Improved sampling strategies for ensemble-based optimization

We are concerned with the efficiency of stochastic gradient estimation methods for large-scale nonlinear optimization in the presence of uncertainty. These methods aim to estimate an approximate gradient from a limited number of random input vector samples and corresponding objective function values. Ensemble methods usually employ Gaussian...

Generation of a Pareto front for a bi-objective water flooding optimization problem using approximate ensemble gradients

Conflicting objectives are frequently encountered in most real-world problems. When dealing with conflicting objectives, decision makers prefer to obtain a range of possible optimal solutions from which to choose. In theory, methods exists that can produce a range of possible solutions, some of which are “Pareto Optimal”. The application of...

A stochastic simplex approximate gradient (StoSAG) for optimization under uncertainty

We consider a technique to estimate an approximate gradient using an ensemble of randomly chosen control vectors, known as Ensemble Optimization (EnOpt) in the oil and gas reservoir simulation community. In particular, we address how to obtain accurate approximate gradients when the underlying numerical mod- els contain uncertain parameters...