demo/lp_demo.cc
author kpeter
Thu, 13 Nov 2008 15:29:04 +0000
changeset 2629 84354c78b068
parent 2391 14a343be7a5a
permissions -rw-r--r--
Improved constructors for min cost flow classes
Removing the non-zero lower bounds is faster
     1 /* -*- C++ -*-
     2  *
     3  * This file is a part of LEMON, a generic C++ optimization library
     4  *
     5  * Copyright (C) 2003-2008
     6  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
     7  * (Egervary Research Group on Combinatorial Optimization, EGRES).
     8  *
     9  * Permission to use, modify and distribute this software is granted
    10  * provided that this copyright notice appears in all copies. For
    11  * precise terms see the accompanying LICENSE file.
    12  *
    13  * This software is provided "AS IS" with no warranty of any kind,
    14  * express or implied, and with no claim as to its suitability for any
    15  * purpose.
    16  *
    17  */
    18 
    19 /// \ingroup demos
    20 /// \file
    21 /// \brief A program demonstrating the LEMON LP solver interface
    22 ///
    23 /// This program is a simple application of the LEMON LP solver
    24 /// interface: we formulate a linear programming (LP) problem and then
    25 /// solve it using the underlying solver (GLPK or CPLEX for
    26 /// example). For the detailed documentation of the LEMON LP solver
    27 /// interface read \ref lemon::LpSolverBase "this".
    28 ///
    29 /// \include lp_demo.cc
    30 
    31 #include <lemon/lp.h>
    32 
    33 #include <iostream>
    34 
    35 using namespace lemon;
    36 
    37 int main()
    38 {     
    39  //The following example is taken from the documentation of the GLPK library.
    40  //See it in the GLPK reference manual and among the GLPK sample files (sample.c)
    41 
    42   //A default solver is taken
    43   Lp lp;
    44   typedef Lp::Row Row;
    45   typedef Lp::Col Col;
    46   
    47 
    48   std::cout<<"A program demonstrating the LEMON LP solver interface"<<std::endl; 
    49   std::cout<<"Solver used: "<<default_solver_name<<std::endl;
    50 
    51   //This will be a maximization
    52   lp.max();
    53 
    54   //We add coloumns (variables) to our problem
    55   Col x1 = lp.addCol();
    56   Col x2 = lp.addCol();
    57   Col x3 = lp.addCol();
    58 
    59   //Constraints
    60   lp.addRow(x1+x2+x3 <=100);  
    61   lp.addRow(10*x1+4*x2+5*x3<=600);  
    62   lp.addRow(2*x1+2*x2+6*x3<=300);  
    63   //Nonnegativity of the variables
    64   lp.colLowerBound(x1, 0);
    65   lp.colLowerBound(x2, 0);
    66   lp.colLowerBound(x3, 0);
    67   //Objective function
    68   lp.obj(10*x1+6*x2+4*x3);
    69   
    70   //Call the routine of the underlying LP solver
    71   lp.solve();
    72 
    73   //Print results
    74   if (lp.primalStatus()==LpSolverBase::OPTIMAL){
    75     std::cout<<"Optimal solution found!"<<std::endl;
    76     printf("optimum value = %g; x1 = %g; x2 = %g; x3 = %g\n", 
    77 	   lp.primalValue(), 
    78 	   lp.primal(x1), lp.primal(x2), lp.primal(x3));
    79   }
    80   else{
    81     std::cout<<"Optimal solution not found!"<<std::endl;
    82   }
    83 
    84   //End of LEMON style code
    85 
    86   //Here comes the same problem written in C using GLPK API routines
    87 
    88 //   LPX *lp;
    89 //       int ia[1+1000], ja[1+1000];
    90 //       double ar[1+1000], Z, x1, x2, x3;
    91 // s1:   lp = lpx_create_prob();
    92 // s2:   lpx_set_prob_name(lp, "sample");
    93 // s3:   lpx_set_obj_dir(lp, LPX_MAX);
    94 // s4:   lpx_add_rows(lp, 3);
    95 // s5:   lpx_set_row_name(lp, 1, "p");
    96 // s6:   lpx_set_row_bnds(lp, 1, LPX_UP, 0.0, 100.0);
    97 // s7:   lpx_set_row_name(lp, 2, "q");
    98 // s8:   lpx_set_row_bnds(lp, 2, LPX_UP, 0.0, 600.0);
    99 // s9:   lpx_set_row_name(lp, 3, "r");
   100 // s10:  lpx_set_row_bnds(lp, 3, LPX_UP, 0.0, 300.0);
   101 // s11:  lpx_add_cols(lp, 3);
   102 // s12:  lpx_set_col_name(lp, 1, "x1");
   103 // s13:  lpx_set_col_bnds(lp, 1, LPX_LO, 0.0, 0.0);
   104 // s14:  lpx_set_obj_coef(lp, 1, 10.0);
   105 // s15:  lpx_set_col_name(lp, 2, "x2");
   106 // s16:  lpx_set_col_bnds(lp, 2, LPX_LO, 0.0, 0.0);
   107 // s17:  lpx_set_obj_coef(lp, 2, 6.0);
   108 // s18:  lpx_set_col_name(lp, 3, "x3");
   109 // s19:  lpx_set_col_bnds(lp, 3, LPX_LO, 0.0, 0.0);
   110 // s20:  lpx_set_obj_coef(lp, 3, 4.0);
   111 // s21:  ia[1] = 1, ja[1] = 1, ar[1] =  1.0; /* a[1,1] =  1 */
   112 // s22:  ia[2] = 1, ja[2] = 2, ar[2] =  1.0; /* a[1,2] =  1 */
   113 // s23:  ia[3] = 1, ja[3] = 3, ar[3] =  1.0; /* a[1,3] =  1 */
   114 // s24:  ia[4] = 2, ja[4] = 1, ar[4] = 10.0; /* a[2,1] = 10 */
   115 // s25:  ia[5] = 3, ja[5] = 1, ar[5] =  2.0; /* a[3,1] =  2 */
   116 // s26:  ia[6] = 2, ja[6] = 2, ar[6] =  4.0; /* a[2,2] =  4 */
   117 // s27:  ia[7] = 3, ja[7] = 2, ar[7] =  2.0; /* a[3,2] =  2 */
   118 // s28:  ia[8] = 2, ja[8] = 3, ar[8] =  5.0; /* a[2,3] =  5 */
   119 // s29:  ia[9] = 3, ja[9] = 3, ar[9] =  6.0; /* a[3,3] =  6 */
   120 // s30:  lpx_load_matrix(lp, 9, ia, ja, ar);
   121 // s31:  lpx_simplex(lp);
   122 // s32:  Z = lpx_get_obj_val(lp);
   123 // s33:  x1 = lpx_get_col_prim(lp, 1);
   124 // s34:  x2 = lpx_get_col_prim(lp, 2);
   125 // s35:  x3 = lpx_get_col_prim(lp, 3);
   126 // s36:  printf("\nZ = %g; x1 = %g; x2 = %g; x3 = %g\n", Z, x1, x2, x3);
   127 // s37:  lpx_delete_prob(lp);
   128 //       return 0;
   129 
   130   return 0;
   131 }