A GAMS Utility for Ranking One-Dimensional Numeric Data.

Thomas F. Rutherford, Department of Economics, University of Colorado
Paul van der Eijk, GAMS Development Corporation
July, 2003

Source of this document:

This utility consists of an executable file, gdxrank.exe, and a GAMS libinclude file, rank.gms. The former resides in the GAMS system directory, and the latter resides in the inclib/ subdirectory of GAMS system.


Usage for GDXRANK

GDXRANK reads one or more one dimensional parameters from a GDX file, sorts each parameter and writes the sorted indices as a one dimensional parameters to the output GDX file.


gdxrank inputfile outputfile

Each one dimensional parameter is read from the input file, sorted and the corresponding integer permutation index written to the output file using the same name for the symbol. GAMS special values such as Eps, +Inf and -Inf are recognized.

Usage for RANK

$LIBINCLUDE rank v s r [p]

The first three arguments are required. The last is optional. These are defined as following:

Type Argument Description
Input:v(s)Array of values to be ranked.
sA one-dimensional set, the domain of array v.
Output:r(s)Rank order of element v(s), an integer between 1 and card(s), ranking from smallest to largest.
Optional: (input and output)p(*)On input this vector specifies percentile levels to be computed. On output, it returns the linearly interpolated percentiles.

General Comments

  • RANK only works for numeric data. You cannot sort sets.
  • The first invocation must be outside of a loop or if block. This routine may be used within a loop or if block only if it is first initialized with blank invocations ("$LIBINCLUDE rank" in a context where set and parameter declarations are permitted (See Example 3).
  • The following names are used within these routines and may not be used in the calling program:
     rank_tmp        rank_u          rank_p
  • This routine returns rank values and does not return sorted vectors, however rank values are easily used to produce a sorted array. This can be done using computed "leads" and "lags" in GAMS' ordered set syntax, as illustrated in examples 1 and 3 below.


In this example we sort a parameter, create a sorted version and verify that the sort worked correctly:

set I /i1 * i6/;

parameter A(I) /i1=+Inf, i2=-Inf, i3=Eps, i4= 10, i5=30, i6=20/;

display A;

* write symbol A to gdx file
execute_unload "rank_in.gdx", A;

* sort symbol; permutation index will be named A also
execute 'gdxrank rank_in.gdx rank_out.gdx';

* load the permutation index
parameter AIndex(i);

execute_load "rank_out.gdx", AIndex=A;

display AIndex;

* create a sorted version
parameter ASorted(i);
ASorted(i + (AIndex(i)- Ord(i))) = A(i);
display ASorted;

* check that the result is sorted
set C(i);
C(i)=Yes$(Ord(i) < Card(i)) and (ASorted(i) > ASorted(i+1));

display C;

Abort$(Card(C) <> 0) 'sort failed';

RANK Example

Example 1: Rank a vector, and display the data in sorted order

set     i       "Set on which random data are defined" /a,b,d,c,e,f /,
        k       "Ordered set for displaying sorted data" /1*6/;

parameter       x(i)    "Random data to be sorted",
                r(i)    "Rank values",
                s(k,i)  "Sorted data";

x(i) = uniform(0,1);

$libinclude rank x i r
display x;

*       Generate a sorted list using the ordered set k.

*       This assignment statement illustrates how the rank orders
*       can be used to sort output for display in proper order.  This
*       statement uses GAMS support for computed "leads" and "lags"
*       on the ordered set k.  The loop is used to improve execution
*       speed for larger dimensional sets:

  s(k+(r(i)-1),i) = x(i);

option s:3:0:1;
display s;

Example1 writes the following lines to ex1.lst:

----     11 PARAMETER x  Random data to be sorted

a 0.172,    b 0.843,    d 0.550,    c 0.301,    e 0.292,    f 0.224

----     75 PARAMETER s  Sorted data

1.a 0.172
2.f 0.224
3.e 0.292
4.c 0.301
5.d 0.550
6.b 0.843

Example1 writes the following lines to ex1.lst:

----     11 PARAMETER x  Random data to be sorted

a 0.172,    b 0.843,    d 0.550,    c 0.301,    e 0.292,    f 0.224

----     75 PARAMETER s  Sorted data

1.a 0.172
2.f 0.224
3.e 0.292
4.c 0.301
5.d 0.550
6.b 0.843

Example 2: Generate percentiles for a random vector

set     i       "Set on which random data are defined" /a,b,d,c,e /,
        p       "Percentiles (all of them)" /0*100/;

parameter       x(i)    "Random data to be sorted";

*       Generate the random data on set i:

x(i) = uniform(0,1);
display x;

parameter       r(i)    "Rank values",
                pct(*)  "Percentiles to be computed" /20 20.0, median 50.0, 75 75.0/;

*       Generate ranks and compute the specified percedntiles (Note that 
*       the rank array, r, is required, even if the values are not used.)

