non-basic and set it to the upper bound;

    if the row is not double-bounded, this status is equivalent to

    \verb|GLP_NL| (only in the case of this routine); \\

 \verb|GLP_NF| & the same as \verb|GLP_NL| (only in the case of this

    routine); \\

 \verb|GLP_NS| & the same as \verb|GLP_NL| (only in the case of this

    routine). \\

 \end{tabular}

 \subsection{glp\_set\_col\_stat---set (change) column status}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_col_stat(glp_prob *lp, int j, int stat);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_col_stat sets| (changes) the current status

 of \verb|j|-th column (structural variable) as specified by the

 parameter \verb|stat|:

 \begin{tabular}{@{}lp{104.2mm}@{}}

 \verb|GLP_BS| & make the column basic; \\

 \verb|GLP_NL| & make the column non-basic; \\

 \verb|GLP_NU| & make the column non-basic and set it to the upper

    bound; if the column is not double-bounded, this status is equivalent

    to \verb|GLP_NL| (only in the case of this routine); \\

 \verb|GLP_NF| & the same as \verb|GLP_NL| (only in the case of this

    routine); \\

 \verb|GLP_NS| & the same as \verb|GLP_NL| (only in the case of this

    routine).

 \end{tabular}

 \subsection{glp\_std\_basis---construct standard initial LP basis}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_std_basis(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_std_basis| constructs the ``standard'' (trivial)

 initial LP basis for the specified problem object.

 In the ``standard'' LP basis all auxiliary variables (rows) are basic,

 and all structural variables (columns) are non-basic (so the

 corresponding basis matrix is unity).

 \newpage

 \subsection{glp\_adv\_basis---construct advanced initial LP basis}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_adv_basis(glp_prob *lp, int flags);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_adv_basis| constructs an advanced initial LP

 basis for the specified problem object.

 The parameter \verb|flags| is reserved for use in the future and must

 be specified as zero.

 In order to construct the advanced initial LP basis the routine does

 the following:

 1) includes in the basis all non-fixed auxiliary variables;

 2) includes in the basis as many non-fixed structural variables as

 possible keeping the triangular form of the basis matrix;

 3) includes in the basis appropriate (fixed) auxiliary variables to

 complete the basis.

 As a result the initial LP basis has as few fixed variables as possible

 and the corresponding basis matrix is triangular.

 \subsection{glp\_cpx\_basis---construct Bixby's initial LP basis}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_cpx_basis(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_cpx_basis| constructs an initial basis for the

 specified problem object with the algorithm proposed by

 R.~Bixby.\footnote{Robert E. Bixby, ``Implementing the Simplex Method:

 The Initial Basis.'' ORSA Journal on Computing, Vol. 4, No. 3, 1992,

 pp. 267-84.}

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Simplex method routines}

 The {\it simplex method} is a well known efficient numerical procedure

 to solve LP problems.

 On each iteration the simplex method transforms the original system of

 equaility constraints (1.2) resolving them through different sets of

 variables to an equivalent system called {\it the simplex table} (or

 sometimes {\it the simplex tableau}), which has the following form:

 $$

 \begin{array}{r@{\:}c@{\:}r@{\:}c@{\:}r@{\:}c@{\:}r}

 z&=&d_1(x_N)_1&+&d_2(x_N)_2&+ \dots +&d_n(x_N)_n \\

 (x_B)_1&=&\xi_{11}(x_N)_1& +& \xi_{12}(x_N)_2& + \dots +&

    \xi_{1n}(x_N)_n \\

 (x_B)_2&=& \xi_{21}(x_N)_1& +& \xi_{22}(x_N)_2& + \dots +&

    \xi_{2n}(x_N)_n \\

 \multicolumn{7}{c}

 {.\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .} \\

 (x_B)_m&=&\xi_{m1}(x_N)_1& +& \xi_{m2}(x_N)_2& + \dots +&

    \xi_{mn}(x_N)_n \\

 \end{array} \eqno (2.3)

 $$

 where: $(x_B)_1, (x_B)_2, \dots, (x_B)_m$ are basic variables;

 $(x_N)_1, (x_N)_2, \dots, (x_N)_n$ are non-basic variables;

 $d_1, d_2, \dots, d_n$ are reduced costs;

 $\xi_{11}, \xi_{12}, \dots, \xi_{mn}$ are coefficients of the

 simplex table. (May note that the original LP problem (1.1)---(1.3) also

 has the form of a simplex table, where all equalities are resolved

 through auxiliary variables.)

 From the linear programming theory it is known that if an optimal

 solution of the LP problem (1.1)---(1.3) exists, it can always be

 written in the form (2.3), where non-basic variables are set on their

 bounds while values of the objective function and basic variables are

 determined by the corresponding equalities of the simplex table.

 A set of values of all basic and non-basic variables determined by the

 simplex table is called {\it basic solution}. If all basic variables are

 within their bounds, the basic solution is called {\it (primal)

 feasible}, otherwise it is called {\it (primal) infeasible}. A feasible

 basic solution, which provides a smallest (in case of minimization) or

 a largest (in case of maximization) value of the objective function is

 called {\it optimal}. Therefore, for solving LP problem the simplex

 method tries to find its optimal basic solution.

 Primal feasibility of some basic solution may be stated by simple

 checking if all basic variables are within their bounds. Basic solution

 is optimal if additionally the following optimality conditions are

 satisfied for all non-basic variables:

 \begin{center}

 \begin{tabular}{lcc}

 Status of $(x_N)_j$ & Minimization & Maximization \\

 \hline

 $(x_N)_j$ is free & $d_j = 0$ & $d_j = 0$ \\

 $(x_N)_j$ is on its lower bound & $d_j \geq 0$ & $d_j \leq 0$ \\

 $(x_N)_j$ is on its upper bound & $d_j \leq 0$ & $d_j \geq 0$ \\

 \end{tabular}

 \end{center}

 In other words, basic solution is optimal if there is no non-basic

 variable, which changing in the feasible direction (i.e. increasing if

 it is free or on its lower bound, or decreasing if it is free or on its

 upper bound) can improve (i.e. decrease in case of minimization or

 increase in case of maximization) the objective function.

