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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<html>
	<HEAD>
		<TITLE>GNU MathProg</TITLE>
		<style TYPE="text/css"> BODY { font-family:verdana,arial,helvetica; margin:0; }
	</style>
	</HEAD>
	<BODY>
		<TABLE STYLE="TABLE-LAYOUT:fixed" class="clsContainer" CELLPADDING="15" CELLSPACING="0"
			WIDTH="100%" BORDER="0">
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				<TD VALIGN="top">
					<h1 align="left"><u>GNU MathProg</u></h1>
                                        <p>GNU MathProg is a modeling language intended for describing linear mathematical programming models.
                                           <br>
                                           Model descriptions written in the GNU MathProg language consist of a set of statements and data blocks
                                           constructed by the user.
                                        </p>

                                        <p>See <a href="http://gnuwin32.sourceforge.net/downlinks/glpk-doc-zip.php">http://gnuwin32.sourceforge.net/downlinks/glpk-doc-zip.php</a> for a complete description of this modeling language.</p>

                                        <p>GNU MathProg is part of the GLPK solver.
                                           See <a href="http://www.gnu.org/software/glpk/glpk.html">http://www.gnu.org/software/glpk/glpk.html</a>
                                           and <a href="http://gnuwin32.sourceforge.net/packages/glpk.htm">http://gnuwin32.sourceforge.net/packages/glpk.htm</a>
                                           for the homepage of it.<br>
                                           Note that MathProg is a subset of the AMPL modeling language. See <a href="AMPL.htm">Using lpsolve from AMPL</a>.<br>
                                           The XLI used by lp_solve to read these models is derived from this code.<br>
                                           <br>
                                           lp_solve can read/write and solve these MathProg models directly via the xli_MathProg XLI driver (see <a href="XLI.htm">External Language Interfaces</a>).
                                           It reads such a model in above format and can solve it then.<br>
                                           <br>
					   For example:</p>

<pre>
lp_solve -rxli xli_MathProg <a href="#Diet1.mod">Diet1.mod</a>
</pre>
                                        <p>This gives as result:</p>
<pre>
Value of objective function: 88.2

Actual values of the variables:
Buy[BEEF]                       0
Buy[CHK]                        0
Buy[FISH]                       0
Buy[HAM]                        0
Buy[MCH]                  46.6667
Buy[MTL]                        0
Buy[SPG]                        0
Buy[TUR]                        0
</pre>

                                        <p>MathProg has also the possibility to have the model and data in two separate files.
                                           lp_solve can handle this also. For example:</p>
<pre>
lp_solve -rxli xli_MathProg <a href="#diet.mod">diet.mod</a> -rxlidata <a href="#diet.dat">diet.dat</a>
</pre>
                                        <p>This gives as result:</p>
<pre>
Value of objective function: 88.2

Actual values of the variables:
Buy[BEEF]                       0
Buy[CHK]                        0
Buy[FISH]                       0
Buy[HAM]                        0
Buy[MCH]                  46.6667
Buy[MTL]                        0
Buy[SPG]                        0
Buy[TUR]                        0
</pre>

                                        <h4>Generating MathProg models</h4>

                                        <p>The XLI can also create a MathProg model, however it doesn't use the strength of the language.
                                        Constraints are written out line per line. But it can be a starter.
                                        For example:</p>

<pre>
lp_solve <a href="#model.lp">model.lp</a> -wxli xli_MathProg model.mod
</pre>

                                        <p>This gives as model.mod:</p>
<pre>
/* Variable definitions */
var x &gt;= 0;
var y &gt;= 0;

/* Objective function */
maximize obj: +143*x +60*y;

/* Constraints */
R1: +120*x +210*y &lt;= 15000;
R2: +110*x +30*y &lt;= 4000;
R3: +x +y &lt;= 75;
</pre>

                                        <h4>API</h4>
                                        <p>Use the lpsolve API call <a href="read_XLI.htm">read_XLI</a> to read a model
                                           and <a href="write_XLI.htm">write_XLI</a> to write a model.
                                           See also <a href="XLI.htm">External Language Interfaces</a>.
                                        </p>

