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RegressionFactory

Expander Functions for Generating Full Gradient and Hessian from Single-Slot and Multi-Slot Base Distributions

Installation

About

The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.

Key Metrics

Version 0.7.4
Published 2020-10-26 1330 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Alireza S. Mahani

Authors

Alireza S. Mahani
Mansour T.A. Sharabiani

Material

ChangeLog
Reference manual
Package source

Vignettes

Expander Framework for Generating High-Dimensional GLM Gradient and Hessian from Low-Dimensional Base Distributions: R Package MfUSampler

macOS

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arm64

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x86_64

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x86_64

Windows

r-devel

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Old Sources

RegressionFactory archive

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