/* pointer to the head endpoint */      void *data;      /* pointer to data associated with the arc */      void *temp;      /* working pointer */      glp_arc *t_prev;      /* pointer to previous arc having the same tail endpoint */      glp_arc *t_next;      /* pointer to next arc having the same tail endpoint */      glp_arc *h_prev;      /* pointer to previous arc having the same head endpoint */      glp_arc *h_next;      /* pointer to next arc having the same head endpoint */};glp_graph *glp_create_graph(int v_size, int a_size);/* create graph */void glp_set_graph_name(glp_graph *G, const char *name);/* assign (change) graph name */int glp_add_vertices(glp_graph *G, int nadd);/* add new vertices to graph */glp_arc *glp_add_arc(glp_graph *G, int i, int j);/* add new arc to graph */void glp_erase_graph(glp_graph *G, int v_size, int a_size);/* erase graph content */void glp_delete_graph(glp_graph *G);/* delete graph */void glp_mincost_lp(glp_prob *lp, glp_graph *G, int names, int v_rhs,      int a_low, int a_cap, int a_cost);/* convert minimum cost flow problem to LP */int glp_mincost_okalg(glp_graph *G, int v_rhs, int a_low, int a_cap,      int a_cost, double *sol, int a_x, int v_pi);/* find minimum-cost flow with out-of-kilter algorithm */void glp_maxflow_lp(glp_prob *lp, glp_graph *G, int names, int s,      int t, int a_cap);/* convert maximum flow problem to LP */int glp_maxflow_ffalg(glp_graph *G, int s, int t, int a_cap,      double *sol, int a_x, int v_cut);/* find maximal flow with Ford-Fulkerson algorithm */int glp_read_mincost(glp_graph *G, int v_rhs, int a_low, int a_cap,      int a_cost, const char *fname);/* read min-cost flow problem data in DIMACS format */int glp_write_mincost(glp_graph *G, int v_rhs, int a_low, int a_cap,      int a_cost, const char *fname);/* write min-cost flow problem data in DIMACS format */int glp_read_maxflow(glp_graph *G, int *s, int *t, int a_cap,      const char *fname);/* read maximum flow problem data in DIMACS format */int glp_write_maxflow(glp_graph *G, int s, int t, int a_cap,      const char *fname);/* write maximum flow problem data in DIMACS format */int glp_netgen(glp_graph *G, int v_rhs, int a_cap, int a_cost,      const int parm[1+15]);/* Klingman's network problem generator */int glp_gridgen(glp_graph *G, int v_rhs, int a_cap, int a_cost,      const int parm[1+14]);/* grid-like network problem generator */int glp_rmfgen(glp_graph *G, int *s, int *t, int a_cap,      const int parm[1+5]);/* Goldfarb's maximum flow problem generator */int glp_weak_comp(glp_graph *G, int v_num);/* find all weakly connected components of graph *//**********************************************************************/#define LPX glp_prob/* problem class: */#define LPX_LP          100   /* linear programming (LP) */#define LPX_MIP         101   /* mixed integer programming (MIP) *//* type of auxiliary/structural variable: */#define LPX_FR          110   /* free variable */#define LPX_LO          111   /* variable with lower bound */#define LPX_UP          112   /* variable with upper bound */#define LPX_DB          113   /* double-bounded variable */#define LPX_FX          114   /* fixed variable *//* optimization direction flag: */#define LPX_MIN         120   /* minimization */#define LPX_MAX         121   /* maximization *//* status of primal basic solution: */#define LPX_P_UNDEF     132   /* primal solution is undefined */#define LPX_P_FEAS      133   /* solution is primal feasible */#define LPX_P_INFEAS    134   /* solution is primal infeasible */#define LPX_P_NOFEAS    135   /* no primal feasible solution exists *//* status of dual basic solution: */#define LPX_D_UNDEF     136   /* dual solution is undefined */#define LPX_D_FEAS      137   /* solution is dual feasible */#define LPX_D_INFEAS    138   /* solution is dual infeasible */#define LPX_D_NOFEAS    139   /* no dual feasible solution exists *//* status of auxiliary/structural variable: */#define LPX_BS          140   /* basic variable */#define LPX_NL          141   /* non-basic variable on lower bound */#define LPX_NU          142   /* non-basic variable on upper bound */#define LPX_NF          143   /* non-basic free variable */#define LPX_NS          144   /* non-basic fixed variable *//* status of interior-point solution: */#define LPX_T_UNDEF     150   /* interior solution is undefined */#define LPX_T_OPT       151   /* interior solution is optimal *//* kind of structural variable: */#define LPX_CV          160   /* continuous variable */#define LPX_IV          161   /* integer variable *//* status of integer solution: */#define LPX_I_UNDEF     170   /* integer solution