 If all non-basic variables satisfy to the optimality conditions shown

 above (independently on whether basic variables are within their bounds

 or not), the basic solution is called {\it dual feasible}, otherwise it

 is called {\it dual infeasible}.

 It may happen that some LP problem has no primal feasible solution due

 to incorrect formulation---this means that its constraints conflict

 with each other. It also may happen that some LP problem has unbounded

 solution again due to incorrect formulation---this means that some

 non-basic variable can improve the objective function, i.e. the

 optimality conditions are violated, and at the same time this variable

 can infinitely change in the feasible direction meeting no resistance

 from basic variables. (May note that in the latter case the LP problem

 has no dual feasible solution.)

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \subsection{glp\_simplex---solve LP problem with the primal or dual

 simplex method}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_simplex(glp_prob *lp, const glp_smcp *parm);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_simplex| is a driver to the LP solver based on

 the simplex method. This routine retrieves problem data from the

 specified problem object, calls the solver to solve the problem

 instance, and stores results of computations back into the problem

 object.

 The simplex solver has a set of control parameters. Values of the

 control parameters can be passed in the structure \verb|glp_smcp|,

 which the parameter \verb|parm| points to. For detailed description of

 this structure see paragraph ``Control parameters'' below.

 Before specifying some control parameters the application program

 should initialize the structure \verb|glp_smcp| by default values of

 all control parameters using the routine \verb|glp_init_smcp| (see the

 next subsection). This is needed for backward compatibility, because in

 the future there may appear new members in the structure

 \verb|glp_smcp|.

 The parameter \verb|parm| can be specified as \verb|NULL|, in which

 case the solver uses default settings.

 \subsubsection*{Returns}

 \def\arraystretch{1}

 \begin{tabular}{@{}p{25mm}p{97.3mm}@{}}

 0 & The LP problem instance has been successfully solved. (This code

 does {\it not} necessarily mean that the solver has found optimal

 solution. It only means that the solution process was successful.) \\

 \verb|GLP_EBADB| & Unable to start the search, because the initial basis

 specified in the problem object is invalid---the number of basic

 (auxiliary and structural) variables is not the same as the number of

 rows in the problem object.\\

 \verb|GLP_ESING| & Unable to start the search, because the basis matrix

 corresponding to the initial basis is singular within the working

 precision.\\

 \verb|GLP_ECOND| & Unable to start the search, because the basis matrix

 corresponding to the initial basis is ill-conditioned, i.e. its

 condition number is too large.\\

 \verb|GLP_EBOUND| & Unable to start the search, because some

 double-bounded (auxiliary or structural) variables have incorrect

 bounds.\\

 \verb|GLP_EFAIL| & The search was prematurely terminated due to the

 solver failure.\\

 \verb|GLP_EOBJLL| & The search was prematurely terminated, because the

 objective function being maximized has reached its lower limit and

 continues decreasing (the dual simplex only).\\

 \verb|GLP_EOBJUL| & The search was prematurely terminated, because the

 objective function being minimized has reached its upper limit and

 continues increasing (the dual simplex only).\\

 \verb|GLP_EITLIM| & The search was prematurely terminated, because the

 simplex iteration limit has been exceeded.\\

 \verb|GLP_ETMLIM| & The search was prematurely terminated, because the

 time limit has been exceeded.\\

 \verb|GLP_ENOPFS| & The LP problem instance has no primal feasible

 solution (only if the LP presolver is used).\\

 \verb|GLP_ENODFS| & The LP problem instance has no dual feasible

 solution (only if the LP presolver is used).\\

 \end{tabular}

 \subsubsection*{Built-in LP presolver}

 The simplex solver has {\it built-in LP presolver}. It is a subprogram

 that transforms the original LP problem specified in the problem object

 to an equivalent LP problem, which may be easier for solving with the

 simplex method than the original one. This is attained mainly due to

 reducing the problem size and improving its numeric properties (for

 example, by removing some inactive constraints or by fixing some

 non-basic variables). Once the transformed LP problem has been solved,

 the presolver transforms its basic solution back to the corresponding

 basic solution of the original problem.

 Presolving is an optional feature of the routine \verb|glp_simplex|,

 and by default it is disabled. In order to enable the LP presolver the

 control parameter \verb|presolve| should be set to \verb|GLP_ON| (see

 paragraph ``Control parameters'' below). Presolving may be used when

 the problem instance is solved for the first time. However, on

 performing re-optimization the presolver should be disabled.

 The presolving procedure is transparent to the API user in the sense

 that all necessary processing is performed internally, and a basic

 solution of the original problem recovered by the presolver is the same

 as if it were computed directly, i.e. without presolving.

 Note that the presolver is able to recover only optimal solutions. If

 a computed solution is infeasible or non-optimal, the corresponding

 solution of the original problem cannot be recovered and therefore

 remains undefined. If you need to know a basic solution even if it is

 infeasible or non-optimal, the presolver should be disabled.

 \subsubsection*{Terminal output}

 Solving large problem instances may take a long time, so the solver

 reports some information about the current basic solution, which is sent

 to the terminal. This information has the following format:

 \begin{verbatim}

 nnn:  obj = xxx  infeas = yyy (ddd)

 \end{verbatim}

 \noindent

 where: `\verb|nnn|' is the iteration number, `\verb|xxx|' is the

 current value of the objective function (it is is unscaled and has

 correct sign); `\verb|yyy|' is the current sum of primal or dual

 infeasibilities (it is scaled and therefore may be used only for visual

 estimating), `\verb|ddd|' is the current number of fixed basic

 variables.

