Better optimization of nonlinear uncertain systems (BONUS): A new algorithm for stochastic programming using reweighting through kernel density estimation

Better optimization of nonlinear uncertain systems (BONUS): A new algorithm for stochastic programming using reweighting through kernel density estimation

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Article ID: iaor20051554
Country: Netherlands
Volume: 132
Issue: 1
Start Page Number: 47
End Page Number: 68
Publication Date: Nov 2004
Journal: Annals of Operations Research
Authors: ,
Keywords: programming: probabilistic
Abstract:

A new nonlinear programming algorithm is proposed for stochastic programming problems. This method relies on sampling to estimate the probabilistic objective function and constraints. The computational burden of excessive model calculations for determining the search direction is bypassed through a reweighting method using Kernel Density Estimation. The improvements accomplished by this algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS) are presented through two real world case studies involving parameter design for off-line quality-control of a chemical reactor, and optimal capacity expansion for electric utilities in uncertain markets.

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