$libinclude rank x i r pct

*       Display three percentiles:

display pct;

The random data are displayed as follows in the listing file:

----     11 PARAMETER x  Random data to be sorted

a 0.172,    b 0.843,    d 0.550,    c 0.301,    e 0.292

The interpolated percentiles are computed as follows:

----    103 PARAMETER pct  Percentiles

20     0.268,    75     0.550,    median 0.301

The following code evaluates a full set of percentiles, from 1 to 100. The GAMS special value of EPS is used to represent zero in the percentile calculation. (Percentiles between zero and one are not permitted to avoid misunderstandings about how percentiles are scaled.)

pct(p) = (ord(p) - 1) + eps;
pct("median") = 0;
display pct;

$libinclude rank x i r pct
display pct;

*       Plot the results using GNUPLOT:

set pl(p) /20,40,60,80,100/;
$setglobal domain p
$setglobal labels pl
$libinclude plot pct

Example 3: Use GDXRANK to report multisectoral Monte Carlo results

One of the most perplexing challenges in economic modeling with GAMS is to present multisectoral results in an easily interpreted format. One simple idea is to present sectoral results in a sorted sequence to make it easier to identify the most seriously affectd sectors. The presentation of results in a multisectoral model is made even more challenging when model results are generated for a randomized set of scenarios. A summary of Monte Carlo results involves reporting both mean results and their sensitivity. One means of characterizing the sensitivity of model results is to report functions of the sample distribution such as the upper and lower quartiles.

In this example, I illustrate how gdxrank can be used to help report results from the Monte Carlo analysis of a multisectoral model.

SET     run     "Samples" (potentially not all solved) /1*1000/, 
        i       "Sectors for which results are to be compared"  /

parameter       v(run,i) "Monte-Carlo results for all sectors";

*       Load the sectoral results from the sensitivity analysis from
*       a GDX file:

$gdxin 'ex3.gdx'
$load v

set     s(run)          "Solved cases",
        k               "Count used for quartiles" /1*1000/,
        ki              "Count used for sectors"   /1*100/
        qtl             "Quartiles (set)"          /q25,q50,q75/,
        imap(ki,i)      "Mapping from ordered list to sector labels";

parameter       qvalue(i,*)     "Quartile values"
                mean(i)         "Mean impact on sector i", 
                meanrank(i)     "Mean rank of sector i",
                x(run)          "Vector used for sorting",
                r(run)          "Rank values returned",
                qv(qtl)         "Quartiles (values)" /q25 25, q50 50, q75 75/
                quartile(qtl)   "Quartiles (evaluated)";

*       Identify which scenarios have been solved (some of the runs
*       may have failed):

s(run) = yes$(smax(i,abs(v(run,i))) ne 0);

*       Evaluate means and support for results:

qvalue(i,"mean") = sum(s, v(s,i)) / card(s);
qvalue(i,"min") = smin(s, v(s,i));
qvalue(i,"max") = smax(s, v(s,i));
display qvalue;

*       Determine ranking of sectors by mean impact:

mean(i) = qvalue(i,"mean");
$libinclude rank mean i meanrank 

*       The following statement creates a tuple matching the ordered
*       set, ki, to the set of sectors, i.  In this tuple, the sequence of
*       assignments corresponds to increasing mean impacts:

imap(ki+(meanrank(i)-ord(ki)),i) = yes;
*       Evaluate quartiles of sectoral impacts for each sector:

  x(s) = v(s,i);

*       Load quartile with the perctiles to be
*       evaluated (25th, 50th and 75th):

  quartile(qtl) = qv(qtl);

$libinclude rank x s r quartile

*       Save the quartile values:

  qvalue(i,qtl) = quartile(qtl);
display qvalue;

parameter       results(ki,i,*)         "Summary of impacts (sorted)";
results(ki,i,"mean")$imap(ki,i) = mean(i);
results(ki,i,qtl)$imap(ki,i) = qvalue(i,qtl);
display results;