 The symbol preceding the iteration number indicates which phase of the

 simplex method is in effect:

 {\it Blank} means that the solver is searching for primal feasible

 solution using the primal simplex or for dual feasible solution using

 the dual simplex;

 {\it Asterisk} (\verb|*|) means that the solver is searching for

 optimal solution using the primal simplex;

 {\it Vertical dash} (\verb/|/) means that the solver is searching for

 optimal solution using the dual simplex.

 \subsubsection*{Control parameters}

 This paragraph describes all control parameters currently used in the

 simplex solver. Symbolic names of control parameters are names of

 corresponding members in the structure \verb|glp_smcp|.

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int msg\_lev} (default: {\tt GLP\_MSG\_ALL})}

 \\

 &Message level for terminal output:\\

 &\verb|GLP_MSG_OFF|---no output;\\

 &\verb|GLP_MSG_ERR|---error and warning messages only;\\

 &\verb|GLP_MSG_ON |---normal output;\\

 &\verb|GLP_MSG_ALL|---full output (including informational messages).

 \\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int meth} (default: {\tt GLP\_PRIMAL})}

 \\

 &Simplex method option:\\

 &\verb|GLP_PRIMAL|---use two-phase primal simplex;\\

 &\verb|GLP_DUAL  |---use two-phase dual simplex;\\

 &\verb|GLP_DUALP |---use two-phase dual simplex, and if it fails,

 switch to the\\

 &\verb|            |$\:$ primal simplex.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int pricing} (default: {\tt GLP\_PT\_PSE})}

 \\

 &Pricing technique:\\

 &\verb|GLP_PT_STD|---standard (textbook);\\

 &\verb|GLP_PT_PSE|---projected steepest edge.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int r\_test} (default: {\tt GLP\_RT\_HAR})}

 \\

 &Ratio test technique:\\

 &\verb|GLP_RT_STD|---standard (textbook);\\

 &\verb|GLP_RT_HAR|---Harris' two-pass ratio test.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt double tol\_bnd} (default: {\tt 1e-7})}

 \\

 &Tolerance used to check if the basic solution is primal feasible.

 (Do not change this parameter without detailed understanding its

 purpose.)\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt double tol\_dj} (default: {\tt 1e-7})}

 \\

 &Tolerance used to check if the basic solution is dual feasible.

 (Do not change this parameter without detailed understanding its

 purpose.)\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt double tol\_piv} (default: {\tt 1e-10})}

 \\

 &Tolerance used to choose eligble pivotal elements of the simplex table.

 (Do not change this parameter without detailed understanding its

 purpose.)\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt double obj\_ll} (default: {\tt -DBL\_MAX})}

 \\

 &Lower limit of the objective function. If the objective function

 reaches this limit and continues decreasing, the solver terminates the

 search. (Used in the dual simplex only.)\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt double obj\_ul} (default: {\tt +DBL\_MAX})}

 \\

 &Upper limit of the objective function. If the objective function

 reaches this limit and continues increasing, the solver terminates the

 search. (Used in the dual simplex only.)\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int it\_lim} (default: {\tt INT\_MAX})}

 \\

 &Simplex iteration limit.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int tm\_lim} (default: {\tt INT\_MAX})}

 \\

 &Searching time limit, in milliseconds.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int out\_frq} (default: {\tt 500})}

 \\

 &Output frequency, in iterations. This parameter specifies how

 frequently the solver sends information about the solution process to

 the terminal.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int out\_dly} (default: {\tt 0})}

 \\

 &Output delay, in milliseconds. This parameter specifies how long the

 solver should delay sending information about the solution process to

 the terminal.\\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int presolve} (default: {\tt GLP\_OFF})}

 \\

 &LP presolver option:\\

 &\verb|GLP_ON |---enable using the LP presolver;\\

 &\verb|GLP_OFF|---disable using the LP presolver.\\

 \end{tabular}

 \subsubsection*{Example 1}

 The following main program reads LP problem instance in fixed MPS

 format from file \verb|25fv47.mps|,\footnote{This instance in fixed MPS

 format can be found in the Netlib LP collection; see

 {\tt ftp://ftp.netlib.org/lp/data/}.} constructs an advanced initial

 basis, solves the instance with the primal simplex method (by default),

 and writes the solution to file \verb|25fv47.txt|.

 \newpage

 \begin{footnotesize}

 \begin{verbatim}

 /* spxsamp1.c */

 #include <stdio.h>

 #include <stdlib.h>

 #include <glpk.h>

 int main(void)

 {     glp_prob *P;

       P = glp_create_prob();

       glp_read_mps(P, GLP_MPS_DECK, NULL, "25fv47.mps");

       glp_adv_basis(P, 0);

       glp_simplex(P, NULL);

       glp_print_sol(P, "25fv47.txt");

       glp_delete_prob(P);

       return 0;

 }

 /* eof */

 \end{verbatim}

 \end{footnotesize}

 \noindent

 Below here is shown the terminal output from this example program.

 \begin{footnotesize}

 \begin{verbatim}

 Reading problem data from `25fv47.mps'...

 Problem: 25FV47

 Objective: R0000

 822 rows, 1571 columns, 11127 non-zeros

 6919 records were read

 Crashing...

 Size of triangular part = 799

       0: obj =   1.627307307e+04  infeas =  5.194e+04 (23)

     200: obj =   1.474901610e+04  infeas =  1.233e+04 (19)

     400: obj =   1.343909995e+04  infeas =  3.648e+03 (13)

     600: obj =   1.756052217e+04  infeas =  4.179e+02 (7)

 *   775: obj =   1.789251591e+04  infeas =  4.982e-14 (1)

 *   800: obj =   1.663354510e+04  infeas =  2.857e-14 (1)

 *  1000: obj =   1.024935068e+04  infeas =  1.958e-12 (1)

 *  1200: obj =   7.860174791e+03  infeas =  2.810e-29 (1)

 *  1400: obj =   6.642378184e+03  infeas =  2.036e-16 (1)

 *  1600: obj =   6.037014568e+03  infeas =  0.000e+00 (1)

 *  1800: obj =   5.662171307e+03  infeas =  6.447e-15 (1)

 *  2000: obj =   5.528146165e+03  infeas =  9.764e-13 (1)

 *  2125: obj =   5.501845888e+03  infeas =  0.000e+00 (1)

 OPTIMAL SOLUTION FOUND

 Writing basic solution to `25fv47.txt'...

