Global optimization of stochastic multivariable functions

This paper presents a new methodology for global optimization of a stochastic multivariable functions subject to stochastic, possibly nonlinear constraints. Least squares parametric estimation is applied as an intermediate step in the stochastic optimizer which then uses a special transformation to capture the global optima estimate. A comparative study of implementing estimation in polynomial least squares versus spline fitting is also presented, together with an illustrative example.<
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