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<div class="section" id="torch-geqrf">
<h1>torch.geqrf<a class="headerlink" href="#torch-geqrf" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.geqrf">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">geqrf</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">out=None) -> (Tensor</em>, <em class="sig-param">Tensor</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.geqrf" title="Permalink to this definition">¶</a></dt>
<dd><p>This is a low-level function for calling LAPACK directly. This function
returns a namedtuple (a, tau) as defined in <a class="reference external" href="https://software.intel.com/en-us/node/521004">LAPACK documentation for geqrf</a> .</p>
<p>You’ll generally want to use <a class="reference internal" href="torch.qr.html#torch.qr" title="torch.qr"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.qr()</span></code></a> instead.</p>
<p>Computes a QR decomposition of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, but without constructing
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>Q</mi></mrow><annotation encoding="application/x-tex">Q</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8777699999999999em;vertical-align:-0.19444em;"></span><span class="mord mathnormal">Q</span></span></span></span>
</span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>R</mi></mrow><annotation encoding="application/x-tex">R</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span></span></span></span>
</span> as explicit separate matrices.</p>
<p>Rather, this directly calls the underlying LAPACK function <cite>?geqrf</cite>
which produces a sequence of ‘elementary reflectors’.</p>
<p>See <a class="reference external" href="https://software.intel.com/en-us/node/521004">LAPACK documentation for geqrf</a> for further details.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input matrix</p></li>
<li><p><strong>out</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</em></a><em>, </em><em>optional</em>) – the output tuple of (Tensor, Tensor)</p></li>
</ul>
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</dl>
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