 \end{verbatim}

 \end{footnotesize}

 \newpage

 \subsubsection*{Example 2}

 The following main program solves the same LP problem instance as in

 Example 1 above, however, it uses the dual simplex method, which starts

 from the standard initial basis.

 \begin{footnotesize}

 \begin{verbatim}

 /* spxsamp2.c */

 #include <stdio.h>

 #include <stdlib.h>

 #include <glpk.h>

 int main(void)

 {     glp_prob *P;

       glp_smcp parm;

       P = glp_create_prob();

       glp_read_mps(P, GLP_MPS_DECK, NULL, "25fv47.mps");

       glp_init_smcp(&parm);

       parm.meth = GLP_DUAL;

       glp_simplex(P, &parm);

       glp_print_sol(P, "25fv47.txt");

       glp_delete_prob(P);

       return 0;

 }

 /* eof */

 \end{verbatim}

 \end{footnotesize}

 \noindent

 Below here is shown the terminal output from this example program.

 \begin{footnotesize}

 \begin{verbatim}

 Reading problem data from `25fv47.mps'...

 Problem: 25FV47

 Objective: R0000

 822 rows, 1571 columns, 11127 non-zeros

 6919 records were read

       0:                          infeas =  1.223e+03 (516)

     200:                          infeas =  7.000e+00 (471)

     240:                          infeas =  1.106e-14 (461)

 |   400: obj =  -5.394267152e+03  infeas =  5.571e-16 (391)

 |   600: obj =  -4.586395752e+03  infeas =  1.389e-15 (340)

 |   800: obj =  -4.158268146e+03  infeas =  1.640e-15 (264)

 |  1000: obj =  -3.725320045e+03  infeas =  5.181e-15 (245)

 |  1200: obj =  -3.104802163e+03  infeas =  1.019e-14 (210)

 |  1400: obj =  -2.584190499e+03  infeas =  8.865e-15 (178)

 |  1600: obj =  -2.073852927e+03  infeas =  7.867e-15 (142)

 |  1800: obj =  -1.164037407e+03  infeas =  8.792e-15 (109)

 |  2000: obj =  -4.370590250e+02  infeas =  2.591e-14 (85)

 |  2200: obj =   1.068240144e+03  infeas =  1.025e-13 (70)

 |  2400: obj =   1.607481126e+03  infeas =  3.272e-14 (67)

 |  2600: obj =   3.038230551e+03  infeas =  4.850e-14 (52)

 |  2800: obj =   4.316238187e+03  infeas =  2.622e-14 (36)

 |  3000: obj =   5.443842629e+03  infeas =  3.976e-15 (11)

 |  3060: obj =   5.501845888e+03  infeas =  8.806e-15 (2)

 OPTIMAL SOLUTION FOUND

 Writing basic solution to `25fv47.txt'...

 \end{verbatim}

 \end{footnotesize}

 \subsection{glp\_exact---solve LP problem in exact arithmetic}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_exact(glp_prob *lp, const glp_smcp *parm);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_exact| is a tentative implementation of the

 primal two-phase simplex method based on exact (rational) arithmetic.

 It is similar to the routine \verb|glp_simplex|, however, for all

 internal computations it uses arithmetic of rational numbers, which is

 exact in mathematical sense, i.e. free of round-off errors unlike

 floating-point arithmetic.

 Note that the routine \verb|glp_exact| uses only two control parameters

 passed in the structure \verb|glp_smcp|, namely, \verb|it_lim| and

 \verb|tm_lim|.

 \subsubsection*{Returns}

 \def\arraystretch{1}

 \begin{tabular}{@{}p{25mm}p{97.3mm}@{}}

 0 & The LP problem instance has been successfully solved. (This code

 does {\it not} necessarily mean that the solver has found optimal

 solution. It only means that the solution process was successful.) \\

 \verb|GLP_EBADB| & Unable to start the search, because the initial basis

 specified in the problem object is invalid---the number of basic

 (auxiliary and structural) variables is not the same as the number of

 rows in the problem object.\\

 \verb|GLP_ESING| & Unable to start the search, because the basis matrix

 corresponding to the initial basis is exactly singular.\\

 \verb|GLP_EBOUND| & Unable to start the search, because some

 double-bounded (auxiliary or structural) variables have incorrect

 bounds.\\

 \verb|GLP_EFAIL| & The problem instance has no rows/columns.\\

 \verb|GLP_EITLIM| & The search was prematurely terminated, because the

 simplex iteration limit has been exceeded.\\

 \verb|GLP_ETMLIM| & The search was prematurely terminated, because the

 time limit has been exceeded.\\

 \end{tabular}

 \subsubsection*{Comments}

 Computations in exact arithmetic are very time consuming, so solving

 LP problem with the routine \verb|glp_exact| from the very beginning is

 not a good idea. It is much better at first to find an optimal basis

 with the routine \verb|glp_simplex| and only then to call

 \verb|glp_exact|, in which case only a few simplex iterations need to

 be performed in exact arithmetic.

 \subsection{glp\_init\_smcp---initialize simplex solver control

 parameters}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_init_smcp(glp_smcp *parm);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_init_smcp| initializes control parameters, which

 are used by the simplex solver, with default values.

 Default values of the control parameters are stored in a \verb|glp_smcp|

 structure, which the parameter \verb|parm| points to.