                                        <h4>IDE</h4>
                                        <p>Also from within the IDE, this XLI can be used. However, some entries
                                           must be added in LpSolveIDE.ini (in the folder where the IDE is installed).
                                        </p>
                                        <p>In the [XLI] section the following must be added:</p>
<pre>
lib1=xli_MathProg
</pre>
                                        <p>And a new section for the MathProg XLI must also be added:</p>
<pre>
[xli_MathProg]
extension=.mod
language=MATHPROG
</pre>
                                        <p>Then make sure that the xli_MathProg.dll is available for the IDE.
                                           This must be done by placing this dll in the IDE folder or in the
                                           Windows system32 folder.</p>

                                        <h4>Example models/data</h4>

<a name="Diet1.mod"></a>
                                        <h5>Diet1.mod</h5>
<pre>
set NUTR;
set FOOD;

param cost {FOOD} &gt; 0;
param f_min {FOOD} &gt;= 0;
param f_max {j in FOOD} &gt;= f_min[j];

param n_min {NUTR} &gt;= 0;
param n_max {i in NUTR} &gt;= n_min[i];

param amt {NUTR,FOOD} &gt;= 0;

var Buy {j in FOOD} &gt;= f_min[j], &lt;= f_max[j];

minimize total_cost:  sum {j in FOOD} cost[j] * Buy[j];

subject to diet {i in NUTR}:
   n_min[i] &lt;= sum {j in FOOD} amt[i,j] * Buy[j] &lt;= n_max[i];


data;

set NUTR := A B1 B2 C ;
set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ;

param:   cost  f_min  f_max :=
  BEEF   3.19    0     100
  CHK    2.59    0     100
  FISH   2.29    0     100
  HAM    2.89    0     100
  MCH    1.89    0     100
  MTL    1.99    0     100
  SPG    1.99    0     100
  TUR    2.49    0     100 ;

param:   n_min  n_max :=
   A      700   10000
   C      700   10000
   B1     700   10000
   B2     700   10000 ;

param amt (tr):
           A    C   B1   B2 :=
   BEEF   60   20   10   15
   CHK     8    0   20   20
   FISH    8   10   15   10
   HAM    40   40   35   10
   MCH    15   35   15   15
   MTL    70   30   15   15
   SPG    25   50   25   15
   TUR    60   20   15   10 ;
end;
</pre>

<a name="diet.mod"></a>
                                        <h5>diet.mod</h5>
<pre>
set NUTR;
set FOOD;

param cost {FOOD} &gt; 0;
param f_min {FOOD} &gt;= 0;
param f_max {j in FOOD} &gt;= f_min[j];

param n_min {NUTR} &gt;= 0;
param n_max {i in NUTR} &gt;= n_min[i];

param amt {NUTR,FOOD} &gt;= 0;

var Buy {j in FOOD} &gt;= f_min[j], &lt;= f_max[j];

minimize total_cost:  sum {j in FOOD} cost[j] * Buy[j];

subject to diet {i in NUTR}:
   n_min[i] &lt;= sum {j in FOOD} amt[i,j] * Buy[j] &lt;= n_max[i];
</pre>

<a name="diet.dat"></a>
                                        <h5>diet.dat</h5>

<pre>
set NUTR := A B1 B2 C ;
set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ;

param:   cost  f_min  f_max :=
  BEEF   3.19    0     100
  CHK    2.59    0     100
  FISH   2.29    0     100
  HAM    2.89    0     100
  MCH    1.89    0     100
  MTL    1.99    0     100
  SPG    1.99    0     100
  TUR    2.49    0     100 ;

param:   n_min  n_max :=
   A      700   10000
   C      700   10000
   B1     700   10000
   B2     700   10000 ;

param amt (tr):
           A    C   B1   B2 :=
   BEEF   60   20   10   15
   CHK     8    0   20   20
   FISH    8   10   15   10
   HAM    40   40   35   10
   MCH    15   35   15   15
   MTL    70   30   15   15
   SPG    25   50   25   15
   TUR    60   20   15   10 ;
</pre>

<a name="model.lp"></a>
                                        <h5>model.lp</h5>

<pre>
/* model.lp */

max: 143 x + 60 y;

120 x + 210 y &lt;= 15000;
110 x + 30 y &lt;= 4000;
x + y &lt;= 75;
</pre>

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