The program produces the following display output:

               q25         q50         q75        mean

1 .FOO     -13.767     -12.110     -10.671     -12.259
2 .MWO     -13.496     -11.982     -10.512     -12.035
3 .SCS     -12.244     -10.418      -8.738     -10.588
4 .CLO      -8.763      -7.407      -6.158      -7.480
5 .ADM      -7.865      -5.601      -3.470      -5.812
6 .CNM      -6.437      -5.320      -4.404      -5.461
7 .OTH      -5.732      -4.781      -4.012      -4.892
8 .PIP      -3.142      -1.930      -0.836      -2.145
9 .OIN      -2.124      -1.339      -0.563      -1.358
10.TPP      -2.527      -0.988       0.521      -1.041
11.GEO      -1.542      -0.826      -0.196      -0.883
12.AGF      -1.178      -0.624      -0.072      -0.625
13.ECM       0.137       0.466       0.796       0.441
14.SSM       0.303       0.711       1.079       0.684
15.CON       0.604       1.071       1.479       1.041
16.PST       0.814       1.924       2.966       1.858
17.RLW       1.885       2.787       3.632       2.738
18.OFU       2.789       3.300       3.829       3.330
19.OLE       3.155       3.553       3.964       3.554
20.ELE       3.480       3.896       4.318       3.892
21.PSM       3.733       4.134       4.591       4.168
22.CAT       3.216       4.417       5.461       4.369
23.TRO       3.953       5.123       6.549       5.282
24.OLP       4.614       5.262       5.909       5.295
25.TRD       5.694       6.454       7.250       6.476
26.AIR       5.227       7.182       9.276       7.320
27.FIN       4.919       7.301      10.175       7.640
28.COA       6.581       7.818       9.125       7.878
29.TMS       5.668       8.530      11.086       8.591
30.CHM       8.071       9.299      10.534       9.319
31.TRK       7.031       9.367      11.851       9.577
32.MAR       9.485      13.072      16.424      13.049
33.GAS       4.437      13.183      22.311      13.462
34.FME      14.794      16.617      18.382      16.642
35.NFM      23.299      26.398      29.281      26.263

The tabular report is helpful, but it does not convey the results as immediately as a picture. GNUPLOT's errorbar plot format is a convenient graphical format for portraying this information. The libinclude interface to GNUPLOT does not support this type of plot, so the continuation of the program produces the GNUPLOT command and data files before invoking the GNUPLOT program:

*       Write out a GNUPLOT file to generate a chart of the results:

file kplt /ex3.gnu/; put kplt; kplt.lw=0;
put "reset"/;
put 'set title "Sectoral Impacts with Quartiles"'/;
put "set linestyle 1 lt 8 lw 1 pt 8 ps 0.5"/;
put "set grid"/;
put 'set ylabel "% change"'/;
put "set xzeroaxis"/;
put "set bmargin 4"/;
put "set xlabel 'sector'"/;
put 'set xrange [1:',card(i),']'/;
put 'set xtics rotate (';
loop(ki, loop(i$imap(ki,i),     put '\'/' "',,'" ',ord(ki):0,',';)); 
put @( ')'/;
put "plot 'ex3.dat' notitle with errorbars ls 1"/;
file kpltdata /ex3.dat/; put kpltdata;; kpltdata.nw=14; kpltdata.nd=6;
loop(ki, loop(i$imap(ki,i),  put ord(ki):0,qvalue(i,"mean"), qvalue(i,"q25"), qvalue(i,"q75")/;));
execute 'wgnupl32 ex3.gnu -';

Example 4: Repeated computation of percentiles within a loop

$title GAMS Program Illustrates Repeated Computation of Percentiles 

Dear Prof Rutherford,

I am sorry to bother you again. If you recall I was having difficulty
with the ‘rank’ function. I wasn’t able to run it in loop and use the
95th percentile value as a constraint in the next run of the loop,
while also retaining a value of the 95th percentile for every
iteration of the loop. [model below].

In the loop $libinclude rank call I ask GAMS to place the ranked
values in pct, it only runs for 1 iteration before the ‘user error’ in
rank. Whereas if I ask gams to put the ranked values in pct2, it runs
for 2 iterations before saying ‘user error’.

This must mean that the rank function only allows pct to be
over-written once, after which it ‘fills up’ and generates a user
error? How can I over come this?

In your last email you though the problem was that the original values
are being over written. So I introduced xparam95(iter). I think rank
does not allow them to be overwritten.

I am only a novice and have been stuck on this for sometime, your help
will be invaluable.

