solving collections of models that have similar structure but modified data

Question

I want to to solve several instances of a model with modified input data. This varied input data usually only affects the coefficients; but, it might affect the number of variables and constraints as well. Ideally, the varied instance of the model should use a warm start from the last solution to reduce run-times.

I am a bit familiar with GMP functions. However, I am not sure how to effectively implement such procedure.

I was wondering if AIMMS has something similar to Gather-Update-Solve-Scatter (GUSS) syntax in GAMS.

I would appreciate your comments :)

Marcel Hunting · Accepted Answer

Hi ​@afattahi. How to warm start a model depends on the model type (LP or MIP). For LP, if the solver isusing the primal or dual simplex algorithm thenyou should set the Solvers General option 'accept basis’ to 1 (AIMMS will store the basis automatically by default). The barrier algorithm cannot efficiently usea warm start. You could use barrier for the first solve and then use the primal or dualsimplex algorithm for thesuccessive solves (assuming barrier does a crossover step, which is normally done by default).For MIP you can provide a MIP start using the procedureGMP::Solution::SetMIPStartFlag. Note that using a MIP start will not help much if the "proving optimality” part takes the most time.How to efficiently do model updates usingGMP functionality depends on your AIMMS project. Please send an email to our support to discuss in detail.I am not familiar with theGUSS functionality of AIMMSbut, after taking a glance at it, it seems that AIMMS does not have something similar.

afattahi · Answer

Hi ​@Marcel Hunting,Thanks for your explanation.I am dealing with a largeLP and usually only aim for the barrier solution. However, if it can reduce the runtime of successive GMPs through warm start, it would make sense to have the crossover enabled for the first run.I (or my colleague) will send an email to support for further discussing this.

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