 \subsection{glp\_get\_status---determine generic status of basic

 solution}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_status(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_status| reports the generic status of the

 current basic solution for the specified problem object as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_OPT|    & solution is optimal; \\

 \verb|GLP_FEAS|   & solution is feasible; \\

 \verb|GLP_INFEAS| & solution is infeasible; \\

 \verb|GLP_NOFEAS| & problem has no feasible solution; \\

 \verb|GLP_UNBND|  & problem has unbounded solution; \\

 \verb|GLP_UNDEF|  & solution is undefined. \\

 \end{tabular}

 More detailed information about the status of basic solution can be

 retrieved with the routines \verb|glp_get_prim_stat| and

 \verb|glp_get_dual_stat|.

 \newpage

 \subsection{glp\_get\_prim\_stat---retrieve status of primal basic

 solution}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_prim_stat(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_prim_stat| reports the status of the primal

 basic solution for the specified problem object as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_UNDEF|  & primal solution is undefined; \\

 \verb|GLP_FEAS|   & primal solution is feasible; \\

 \verb|GLP_INFEAS| & primal solution is infeasible; \\

 \verb|GLP_NOFEAS| & no primal feasible solution exists. \\

 \end{tabular}

 \subsection{glp\_get\_dual\_stat---retrieve status of dual basic

 solution}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_dual_stat(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_dual_stat| reports the status of the dual

 basic solution for the specified problem object as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_UNDEF|  & dual solution is undefined; \\

 \verb|GLP_FEAS|   & dual solution is feasible; \\

 \verb|GLP_INFEAS| & dual solution is infeasible; \\

 \verb|GLP_NOFEAS| & no dual feasible solution exists. \\

 \end{tabular}

 \subsection{glp\_get\_obj\_val---retrieve objective value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_obj_val(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_obj_val| returns current value of the

 objective function.

 \subsection{glp\_get\_row\_stat---retrieve row status}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_row_stat(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_stat| returns current status assigned to

 the auxiliary variable associated with \verb|i|-th row as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_BS| & basic variable; \\

 \verb|GLP_NL| & non-basic variable on its lower bound; \\

 \verb|GLP_NU| & non-basic variable on its upper bound; \\

 \verb|GLP_NF| & non-basic free (unbounded) variable; \\

 \verb|GLP_NS| & non-basic fixed variable. \\

 \end{tabular}

 \subsection{glp\_get\_row\_prim---retrieve row primal value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_row_prim(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_prim| returns primal value of the

 auxiliary variable associated with \verb|i|-th row.

 \subsection{glp\_get\_row\_dual---retrieve row dual value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_row_dual(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_row_dual| returns dual value (i.e. reduced

 cost) of the auxiliary variable associated with \verb|i|-th row.

 \newpage

 \subsection{glp\_get\_col\_stat---retrieve column status}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_col_stat(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_stat| returns current status assigned to

 the structural variable associated with \verb|j|-th column as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_BS| & basic variable; \\

 \verb|GLP_NL| & non-basic variable on its lower bound; \\

 \verb|GLP_NU| & non-basic variable on its upper bound; \\

 \verb|GLP_NF| & non-basic free (unbounded) variable; \\

 \verb|GLP_NS| & non-basic fixed variable. \\

 \end{tabular}

 \subsection{glp\_get\_col\_prim---retrieve column primal value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_col_prim(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_prim| returns primal value of the

 structural variable associated with \verb|j|-th column.

 \subsection{glp\_get\_col\_dual---retrieve column dual value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_get_col_dual(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_dual| returns dual value (i.e. reduced

 cost) of the structural variable associated with \verb|j|-th column.

 \newpage

 \subsection{glp\_get\_unbnd\_ray---determine variable causing\\

 unboundedness}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_unbnd_ray(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_unbnd_ray| returns the number $k$ of

 a variable, which causes primal or dual unboundedness.

 If $1\leq k\leq m$, it is $k$-th auxiliary variable, and if

 $m+1\leq k\leq m+n$, it is $(k-m)$-th structural variable, where $m$ is

 the number of rows, $n$ is the number of columns in the problem object.

 If such variable is not defined, the routine returns 0.

 \subsubsection*{Comments}

 If it is not exactly known which version of the simplex solver

 detected unboundedness, i.e. whether the unboundedness is primal or

 dual, it is sufficient to check the status of the variable

 with the routine \verb|glp_get_row_stat| or \verb|glp_get_col_stat|.

 If the variable is non-basic, the unboundedness is primal, otherwise,

 if the variable is basic, the unboundedness is dual (the latter case

 means that the problem has no primal feasible dolution).

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Interior-point method routines}

 {\it Interior-point methods} (also known as {\it barrier methods}) are

 more modern and powerful numerical methods for large-scale linear

 programming. Such methods are especially efficient for very sparse LP

 problems and allow solving such problems much faster than the simplex

 method.

 In brief, the GLPK interior-point solver works as follows.

 At first, the solver transforms the original LP to a {\it working} LP

 in the standard format:

 \medskip

 \noindent

 \hspace{.5in} minimize

 $$z = c_1x_{m+1} + c_2x_{m+2} + \dots + c_nx_{m+n} + c_0 \eqno (2.4)$$

 \hspace{.5in} subject to linear constraints

 $$

 \begin{array}{r@{\:}c@{\:}r@{\:}c@{\:}r@{\:}c@{\:}l}

 a_{11}x_{m+1}&+&a_{12}x_{m+2}&+ \dots +&a_{1n}x_{m+n}&=&b_1 \\

 a_{21}x_{m+1}&+&a_{22}x_{m+2}&+ \dots +&a_{2n}x_{m+n}&=&b_2 \\

 \multicolumn{7}{c}

 {.\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .\ \ .} \\

 a_{m1}x_{m+1}&+&a_{m2}x_{m+2}&+ \dots +&a_{mn}x_{m+n}&=&b_m \\

 \end{array} \eqno (2.5)

 $$

 \hspace{.5in} and non-negative variables

 $$x_1\geq 0,\ \ x_2\geq 0,\ \ \dots,\ \ x_n\geq 0 \eqno(2.6)$$

 where: $z$ is the objective function; $x_1$, \dots, $x_n$ are variables;

 $c_1$, \dots, $c_n$ are objective coefficients; $c_0$ is a constant term

 of the objective function;\linebreak $a_{11}$, \dots, $a_{mn}$ are

 constraint coefficients; $b_1$, \dots, $b_m$ are right-hand sides.