Kind regards,


set iter        "Iterations" /iter1*iter10/,
    week        "Weeks in the year" /1*52/,
    percentile  "Percentiles (all of them)" /1*100/,
    pctl(percentile) "Percentiles to be computed" /50, 75, 80, 95/;

    z(week)     "Values to be sorted",
    rnk(week)   "Rank values",
    pct(*)      "Percentiles to be computed (input) and those values (output)",
    pct0(*)     "Percentiles to be computed",
    pctval(iter,*)  "Percentile values in successive iterations";

*  Generate a "permanent copy" of the percentiles to be computed.  
*  This will be used to initialize pct before each call to rank;

pct0(percentile)$pctl(percentile) = ord(percentile);

*  Assume that a model solution delivers the values;

z(week) = uniform(0,1);

*  Assign the percentile values to be computed here:

pct(pctl) = pct0(pctl);

display "Here are the INPUT values of PCT0 and PCT prior to the call to rank:", pct0, pct;

$libinclude rank z week rnk pct

display "Here are the values of PCT0 and PCT after the call to rank:", pct0, 
    "Note that rank has changed the OUTPUT value of pct", pct;

*  Do several iterations, computing percentiles in each step:


*  Substitute a call to the NLP solver by a call to the random
*  number generator.  In many applications, this substitution
*  produces profoundly more sensible results.
*                 solve catchment using nlp maximizing max;

   z(week) = uniform(0,1);

*  If you want to retrieve percentile values, you need to reassign
*  the percentiles that you wish to retrieve at this point in the
*  program.  If pct() were not reassigned at this point, the INPUT
*  values would correspond to the OUTPUTs from the previous call.

   pct(pctl) = pct0(pctl);

$libinclude rank z week rnk pct

   pctval(iter,pctl) = pct(pctl);


display pctval;


----     58 Here are the INPUT values of PCT0 and PCT prior to the call to rank:

----     58 PARAMETER pct0  Percentiles to be computed

50 50.000,    75 75.000,    80 80.000,    95 95.000

----     58 PARAMETER pct  Percentiles to be computed (input) and those values (output)

50 50.000,    75 75.000,    80 80.000,    95 95.000

----    153 Here are the values of PCT0 and PCT after the call to rank:

----    153 PARAMETER pct0  Percentiles to be computed

50 50.000,    75 75.000,    80 80.000,    95 95.000

----    153 Note that rank has changed the OUTPUT value of pct

----    153 PARAMETER pct  Percentiles to be computed (input) and those values (output)

50 0.424,    75 0.662,    80 0.712,    95 0.864

----    273 PARAMETER pctval  Percentile values in successive iterations

                50          75          80          95

iter1        0.345       0.594       0.638       0.922
iter2        0.385       0.633       0.672       0.941
iter3        0.474       0.705       0.766       0.962
iter4        0.627       0.796       0.823       0.978
iter5        0.428       0.682       0.793       0.904
iter6        0.422       0.690       0.729       0.958
iter7        0.558       0.716       0.756       0.902
iter8        0.451       0.638       0.726       0.942
iter9        0.464       0.704       0.755       0.916
iter10       0.564       0.805       0.831       0.974

Example 5: Use GDXRANK generating percentiles for heterogenous households.

$title  Percentile ranking of household expenditure data with heterogenous household size

set     h /0*100/;

parameter       y(h)            "Aggregate expenditure associated with household type h",
                n(h)            "Number of persons associated with household type h",
                ypc(h)          "Per-capita expenditure of household type h"
                rank(h)         "Rank of household in per-capita expenditure";

*       Assigne some random values:

y(h) = uniform(0.2,1.2);
n(h) = uniform(1,6);
ypc(h) = y(h) / n(h);

*       Assign ranks to household based on per-capita expenditures:

$libinclude rank ypc h rank

*       Now determine percentile ranking of the households taking into account
*       differences in numbers of members and household representation:

set     r       "Temporary set used for ranking" /r0*r100/;

parameter       pcttmp(r)       "Temporary array for computing percentiles",
                pct(h)          "Percentile rankings for households";

set r0(r) /r0/;

*       First, create an array with households assigned 

loop((r0(r),h),         pcttmp(r+(rank(h)-1)) = n(h););

loop(r,                 pcttmp(r) = pcttmp(r) + pcttmp(r-1););

                        pcttmp(r) = pcttmp(r) / sum(h, n(h));

loop((r0(r),h),         pct(h) = pcttmp(r+(rank(h)-1)););

parameter       ranking         "Ranking of households and expenditures";

loop((r0(r),h),         ranking(r+(rank(h)-1),h,"n") = n(h);
                        ranking(r+(rank(h)-1),h,"ypc") = ypc(h);
                        ranking(r+(rank(h)-1),h,"pct") = pct(h); );

display ranking;


                         n         ypc         pct
        r0  .43        5.662       0.044       0.018
        r1  .8         5.682       0.047       0.036
        r2  .58        4.477       0.052       0.050
        r3  .14        5.742       0.058       0.068
        r4  .55        4.815       0.063       0.083
        r5  .65        3.702       0.063       0.094
        r6  .61        5.880       0.064       0.113
        r7  .54        4.463       0.064       0.127
        r98 .62        1.134       0.640       0.991
        r99 .10        1.673       0.716       0.997
        r100.73        1.053       1.076       1.000