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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.

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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.

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  1. Dec 1, 2016. In digital subscriber line systems, spectrum coordination is a powerful technique to improve performance. A typical spectrum coordination algorithm employs.

  2. In recent years, there has been a surge of research into methods for estimating derivatives of performance measures from sample paths of stochastic systems. problems in which infinitesimal perturbation analysis is provably applicable. M ost real-world. conditions for derivatives based on regenerative cycles are given in.

  3. Present the application of neural networks for the prediction and analysis of antitubercular activity of. Oxazolines and Oxazoles derivatives. This study presents techniques based on the development of Single hidden layer neural network SHLFFNN, Gradient Descent Back propagation neural network GDBPNN. Gradient.

  4. STRUCTURAL DYNAMIC. MODEL UPDATING. USING. EIGENSENSITIVITY ANALYSIS. bY. Haeil Jung. A thesis submitted to the University of London for the degree of. Doctor of. derivative with respect to displacement. quite successful, the methods are generally based on the assumption that the test data are.

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