N.B. THIS IS NOT AN OFFICIAL RELEASE OF GLPK 4.6.
Here is a patch to upgrade glpk 4.5 to 4.6 (please see the attachment).
Its MD5 check-sum must be:
757c51aaf23fb2e32feed95a0a2922f1 *glpk.diff.gz
To upgrade the package do the following:
1. Download glpk-4.5.tar.gz from GNU ftp site or some its mirror.
2. Unzip and untar glpk-4.5.tar.gz in a working subdirectory.
3. Unzip the patch attached and place it in the same working directory,
i.e. the subdirectory 'glpk-4.5' and the file 'glpk.diff' must be in
the same subdirectory.
4. Run the command (only once!): patch -p0 < glpk.diff
There will be some warnings about patching glpkmex files. Never mind
on them.
5. Rename the subdirectory 'glpk-4.5' to 'glpk-4.6'.
6. Configure and compile/install the package as usual.
What's new in glpk 4.6:
***********************
Two new statements of the GNU MathProg language were implemented:
solve and printf.
The solve statement is optional and can be used only once in the model
description. It has the following syntax:
solve;
Having been executed the solve statement makes all model variables to
be similar model parameters, i.e. below the solve statement any variable
can be referenced in the same way as a parameter. Note that variable,
constraint, and objective statements can be used only above the solve
statement while set, parameter, display, and printf statements can be
used above as well as below the solve statement.
The printf statement is intended to produce resulting reports. It has
the following syntax:
printf format-string, expr, expr, ..., expr;
printf { domain } : format-string, expr, expr, ..., expr;
where format-string is a symbolic literal or _expression_ which specifies
a format control string in the same way as in the C language; expr is a
numeric, symbolic, or logical _expression_ (if printf is used below the
solve statement, the _expression_ may refer to model variables).
Both statements solve and printf are supported by the solver glpsol.
The output may be redirected with '-y' or '--display' option.
Below here is a brief example which illustrates how to use the solve and
printf statements.
Any comments and suggestions are welcome.
Andrew Makhorin
========================================================================
# A TRANSPORTATION PROBLEM
#
# This problem finds a least cost shipping schedule that meets
# requirements at markets and supplies at factories.
#
# References:
# Dantzig G B, "Linear Programming and Extensions."
# Princeton University Press, Princeton, New Jersey, 1963,
# Chapter 3-3.
set I;
/* canning plants */
set J;
/* markets */
param a{i in I};
/* capacity of plant i in cases */
param b{j in J};
/* demand at market j in cases */
param d{i in I, j in J};
/* distance in thousands of miles */
param f;
/* freight in dollars per case per thousand miles */
param c{i in I, j in J} := f * d[i,j] / 1000;
/* transport cost in thousands of dollars per case */
var x{i in I, j in J} >= 0;
/* shipment quantities in cases */
minimize cost: sum{i in I, j in J} c[i,j] * x[i,j];
/* total transportation costs in thousands of dollars */
s.t. supply{i in I}: sum{j in J} x[i,j] <= a[i];
/* observe supply limit at plant i */
s.t. demand{j in J}: sum{i in I} x[i,j] >= b[j];
/* satisfy demand at market j */
solve;
printf "";
printf "From To Cost Shipping Total cost";
printf "---------- ---------- ---------- ---------- ----------";
printf {i in I, j in J: x[i,j] != 0}:
"%-10s %-10s %10.3f %10d %10.3f", i, j, c[i,j], x[i,j],
c[i,j] * x[i,j];
printf "------------------------------------------------------";
printf " %10.3f",
sum{i in I, j in J} c[i,j] * x[i,j];
printf "";
data;
set I := Seattle San-Diego;
set J := New-York Chicago Topeka;
param a := Seattle 350
San-Diego 600;
param b := New-York 325
Chicago 300
Topeka 275;
param d : New-York Chicago Topeka :=
Seattle 2.5 1.7 1.8
San-Diego 2.5 1.8 1.4 ;
param f := 90;
end;
========================================================================
$ ./glpsol transp.mod
Reading model section from transp.mod...
Reading data section from transp.mod...
76 lines were read
Generating cost...
Generating supply...
Generating demand...
Model has been successfully generated
lpx_simplex: original LP has 6 rows, 6 columns, 18 non-zeros
lpx_simplex: presolved LP has 5 rows, 6 columns, 12 non-zeros
lpx_adv_basis: size of triangular part = 5
0: objval = 0.000000000e+00 infeas = 1.000000000e+00 (0)
4: objval = 1.563750000e+02 infeas = 0.000000000e+00 (0)
* 4: objval = 1.563750000e+02 infeas = 0.000000000e+00 (0)
* 5: objval = 1.536750000e+02 infeas = 0.000000000e+00 (0)
OPTIMAL SOLUTION FOUND
Time used: 0.0 secs
Memory used: 0.2M (174146 bytes)
>From To Cost Shipping Total cost
---------- ---------- ---------- ---------- ----------
Seattle Chicago 0.153 300 45.900
San-Diego New-York 0.225 325 73.125
San-Diego Topeka 0.126 275 34.650
------------------------------------------------------
153.675
Model has been successfully processed
========================================================================
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