GAMS — The Solver Manuals - Available Software

204 CPLEX 11
1 Display standard minimal reporting
2 Display standard report plus parameter settings being tried
3 Display exhaustive report and log
tuningmeasure (integer)
Controls the measure for evaluating progress when a suite of models is being tuned. Choices are mean
average and minmax of time to compare different parameter sets over a suite of models
(default = 1)
1 mean average
2 minmax
tuningrepeat (integer)
Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The
problem is perturbed automatically by Cplex permuting its rows and columns. This repetition is helpful
when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust
tuning results. This parameter applies to only one problem in a tuning session.
(default = 1)
tuningtilim (real)
Sets a time limit per model and per test set (that is, suite of models).
As an example, suppose that you want to spend an overall amount of time tuning the parameter settings
for a given model, say, 2000 seconds. Also suppose that you want Cplex to make multiple attempts within
that overall time limit to tune the parameter settings for your model. Suppose further that you want to set
a time limit on each of those attempts, say, 200 seconds per attempt. In this case you need to specify an
overall time limit of 2000 using GAMS option reslim or Cplex option tilim and tuningtilim to 200.
(default = 0.2*GAMS ResLim)
userincbcall (string)
The GAMS command line (minus the GAMS executable name) to call the incumbent checking routine. The
incumbent is rejected if the GAMS program terminates normally. In case of a compilation or execution
error, the incumbent is accepted.
varsel (integer)
This option is used to set the rule for selecting the branching variable at the node which has been selected
for branching. The default value of 0 allows Cplex to select the best rule based on the problem and its
progress.
(default = 0)
-1 Branch on variable with minimum infeasibility. This rule may lead more quickly to a first integer
feasible solution, but will usually be slower overall to reach the optimal integer solution.
0 Branch variable automatically selected
1 Branch on variable with maximum infeasibility. This rule forces larger changes earlier in the tree,
which tends to produce faster overall times to reach the optimal integer solution.
2 Branch based on pseudo costs. Generally, the pseudo-cost setting is more effective when the problem
contains complex trade-offs and the dual values have an economic interpretation.
3 Strong Branching. This setting causes variable selection based on partially solving a number of subproblems
with tentative branches to see which branch is most promising. This is often effective on
large, difficult problems.
4 Branch based on pseudo reduced costs