 Using vector and matrix notations the working LP (2.4)---(2.6) can be

 written as follows:

 $$z=c^Tx+c_0\ \rightarrow\ \min,\eqno(2.7)$$

 $$Ax=b,\eqno(2.8)$$

 $$x\geq 0,\eqno(2.9)$$

 where: $x=(x_j)$ is $n$-vector of variables, $c=(c_j)$ is $n$-vector of

 objective coefficients, $A=(a_{ij})$ is $m\times n$-matrix of

 constraint coefficients, and $b=(b_i)$ is $m$-vector of right-hand

 sides.

 Karush--Kuhn--Tucker optimality conditions for LP (2.7)---(2.9) are the

 following:

 \newpage

 $$Ax=b,\eqno(2.10)$$

 $$A^T\pi+\lambda=c,\eqno(2.11)$$

 $$\lambda^Tx=0,\eqno(2.12)$$

 $$x\geq 0,\ \ \lambda\geq 0,\eqno(2.13)$$

 where: $\pi$ is $m$-vector of Lagrange multipliers (dual variables) for

 equality constraints (2.8), $\lambda$ is $n$-vector of Lagrange

 multipliers (dual variables) for non-negativity constraints (2.9),

 (2.10) is the primal feasibility condition, (2.11) is the dual

 feasibility condition, (2.12) is the primal-dual complementarity

 condition, and (2.13) is the non-negativity conditions.

 The main idea of the primal-dual interior-point method is based on

 finding a point in the primal-dual space (i.e. in the space of all

 primal and dual variables $x$, $\pi$, and $\lambda$), which satisfies

 to all optimality conditions (2.10)---(2.13). Obviously, $x$-component

 of such point then provides an optimal solution to the working LP

 (2.7)---(2.9).

 To find the optimal point $(x^*,\pi^*,\lambda^*)$ the interior-point

 method attempts to solve the system of equations (2.10)---(2.12), which

 is closed in the sense that the number of variables $x_j$, $\pi_i$, and

 $\lambda_j$ and the number equations are the same and equal to $m+2n$.

 Due to condition (2.12) this system of equations is non-linear, so it

 can be solved with a version of {\it Newton's method} provided with

 additional rules to keep the current point within the positive orthant

 as required by the non-negativity conditions (2.13).

 Finally, once the optimal point $(x^*,\pi^*,\lambda^*)$ has been found,

 the solver performs inverse transformations to recover corresponding

 solution to the original LP passed to the solver from the application

 program.

 \subsection{glp\_interior---solve LP problem with the interior-point

 method}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_interior(glp_prob *P, const glp_iptcp *parm);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_interior| is a driver to the LP solver based on

 the primal-dual interior-point method. This routine retrieves problem

 data from the specified problem object, calls the solver to solve the

 problem instance, and stores results of computations back into the

 problem object.

 The interior-point solver has a set of control parameters. Values of

 the control parameters can be passed in the structure \verb|glp_iptcp|,

 which the parameter \verb|parm| points to. For detailed description of

 this structure see paragraph ``Control parameters'' below. Before

 specifying some control parameters the application program should

 initialize the structure \verb|glp_iptcp| by default values of all

 control parameters using the routine \verb|glp_init_iptcp| (see the

 next subsection). This is needed for backward compatibility, because in

 the future there may appear new members in the structure

 \verb|glp_iptcp|.

 The parameter \verb|parm| can be specified as \verb|NULL|, in which

 case the solver uses default settings.

 \subsubsection*{Returns}

 \def\arraystretch{1}

 \begin{tabular}{@{}p{25mm}p{97.3mm}@{}}

 0 & The LP problem instance has been successfully solved. (This code

 does {\it not} necessarily mean that the solver has found optimal

 solution. It only means that the solution process was successful.) \\

 \verb|GLP_EFAIL| & The problem has no rows/columns.\\

 \verb|GLP_ENOCVG| & Very slow convergence or divergence.\\

 \verb|GLP_EITLIM| & Iteration limit exceeded.\\

 \verb|GLP_EINSTAB| & Numerical instability on solving Newtonian

 system.\\

 \end{tabular}

 \subsubsection*{Comments}

 The routine \verb|glp_interior| implements an easy version of

 the primal-dual interior-point method based on Mehrotra's

 technique.\footnote{S. Mehrotra. On the implementation of a primal-dual

 interior point method. SIAM J. on Optim., 2(4), pp. 575-601, 1992.}

 Note that currently the GLPK interior-point solver does not include

 many important features, in particular:

 $\bullet$ it is not able to process dense columns. Thus, if the

 constraint matrix of the LP problem has dense columns, the solving

 process may be inefficient;

 $\bullet$ it has no features against numerical instability. For some

 LP problems premature termination may happen if the matrix $ADA^T$

 becomes singular or ill-conditioned;

 $\bullet$ it is not able to identify the optimal basis, which

 corresponds to the interior-point solution found.

