Sequential sampling

Given a confidence interval, one can try to find a candidate solution xhat_one such that its optimality gap has this confidence interval. The class SeqSampling in mpisppy.confiden_intervals.seqsampling.py implements three procedures described in [bm2011] and [bpl2012]. It takes as an input a method to generate candidate solutions and options, and its run method returns a xhat_one and a confidence interval on its optimality gap.

There are two stopping criterion supported with names based on the initials of the authors who defined them: “BM” and “BPL”.

Examples of use with the farmer problem and several options can be found in the main of seqsampling.py. The following options dictionaries are illustrated:

• relative Width;

• fixed width, sequential;

• fixed width with stochastic samples.

  The keys used in the options dictionaries are taken directly from the corresponding paper, perhaps abbreviated in an obvious way. For example, the key eps corresponds to epsilon in the papers.

For multi-stage, use multi_seqsampling.py.

Examples

There is sample code for two-stage, sequential sampling in examples.farmer.farmer_seqsampling.py and a bash scrip to test drive it is examples.farmer.farmer_sequential.bash.

There is sample code for multi-stage, sequential sampling in examples.aircond.aircond_seqsampling.py and a bash scrip to test drive it is examples.aircond.aircond_sequential.bash.