torch_geqrf

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R
using the 'libtorch' library. Also supports low-level tensor operations and
'GPU' acceleration.

Daniel Falbel

torch

Tensors and Neural Networks with 'GPU' Acceleration

Javier Luraschi

Dmitriy Selivanov

Athos Damiani

Christophe Regouby

Krzysztof Joachimiak

Hamada S. Badr

 RStudio

torch_geqrf function

<dl><dt>self</dt>
<dd>(Tensor) the input matrix</dd></dl>

Arguments

This is a low-level function for calling LAPACK directly. This function
returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .
You'll generally want to use <code>torch_qr</code> instead.
Computes a QR decomposition of <code>input</code>, but without constructing
\(Q\) and \(R\) as explicit separate matrices.
Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
which produces a sequence of 'elementary reflectors'.
See <code>LAPACK documentation for geqrf</code>_ for further details.

geqrf(input, out=NULL) -&gt; (Tensor, Tensor) 

Geqrf — torch_geqrf

<dl>

<dt>self</dt>
<dd>(Tensor) the input matrix</dd>

</dl>

This is a low-level function for calling LAPACK directly. This function
returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .
You'll generally want to use <code>torch_qr</code> instead.
Computes a QR decomposition of <code>input</code>, but without constructing
\(Q\) and \(R\) as explicit separate matrices.
Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
which produces a sequence of 'elementary reflectors'.
See <code>LAPACK documentation for geqrf</code>_ for further details.

The Learning Leader's Guide to AI Literacy

torch_geqrf: Geqrf

Description

Usage

Arguments

geqrf(input, out=NULL) -> (Tensor, Tensor)

The Learning Leader's Guide to AI Literacy

Description

Usage

Arguments

geqrf(input, out=NULL) -&gt; (Tensor, Tensor)

geqrf(input, out=NULL) -> (Tensor, Tensor)