lpdf$optnewton

<pre>
lpdf$optnewton()
</pre><p>This optimizes the coefficient vector <code>coeff</code> using Newton's Method.  
It currently is designed only for quadratic <code><a rd-options="" href="/link/lpdf?package=outerbase&version=0.1.0" data-mini-rdoc="outerbase::lpdf">lpdf</a></code> instances.  
It should take a single step.</p>

High-dimensional regression using outer product models.  Research on the methods is currently under investigation and published resources will be posted as they are available.  As the method is new, the website is the best resource for understanding the principals. Some of the core ideas are based on Plumlee and coauthors' work on analysis of grid-structured  experiments described in Plumlee (2014) <doi:10.1080/01621459.2014.900250> and  Plumlee, Erickson, Ankenman, Lawrence (2021) <doi:10.1093/biomet/asaa084>.  Some additional textbooks for additional information on Gaussian processes are Rasmussen and Williams (2005) <doi:10.7551/mitpress/3206.001.0001> and  Gramacy (2022) <doi:10.1201/9780367815493>.

Matthew Plumlee

outerbase

Outer Product Regression

lpdf$optnewton function

<pre>
lpdf$optnewton()
</pre><p>This optimizes the coefficient vector <code>coeff</code> using Newton's Method.  
It currently is designed only for quadratic <code><a rd-options='' href='lpdf'>lpdf</a></code> instances.  
It should take a single step.</p>

lpdf$optnewton: Optimization via Newton's Method

Description

Arguments

Value

See Also