is undefined */#define LPX_I_OPT       171   /* integer solution is optimal */#define LPX_I_FEAS      172   /* integer solution is feasible */#define LPX_I_NOFEAS    173   /* no integer solution exists *//* status codes reported by the routine lpx_get_status: */#define LPX_OPT         180   /* optimal */#define LPX_FEAS        181   /* feasible */#define LPX_INFEAS      182   /* infeasible */#define LPX_NOFEAS      183   /* no feasible */#define LPX_UNBND       184   /* unbounded */#define LPX_UNDEF       185   /* undefined *//* exit codes returned by solver routines: */#define LPX_E_OK        200   /* success */#define LPX_E_EMPTY     201   /* empty problem */#define LPX_E_BADB      202   /* invalid initial basis */#define LPX_E_INFEAS    203   /* infeasible initial solution */#define LPX_E_FAULT     204   /* unable to start the search */#define LPX_E_OBJLL     205   /* objective lower limit reached */#define LPX_E_OBJUL     206   /* objective upper limit reached */#define LPX_E_ITLIM     207   /* iterations limit exhausted */#define LPX_E_TMLIM     208   /* time limit exhausted */#define LPX_E_NOFEAS    209   /* no feasible solution */#define LPX_E_INSTAB    210   /* numerical instability */#define LPX_E_SING      211   /* problems with basis matrix */#define LPX_E_NOCONV    212   /* no convergence (interior) */#define LPX_E_NOPFS     213   /* no primal feas. sol. (LP presolver) */#define LPX_E_NODFS     214   /* no dual feas. sol. (LP presolver) */#define LPX_E_MIPGAP    215   /* relative mip gap tolerance reached *//* control parameter identifiers: */#define LPX_K_MSGLEV    300   /* lp->msg_lev */#define LPX_K_SCALE     301   /* lp->scale */#define LPX_K_DUAL      302   /* lp->dual */#define LPX_K_PRICE     303   /* lp->price */#define LPX_K_RELAX     304   /* lp->relax */#define LPX_K_TOLBND    305   /* lp->tol_bnd */#define LPX_K_TOLDJ     306   /* lp->tol_dj */#define LPX_K_TOLPIV    307   /* lp->tol_piv */#define LPX_K_ROUND     308   /* lp->round */#define LPX_K_OBJLL     309   /* lp->obj_ll */#define LPX_K_OBJUL     310   /* lp->obj_ul */#define LPX_K_ITLIM     311   /* lp->it_lim */#define LPX_K_ITCNT     312   /* lp->it_cnt */#define LPX_K_TMLIM     313   /* lp->tm_lim */#define LPX_K_OUTFRQ    314   /* lp->out_frq */#define LPX_K_OUTDLY    315   /* lp->out_dly */#define LPX_K_BRANCH    316   /* lp->branch */#define LPX_K_BTRACK    317   /* lp->btrack */#define LPX_K_TOLINT    318   /* lp->tol_int */#define LPX_K_TOLOBJ    319   /* lp->tol_obj */#define LPX_K_MPSINFO   320   /* lp->mps_info */#define LPX_K_MPSOBJ    321   /* lp->mps_obj */#define LPX_K_MPSORIG   322   /* lp->mps_orig */#define LPX_K_MPSWIDE   323   /* lp->mps_wide */#define LPX_K_MPSFREE   324   /* lp->mps_free */#define LPX_K_MPSSKIP   325   /* lp->mps_skip */#define LPX_K_LPTORIG   326   /* lp->lpt_orig */#define LPX_K_PRESOL    327   /* lp->presol */#define LPX_K_BINARIZE  328   /* lp->binarize */#define LPX_K_USECUTS   329   /* lp->use_cuts */#define LPX_K_BFTYPE    330   /* lp->bfcp->type */#define LPX_K_MIPGAP    331   /* lp->mip_gap */#define LPX_C_COVER     0x01  /* mixed cover cuts */#define LPX_C_CLIQUE    0x02  /* clique cuts */#define LPX_C_GOMORY    0x04  /* Gomory's mixed integer cuts */#define LPX_C_MIR       0x08  /* mixed integer rounding cuts */#define LPX_C_ALL       0xFF  /* all cuts */typedef struct{     /* this structure contains results reported by the routines which         checks Karush-Kuhn-Tucker conditions (for details see comments         to those routines) */      /*--------------------------------------------------------------*/      /* xR - A * xS = 0 (KKT.PE) */      double pe_ae_max;      /* largest absolute error */      int    pe_ae_row;      /* number of row with largest absolute error */      double pe_re_max;      /* largest relative error */      int    pe_re_row;      /* number of row with largest relative error */      int    pe_quality;      /* quality of primal solution:         'H' - high         'M' - medium         'L' - low         '?' - primal solution is wrong */      /*--------------------------------------------------------------*/      /* l[k] <= x[k] <= u[k] (KKT.PB) */      double pb_ae_max;      /* largest absolute error */      int    pb_ae_ind;      /* number of variable with largest absolute error */      double pb_re_max;      /* largest relative error */      int    pb_re_ind;      /* number of variable with largest relative error */      int    pb_quality;      /* quality of primal feasibility:         'H' - high         'M' - medium         'L' - low         '?' - primal solution is infeasible */      /*--------------------------------------------------------------*/      /* A' * (dR - cR) + (dS - cS) = 0 (KKT.DE) */      double de_ae_max;      /* largest absolute error */      int    de_ae_col;      /* number of column with largest absolute error */      double de_re_max;      /* largest relative error */      int    de_re_col;      /* number of column with largest relative error */      int    de_quality;      /* quality of dual solution:         'H' - high         'M' - medium         'L' - low         '?' - dual solution is wrong */      /*--------------------------------------------------------------*/      /* d[k] >= 0 or d[k] <= 0 (KKT.DB) */      double db_ae_max;      /* largest absolute error */      int    db_ae_ind;      /* number of variable with largest absolute error */      double db_re_max;      /* largest relative error */      int    db_re_ind;      /* number of variable with largest relative error */      int    db_quality;      /* quality of dual feasibility:         'H' - high         'M' - medium         'L' - low         '?' - dual solution is infeasible */      /*--------------------------------------------------------------*/      /* (x[k] - bound of x[k]) * d[k] = 0 (KKT.CS) */      double cs_ae_max;      /* largest absolute error */      int    cs_ae_ind;      /* number of variable with largest absolute error */      double cs_re_max;      /* largest relative error */      int    cs_re_ind;      /* number of variable with largest relative error */      int    cs_quality;      /* quality of complementary slackness:         'H' - high         'M' - medium         'L' - low         '?' - primal and dual solutions are not complementary */} LPXKKT;#define lpx_create_prob _glp_lpx_create_probLPX *lpx_create_prob(void);/* create problem object */#define lpx_set_prob_name _glp_lpx_set_prob_namevoid lpx_set_prob_name(LPX *lp, const char *name);/* assign (change) problem name */#define lpx_set_obj_name _glp_lpx_set_obj_namevoid lpx_set_obj_name(LPX *lp, const char *name);/* assign (change) objective function name */#define lpx_set_obj_dir _glp_lpx_set_obj_dirvoid lpx_set_obj_dir(LPX *lp, int dir);/* set (change) optimization direction flag */#define lpx_add_rows _glp_lpx_add_rowsint lpx_add_rows(LPX *lp, int nrs);/* add new rows to problem object */#define lpx_add_cols _glp_lpx_add_colsint lpx_add_cols(LPX *lp, int ncs);/* add new columns to problem object */#define lpx_set_row_name _glp_lpx_set_row_namevoid lpx_set_row_name(LPX *lp, int i, const char *name);/* assign (change) row name */#define lpx_set_col_name _glp_lpx_set_col_namevoid lpx_set_col_name(LPX *lp, int j, const char *name);/* assign (change) column name */#define lpx_set_row_bnds _glp_lpx_set_row_bndsvoid lpx_set_row_bnds(LPX *lp, int i, int type, double lb, double ub);/* set (change) row bounds */#define lpx_set_col_bnds _glp_lpx_set_col_bndsvoid lpx_set_col_bnds(LPX *lp, int j, int type, double lb, double ub);/* set (change) column bounds */#define lpx_set_obj_coef _glp_lpx_set_obj_coefvoid lpx_set_obj_coef(glp_prob *lp, int j, double coef);/* set (change) obj. coefficient or constant term */#define lpx_set_mat_row _glp_lpx_set_mat_rowvoid lpx_set_mat_row(LPX *lp, int i, int len, const int ind[],      const double val[]);/* set (replace) row of the constraint matrix */#define lpx_set_mat_col _glp_lpx_set_mat_colvoid lpx_set_mat_col(LPX *lp, int j, int len, const int ind[],      const double val[]);/* set (replace) column of the constraint matrix */#define lpx_load_matrix _glp_lpx_load_matrixvoid lpx_load_matrix(LPX *lp, int ne, const int ia[], const int ja[],      const double ar[]);/* load (replace) the whole constraint matrix */#define lpx_del_rows _glp_lpx_del_rowsvoid lpx_del_rows(LPX *lp, int nrs, const int num[]);/* delete specified rows from problem object */#define lpx_del_cols _glp_lpx_del_colsvoid lpx_del_cols(LPX *lp, int ncs, const int num[]);/* delete specified columns from problem object */#define lpx_delete_prob _glp_lpx_delete_probvoid lpx_delete_prob(LPX *lp);/* delete problem object */#define lpx_get_prob_name _glp_lpx_get_prob_nameconst char *lpx_get_prob_name(LPX *lp);/* retrieve problem name */#define lpx_get_obj_name _glp_lpx_get_obj_nameconst char *lpx_get_obj_name(LPX *lp);/* retrieve objective function name */#define lpx_get_obj_dir _glp_lpx_get_obj_dirint lpx_get_obj_dir(LPX *lp);/* retrieve optimization direction flag */#define lpx_get_num_rows _glp_lpx_get_num_rowsint lpx_get_num_rows(LPX *lp);/* retrieve number of rows */#define lpx_get_num_cols _glp_lpx_get_num_colsint lpx_get_num_cols(LPX *lp);/* retrieve number of columns */#define lpx_get_row_name _glp_lpx_get_row_name