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Bayesian inference provides a probabilistic framework for combining prior knowledge with mathematical models and observational data. Characterizing a Bayesian posterior probability distribution can be a computationally challenging undertaking, however, particularly when evaluations of the posterior density are expensive and when the posterior has complex(More)
Particle swarm optimization (PSO) is a population-based, heuristic minimization technique that is based on social behavior. The method has been shown to perform well on a variety of problems including those with nonconvex, nonsmooth objective functions with multiple local minima. However, the method can be computationally expensive in that a large number of(More)
Farmers in regions experiencing water stress or drought conditions can struggle to balance their crop portfolios. Periods of low precipitation often lead to increased, unsustainable reliance on groundwatersupplied irrigation. As a result, regional water management agencies place limits on the amount of water which can be obtained from groundwater, requiring(More)
In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel Search Package), a new software platform which facilitates combining multiple optimization routines into a single, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The framework is designed such that existing optimization source code can(More)
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