 \newpage

 \subsubsection*{Terminal output}

 Solving large LP problems may take a long time, so the solver reports

 some information about every interior-point iteration,\footnote{Unlike

 the simplex method the interior point method usually needs 30---50

 iterations (independently on the problem size) in order to find an

 optimal solution.} which is sent to the terminal. This information has

 the following format:

 \begin{verbatim}

 nnn: obj = fff; rpi = ppp; rdi = ddd; gap = ggg

 \end{verbatim}

 \noindent where: \verb|nnn| is iteration number, \verb|fff| is the

 current value of the objective function (in the case of maximization it

 has wrong sign), \verb|ppp| is the current relative primal

 infeasibility (cf. (2.10)):

 $$\frac{\|Ax^{(k)}-b\|}{1+\|b\|},\eqno(2.14)$$

 \verb|ddd| is the current relative dual infeasibility (cf. (2.11)):

 $$\frac{\|A^T\pi^{(k)}+\lambda^{(k)}-c\|}{1+\|c\|},\eqno(2.15)$$

 \verb|ggg| is the current primal-dual gap (cf. (2.12)):

 $$\frac{|c^Tx^{(k)}-b^T\pi^{(k)}|}{1+|c^Tx^{(k)}|},\eqno(2.16)$$

 and $[x^{(k)},\pi^{(k)},\lambda^{(k)}]$ is the current point on $k$-th

 iteration, $k=0,1,2,\dots$\ . Note that all solution components are

 internally scaled, so information sent to the terminal is suitable only

 for visual inspection.

 \subsubsection*{Control parameters}

 This paragraph describes all control parameters currently used in the

 interior-point solver. Symbolic names of control parameters are names of

 corresponding members in the structure \verb|glp_iptcp|.

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int msg\_lev} (default: {\tt GLP\_MSG\_ALL})}

 \\

 &Message level for terminal output:\\

 &\verb|GLP_MSG_OFF|---no output;\\

 &\verb|GLP_MSG_ERR|---error and warning messages only;\\

 &\verb|GLP_MSG_ON |---normal output;\\

 &\verb|GLP_MSG_ALL|---full output (including informational messages).

 \\

 \end{tabular}

 \medskip

 \noindent\begin{tabular}{@{}p{17pt}@{}p{120.5mm}@{}}

 \multicolumn{2}{@{}l}{{\tt int ord\_alg} (default: {\tt GLP\_ORD\_AMD})}

 \\

 &Ordering algorithm used prior to Cholesky factorization:\\

 &\verb|GLP_ORD_NONE  |---use natural (original) ordering;\\

 &\verb|GLP_ORD_QMD   |---quotient minimum degree (QMD);\\

 &\verb|GLP_ORD_AMD   |---approximate minimum degree (AMD);\\

 &\verb|GLP_ORD_SYMAMD|---approximate minimum degree (SYMAMD).\\

 \end{tabular}

 \subsubsection*{Example}

 The following main program reads LP problem instance in fixed MPS

 format from file \verb|25fv47.mps|,\footnote{This instance in fixed MPS

 format can be found in the Netlib LP collection; see

 {\tt ftp://ftp.netlib.org/lp/data/}.} solves it with the interior-point

 solver, and writes the solution to file \verb|25fv47.txt|.

 \begin{footnotesize}

 \begin{verbatim}

 /* iptsamp.c */

 #include <stdio.h>

 #include <stdlib.h>

 #include <glpk.h>

 int main(void)

 {     glp_prob *P;

       P = glp_create_prob();

       glp_read_mps(P, GLP_MPS_DECK, NULL, "25fv47.mps");

       glp_interior(P, NULL);

       glp_print_ipt(P, "25fv47.txt");

       glp_delete_prob(P);

       return 0;

 }

 /* eof */

 \end{verbatim}

 \end{footnotesize}

 \noindent

 Below here is shown the terminal output from this example program.

 \begin{footnotesize}

 \begin{verbatim}

 Reading problem data from `25fv47.mps'...

 Problem: 25FV47

 Objective: R0000

 822 rows, 1571 columns, 11127 non-zeros

 6919 records were read

 Original LP has 822 row(s), 1571 column(s), and 11127 non-zero(s)

 Working LP has 821 row(s), 1876 column(s), and 10705 non-zero(s)

 Matrix A has 10705 non-zeros

 Matrix S = A*A' has 11895 non-zeros (upper triangle)

 Minimal degree ordering...

 Computing Cholesky factorization S = L'*L...

 Matrix L has 35411 non-zeros

 Guessing initial point...

 Optimization begins...

