The program, called GENOUD (GENetic Optimization Using Derivatives), effectively solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized.
When a statistical model's estimating function (for example, a log-likelihood) is nonlinear in the model's parameters, the function to be optimized will usually not be globally concave and may contain irregularities such as saddlepoints or discontinuous jumps.
This enables firms to exploit control theoretic techniques for Business Performance Management.
Keywords: Derivative-driven analysis; Window-based regression; Gas turbine prognostics; Condition based maintenance (search for similar items in Econ Papers) Date: 2017 References: View references in Econ Papers View complete reference list from Cit Ec Citations View citations in Econ Papers (1) Track citations by RSS feed Downloads: (external link) Full text for Science Direct subscribers only Related works: This item may be available elsewhere in Econ Papers: Search for items with the same title.
We discuss the theoretical basis for expecting GENOUD to have a high probability of finding global optima.
We conduct Monte Carlo experiments using scalar Normal mixture densities to illustrate this capability.
The elastic domain is defined by a level-set function, and the evolution of the domain is obtained by moving the level-set along a velocity field using a transport equation.
The velocity field is the largest decreasing direction of the shape derivative that satisfies a certain regularity requirement and the computation of the shape derivative is based on a volume formulation.