solver:using_lazy_constraints_or_user_cuts

GAMS/CPLEX does currently not support lazy constraint or user cuts. We have not seen convincing examples where these Cplex features make a significant difference in performance. We understand that this might be a chicken and egg problem, therefore we introduced some time ago a way to experiment with these Cplex features. This is not for production models but to see if these features improve performance for your model.

If you (or other users) have good examples where lazy constraints or user cuts make a significant difference that cannot be accomplished by other Cplex features, we will quickly make these features available in GAMS/CPLEX.

Here is what you need to do:

- Enter the user cuts and or lazy constraints as normal equations to your model. Order the equations in a way that the lazy constraints and user curs are last in your model (instead of
`model m /all/`

you can list the names and get the appropriate order of your equations). - Solve your model with a
`cplex.opt`

option file containing:`writelp mymodel.lp`

. You can interrupt the run when Cplex starts the presolve. - Edit the file mymodel.lp. Just in front of the constraints that should be treated as lazy constraints add a line with
`Lazy Constraints`

. Before the constraints that should be used as user cuts, add a line with`User Cuts`

. - Rerun the GAMS model with the solve statement, but change
`cplex.opt`

to`probread mymodel.lp`

. This instructs GAMS/CPLEX not to get the model from GAMS but read the model from the file`mymodel.lp`

. If you have lazy constraint you need to turn off dual reductions by adding option`reduce 1`

. If you have user cuts only you need to turn off non-linear reductions by adding`prelinear 0`

. If you have both,`reduce 1`

will suffice. Since the model passed from GAMS to GAMS/CPLEX might have nothing to do with the model you supplied in`mymodel.lp`

, no solution is returned to GAMS, so you can't do much with that other than experimenting with the performance improvements of the Cplex features Lazy Constraints and User Cuts.

We took the `rotdk`

model from the GAMS model library and automated these steps using awk to add the the line `lazy constraints`

to the LP file.

$title Robust Optimization (ROTDK,SEQ=185) $Ontext Robust Optimization Laguna, M, Applying Robust Optimization to Capacity Expansion of One Location in Telecommunications with Demand Uncertainty. Management Science 44, 11 (1998), 101-110. $Offtext SETS s scenarios / 1*1000/ t time periods / t1*t12 / j components / C001*C010 / alias(t,tt); PARAMETERS di(s,t) increment D(t,s) demand c(j) capacity size p(j) capacity cost mu mean capacity parameter sigma std capacity parameter; mu = 100; sigma = 10; c(j) = round(uniform(1,mu)); p(j) = round(mu + c(j) + uniform(-sigma,sigma)); di(s,t)$(ord(s) <= 0.25*card(s)) = round(normal( 50,10)); di(s,t)$(ord(s) > 0.25*card(s) and ord(s) <= 0.75*card(s)) = round(normal(100,20)); di(s,t)$(ord(s) > 0.75*card(s)) = round(normal(150,40)); d(t,s) = sum(tt$(ord(tt) <= ord(t)), di(s,tt)); *display c,p,di,d; parameters dis(t) discount factor w shortage penalty; dis(t) = power(.86,ord(t)-1); w = 5; variables x(j,t) expansion z(s) max capacity shortage cap(t) installed capacity obj; integer variable x; positive variable z; equations capbal(t) capacity balance dembal(t,s) demand balance objdef; objdef.. obj =e= sum((j,t), dis(t)*p(j)*x(j,t)) + w/card(s)*sum(s, z(s)); capbal(t).. cap(t) =e= cap(t-1) + sum(j, c(j)*x(j,t)); dembal(t,s).. cap(t) + z(s) =g= d(t,s); *model rotdk / all /; * We want dembal as lazy constraints, so let's order the constraints so dembal is last model rotdk / objdef, capbal, dembal /; $echo writelp rotdk.lp > cplex.opt $onecho > cplex.op2 probread rotdkLC.lp reduce 1 $offecho option optcr=0.0,limcol=0,limrow=0; scalar DefaultResUsed, LazyConstraintsResUsed; rotdk.optfile=1; solve rotdk min obj us mip; DefaultResUsed = rotdk.resusd; * Insert the line 'Lazy Constraints' into rotdk.lp before the first occurance of dembal $onecho > x.awk !m && /dembal/ { print "Lazy Constraints"; m=1; } { print $0 } $offecho execute 'awk -f x.awk rotdk.lp > rotdkLC.lp'; * Solve the model defined by the Cplex LP file rotdkLC.lp. * The model passed to Cplex in the solve line below is irrelavant to the actual solve. * We demonstrate this by chaing the model type to rmip and maximize instead of minimize. rotdk.optfile=2; solve rotdk max obj us rmip; LazyConstraintsResUsed = rotdk.resusd; display DefaultResUsed, LazyConstraintsResUsed;

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solver/using_lazy_constraints_or_user_cuts.txt · Last modified: 2010/03/09 13:36 by support