One of the many complications that arise when solving non-linear optimization problems (NLPs) is that the presence of extrinsic indexes may introduce computational inefficiencies when array results within your model are indexed by the extrinsic dimensions, even though any single optimization needs only a single slice of the result. What happens is that while one NLP is being solved, array results are being computed and propagated within the internals of your model, where only a single slice of the result is actually used by the NLP being solved. Even though it may have required substantial computation to obtain, the remainder of the array ends up being thrown away without being used.

The «SetContext» parameter of DefineOptimization provides a way to avoid this inefficiency.

The inefficiency only arises when you are solving NLPs, so if your problem is linear or quadratic, there is no need to make use of «SetContext». Also, it only arises when extrinsic indexes are present -- in other words, when DefineOptimization found that extra indexes were present in the objective or constraints and thus array-abstracted over these extra (extrinsic) indexes, returning an array of optimization problems. Thus, if you are dealing with a single NLP, once again there is no need to worry about this. One situation where it does arise naturally is the case where you are performing an optimization that depends on uncertain quantities, such that in the uncertainty mode you end up with a Monte Carlo sample of NLPs. If you don't utilize «SetContext» appropriately, the time-complexity of your model will scale quadratically with sample size rather than linearly.

Using SetContext to efficiently solve NLPs

How SetContext Works