   0: obj =   1.823377629e+05; rpi =  1.3e+01; rdi =  1.4e+01; gap =  9.3e-01

   1: obj =   9.260045192e+04; rpi =  5.3e+00; rdi =  5.6e+00; gap =  6.8e+00

   2: obj =   3.596999742e+04; rpi =  1.5e+00; rdi =  1.2e+00; gap =  1.8e+01

   3: obj =   1.989627568e+04; rpi =  4.7e-01; rdi =  3.0e-01; gap =  1.9e+01

   4: obj =   1.430215557e+04; rpi =  1.1e-01; rdi =  8.6e-02; gap =  1.4e+01

   5: obj =   1.155716505e+04; rpi =  2.3e-02; rdi =  2.4e-02; gap =  6.8e+00

   6: obj =   9.660273208e+03; rpi =  6.7e-03; rdi =  4.6e-03; gap =  3.9e+00

   7: obj =   8.694348283e+03; rpi =  3.7e-03; rdi =  1.7e-03; gap =  2.0e+00

   8: obj =   8.019543639e+03; rpi =  2.4e-03; rdi =  3.9e-04; gap =  1.0e+00

   9: obj =   7.122676293e+03; rpi =  1.2e-03; rdi =  1.5e-04; gap =  6.6e-01

  10: obj =   6.514534518e+03; rpi =  6.1e-04; rdi =  4.3e-05; gap =  4.1e-01

  11: obj =   6.361572203e+03; rpi =  4.8e-04; rdi =  2.2e-05; gap =  3.0e-01

  12: obj =   6.203355508e+03; rpi =  3.2e-04; rdi =  1.7e-05; gap =  2.6e-01

  13: obj =   6.032943411e+03; rpi =  2.0e-04; rdi =  9.3e-06; gap =  2.1e-01

  14: obj =   5.796553021e+03; rpi =  9.8e-05; rdi =  3.2e-06; gap =  1.0e-01

  15: obj =   5.667032431e+03; rpi =  4.4e-05; rdi =  1.1e-06; gap =  5.6e-02

  16: obj =   5.613911867e+03; rpi =  2.5e-05; rdi =  4.1e-07; gap =  3.5e-02

  17: obj =   5.560572626e+03; rpi =  9.9e-06; rdi =  2.3e-07; gap =  2.1e-02

  18: obj =   5.537276001e+03; rpi =  5.5e-06; rdi =  8.4e-08; gap =  1.1e-02

  19: obj =   5.522746942e+03; rpi =  2.2e-06; rdi =  4.0e-08; gap =  6.7e-03

  20: obj =   5.509956679e+03; rpi =  7.5e-07; rdi =  1.8e-08; gap =  2.9e-03

  21: obj =   5.504571733e+03; rpi =  1.6e-07; rdi =  5.8e-09; gap =  1.1e-03

  22: obj =   5.502576367e+03; rpi =  3.4e-08; rdi =  1.0e-09; gap =  2.5e-04

  23: obj =   5.502057119e+03; rpi =  8.1e-09; rdi =  3.0e-10; gap =  7.7e-05

  24: obj =   5.501885996e+03; rpi =  9.4e-10; rdi =  1.2e-10; gap =  2.4e-05

  25: obj =   5.501852464e+03; rpi =  1.4e-10; rdi =  1.2e-11; gap =  3.0e-06

  26: obj =   5.501846549e+03; rpi =  1.4e-11; rdi =  1.2e-12; gap =  3.0e-07

  27: obj =   5.501845954e+03; rpi =  1.4e-12; rdi =  1.2e-13; gap =  3.0e-08

  28: obj =   5.501845895e+03; rpi =  1.5e-13; rdi =  1.2e-14; gap =  3.0e-09

 OPTIMAL SOLUTION FOUND

 Writing interior-point solution to `25fv47.txt'...

 \end{verbatim}

 \end{footnotesize}

 \subsection{glp\_init\_iptcp---initialize interior-point solver control

 parameters}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_init_iptcp(glp_iptcp *parm);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_init_iptcp| initializes control parameters, which

 are used by the interior-point solver, with default values.

 Default values of the control parameters are stored in the structure

 \verb|glp_iptcp|, which the parameter \verb|parm| points to.

 \subsection{glp\_ipt\_status---determine solution status}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_ipt_status(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_ipt_status| reports the status of a solution

 found by the interior-point solver as follows:

 \begin{tabular}{@{}p{25mm}p{91.3mm}@{}}

 \verb|GLP_UNDEF| & interior-point solution is undefined. \\

 \verb|GLP_OPT|   & interior-point solution is optimal. \\

 \verb|GLP_INFEAS|& interior-point solution is infeasible. \\

 \verb|GLP_NOFEAS|& no feasible primal-dual solution exists.\\

 \end{tabular}

 \subsection{glp\_ipt\_obj\_val---retrieve objective value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_ipt_obj_val(glp_prob *lp);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_ipt_obj_val| returns value of the objective

 function for interior-point solution.

 \subsection{glp\_ipt\_row\_prim---retrieve row primal value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_ipt_row_prim(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_ipt_row_prim| returns primal value of the

 auxiliary variable associated with \verb|i|-th row.

 \newpage

 \subsection{glp\_ipt\_row\_dual---retrieve row dual value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_ipt_row_dual(glp_prob *lp, int i);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_ipt_row_dual| returns dual value (i.e. reduced

 cost) of the auxiliary variable associated with \verb|i|-th row.

 \subsection{glp\_ipt\_col\_prim---retrieve column primal value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_ipt_col_prim(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_ipt_col_prim| returns primal value of the

 structural variable associated with \verb|j|-th column.

 \subsection{glp\_ipt\_col\_dual---retrieve column dual value}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 double glp_ipt_col_dual(glp_prob *lp, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_ipt_col_dual| returns dual value (i.e. reduced

 cost) of the structural variable associated with \verb|j|-th column.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 \newpage

 \section{Mixed integer programming routines}

 \subsection{glp\_set\_col\_kind---set (change) column kind}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 void glp_set_col_kind(glp_prob *mip, int j, int kind);

 \end{verbatim}

 \subsubsection*{Description}

 The routine \verb|glp_set_col_kind| sets (changes) the kind of

 \verb|j|-th column (structural variable) as specified by the parameter

 \verb|kind|:

 \begin{tabular}{@{}ll}

 \verb|GLP_CV| & continuous variable; \\

 \verb|GLP_IV| & integer variable; \\

 \verb|GLP_BV| & binary variable. \\

 \end{tabular}

 %If a column is set to \verb|GLP_IV|, its bounds must be exact integer

 %numbers with no tolerance, such that the condition

 %\verb|bnd == floor(bnd)| would hold.

 Setting a column to \verb|GLP_BV| has the same effect as if it were

 set to \verb|GLP_IV|, its lower bound were set 0, and its upper bound

 were set to 1.

 \subsection{glp\_get\_col\_kind---retrieve column kind}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_col_kind(glp_prob *mip, int j);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_col_kind| returns the kind of \verb|j|-th

 column (structural variable) as follows:

 \begin{tabular}{@{}ll}

 \verb|GLP_CV| & continuous variable; \\

 \verb|GLP_IV| & integer variable; \\

 \verb|GLP_BV| & binary variable. \\

 \end{tabular}

 \subsection{glp\_get\_num\_int---retrieve number of integer columns}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_num_int(glp_prob *mip);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_num_int| returns the number of columns

 (structural variables), which are marked as integer. Note that this

 number {\it does} include binary columns.

 \subsection{glp\_get\_num\_bin---retrieve number of binary columns}

 \subsubsection*{Synopsis}

 \begin{verbatim}

 int glp_get_num_bin(glp_prob *mip);

 \end{verbatim}

 \subsubsection*{Returns}

 The routine \verb|glp_get_num_bin| returns the number of columns

 (structural variables), which are marked as integer and whose lower