Hi all,
I’m new in the group. I began by follow most common topics in the group. So I think I need more help for the problem I’m facing to. I’m a Ph.D. student and I’m building an economic model with 96 accounts for the social accounting matrix (SAM). The model has 2828 equations and 2918 variables. In use the version 24.5.6 of gams. The solver is CONOPT in an NLP maximization problem. I have some preoccupations:
First my model doesn’t reproduce the initial data base of SAM. In a such a situation the report summary is
**** REPORT SUMMARY : 0 NONOPT
1499 INFEASIBLE (INFES)
SUM 1.414
MAX 0.001
MEAN 9.4338E-4
0 UNBOUNDED
0 ERRORS
The process windows shows infeasible solution. Reduced gradient less than tolerance. Normal completion is 1 and locally infeasible is at 5. I scaled some variables and all have been initialized to that of the SAM. Some of them are fixed using .fx (I don’t know if there is a rule for choosing variables that may be fixed). I have the impression that bad values I got for the variables are due to the infeasibility and if it is the case how can I overcome that to problem.
Second I sometime tried to make simulations even with those errors but I realized that nothing didn’t change. (Is it still due to problem of infeasibility and sometime of errors that I get in report summary?)
I could send the gms file if necessary

If you use a benchmard data set, you should be able to reproduce this running your economic model. This is how you can debug your model:

If you inititalize all your variables with the benchmark data (e.g. prices equal to 1, activitiy levels to 1, INCOME.L = SAM(‘LAB’, ‘HH’) + SAM(‘CAP’, ‘HH’) + …).

Set the iteration limit to zero (e.g. mymodel.iterlim = 0), and

run the model, it should stop immediately and GAMS should have found the solution.

If not, either your data is wrong, the starting values aren’t set correctly, or the equations are wrong).

If you go in the listing file, you will find the equations listings. If there is an infeasible in one of the equations, this means that this equation has the wrong starting values (or no starting values assigned to), or the equation is wrong.

Correct every equation with an infeasible bigger then 1E-7 and you should find the benchmark data as a solution.

Simplified:

set x /dema , demb, sup/;
table data(x,*)
Agr
demA 100
demB 50
sup 200;
variables
DA demand a
DB demand b
S supply
DUMMY ;
parameter c /50/;
equations
market_clearing Market clearing
dummy_eq Just a dummy equation to get an optimization model;
market_clearing..
S =G= DA + DB +c;
dummy_eq..
DUMMY =E= 1;
model demandsupply /all/;
demandsupply.iterlim = 0;
* This will cause infeasibilities
solve demandsupply using nlp minimizing dummy;
* Initialize the variables
S.l = data("Supply", "agr");
DUMMY.l = 1;
DA.l = data("dema", "agr");
DB.l = data("demb", "agr");
solve demandsupply using nlp minimizing dummy;
* Don't forget to reset your iteration limit
demandsupply.iterlim = 10000;

The first solve will give you this in the equations listing:

---- market_clearing =G= Market clearing
market_clearing.. - da - db + s =G= 50 ; (LHS = 0, INFES = 50 ****)
---- dummy_eq =E= Just a dummy equation to get an optimization model
dummy_eq.. dummy =E= 1 ; (LHS = 0, INFES = 1 ****)

The second propely initialized model this:

---- market_clearing =G= Market clearing
market_clearing.. - da - db + s =G= 50 ; (LHS = 50)
---- dummy_eq =E= Just a dummy equation to get an optimization model
dummy_eq.. dummy =E= 1 ; (LHS = 1)

Hi Renger. I’m really grateful for your reply. It is very interesting especially the examples. I’m going to follow your advice and I hope the model will reproduce the benchmark data. Thank you very much

Hello Renger
I tried options you recommended me even though I haven’t got a right solution. However, I read other posts which helped me notably the artificial method described in this forum. By this one I dealt with infeasibilities and the benchmark situation has been successfully reproduced. Now, I’m confused since the log file indicates
** optimal solution. Reduced gradient less than tolerance.

In a given post It has been advice to increase Rtmaxv to 1E15 for example. I followed that but the problem remains.
So far because in report summary I get 0 everywhere (as indicated below), I tried a simulation but no value doesn’t change. I got the same values to that of the benchmark. I feel that, this situation remains until the problem of reduced gradient will have been solved. The summary of listing file is the following

S O L V E S U M M A R Y
MODEL megcdycam OBJECTIVE CC
TYPE NLP DIRECTION MAXIMIZE
SOLVER GAMSCHK FROM LINE 694

**** SOLVER STATUS 1 Normal Completion
**** MODEL STATUS 2 Locally Optimal
**** OBJECTIVE VALUE 9098.0000

** Optimal solution. Reduced gradient less than tolerance.
**** REPORT SUMMARY : 0 NONOPT
0 INFEASIBLE
0 UNBOUNDED
0 ERRORS

GAMS 25.0.3 r65947 Released Mar 21, 2018 WEX-WEI x86 64bit/MS Windows 06/08/18 11:07:28 Page 6
A Recursive-Dynamic Standard CGE Model (DYNCGE,SEQ=410)

So my question is what is the process to overcome that issue of reduced gradient? And if I can keep that message what can I do in other to realize simulations?

Because of the size of the model I don’t know if it’s necessary to share the gms file. In contrary I attached the log file.