Algorithmic Derivatives

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Algorithmic Derivatives

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The GAUSS AD 1.0 module is an application program for generating GAUSS procedures for computing algorithmic derivatives. A major achievement of AD is improved accuracy for optimization. Numerical derivatives invariably produce a loss of precision. Th...
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Algorithmic Derivatives

The GAUSS AD 1.0 module is an application program for generating GAUSS procedures for computing algorithmic derivatives. A major achievement of AD is improved accuracy for optimization. Numerical derivatives invariably produce a loss of precision. The loss of precision is greater for standard errors than it is for estimates.

At the default tolerance, Constrained Maximum Likelihood (CML) and Maximum Likelihood (Maxlik) can be expected generally to have four or five places of accuracy, whereas standard errors will have about two places. Accuracy essentially doubles with AD. AD works independently of any application to improve derivatives, and it can be used with any application that uses derivatives.

For some types of optimization problems, convergence is accelerated. Iterations are faster and fewer of them are needed to achieve convergence. The types of problems that will see the most improvement are those with a large amount of computation.

Constrained Maximum Likelihood 2.0.6+ and Maximum Likelihood 5.0.7+ have been updated to improve speed with AD.

Platforms: Windows, Mac and Linux.

Requirements: GAUSS/GAUSS Engine/GAUSS Light v6.0 or higher.

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4 + 8 = enter the result ex:(3 + 2 = 5)



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