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Doc Miscellaneous docstring fixes and updates (#944)
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@rasolca
rasolca committed Sep 13, 2023
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20 changes: 10 additions & 10 deletions master/band__to__tridiag_8h.html
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</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd>mat_a has a square size, </dd>
<dl class="section pre"><dt>Precondition</dt><dd><code>mat_a</code> is not distributed </dd>
<dd>
mat_a has a square block size, </dd>
<code>mat_a</code> has size (N x N) </dd>
<dd>
band_size is a divisor of mat_a.blockSize().cols(), and band_size &gt;= 2 </dd>
<code>mat_a</code> has blocksize (NB x NB) </dd>
<dd>
mat_a is not distributed, </dd>
<code>mat_a</code> has tilesize (NB x NB)</dd>
<dd>
mat_a has equal tile and block sizes. </dd></dl>
<code>band_size</code> is a divisor of <code>mat_a.blockSize().cols()</code>, and <code>band_size</code> &gt;= 2 </dd></dl>

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</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd>mat_a has a square size, </dd>
<dl class="section pre"><dt>Precondition</dt><dd><code>mat_a</code> is distributed according to <code>grid</code> </dd>
<dd>
mat_a has a square block size, </dd>
<code>mat_a</code> has size (N x N) </dd>
<dd>
band_size is a divisor of mat_a.blockSize().cols() and band_size &gt;= 2, </dd>
<code>mat_a</code> has blocksize (NB x NB) </dd>
<dd>
mat_a is distributed according to grid, </dd>
<code>mat_a</code> has tilesize (NB x NB)</dd>
<dd>
mat_a has equal tile and block sizes. </dd></dl>
<code>band_size</code> is a divisor of <code>mat_a.blockSize().cols()</code>, and <code>band_size</code> &gt;= 2 </dd></dl>

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108 changes: 54 additions & 54 deletions master/band__to__tridiag_8h_source.html
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165 changes: 159 additions & 6 deletions master/bt__band__to__tridiag_8h.html
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:ac93781339b28227cf04add9d24d77340"><td class="memTemplParams" colspan="2"><a id="ac93781339b28227cf04add9d24d77340"></a>
template&lt;Backend B, Device D, class T &gt; </td></tr>
<tr class="memitem:ac93781339b28227cf04add9d24d77340"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>dlaf::eigensolver::internal::bt_band_to_tridiagonal</b> (const SizeType band_size, matrix::Matrix&lt; T, D &gt; &amp;mat_e, matrix::Matrix&lt; const T, Device::CPU &gt; &amp;mat_hh)</td></tr>
<tr class="memitem:ac93781339b28227cf04add9d24d77340"><td class="memTemplParams" colspan="2">template&lt;Backend B, Device D, class T &gt; </td></tr>
<tr class="memitem:ac93781339b28227cf04add9d24d77340"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="bt__band__to__tridiag_8h.html#ac93781339b28227cf04add9d24d77340">dlaf::eigensolver::internal::bt_band_to_tridiagonal</a> (const SizeType band_size, matrix::Matrix&lt; T, D &gt; &amp;mat_e, matrix::Matrix&lt; const T, Device::CPU &gt; &amp;mat_hh)</td></tr>
<tr class="separator:ac93781339b28227cf04add9d24d77340"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a479dac42130a879850a15d876c88d5f9"><td class="memTemplParams" colspan="2"><a id="a479dac42130a879850a15d876c88d5f9"></a>
template&lt;Backend B, Device D, class T &gt; </td></tr>
<tr class="memitem:a479dac42130a879850a15d876c88d5f9"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>dlaf::eigensolver::internal::bt_band_to_tridiagonal</b> (comm::CommunicatorGrid grid, const SizeType band_size, matrix::Matrix&lt; T, D &gt; &amp;mat_e, matrix::Matrix&lt; const T, Device::CPU &gt; &amp;mat_hh)</td></tr>
<tr class="memitem:a479dac42130a879850a15d876c88d5f9"><td class="memTemplParams" colspan="2">template&lt;Backend B, Device D, class T &gt; </td></tr>
<tr class="memitem:a479dac42130a879850a15d876c88d5f9"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="bt__band__to__tridiag_8h.html#a479dac42130a879850a15d876c88d5f9">dlaf::eigensolver::internal::bt_band_to_tridiagonal</a> (comm::CommunicatorGrid grid, const SizeType band_size, matrix::Matrix&lt; T, D &gt; &amp;mat_e, matrix::Matrix&lt; const T, Device::CPU &gt; &amp;mat_hh)</td></tr>
<tr class="separator:a479dac42130a879850a15d876c88d5f9"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<h2 class="groupheader">Function Documentation</h2>
<a id="a479dac42130a879850a15d876c88d5f9"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a479dac42130a879850a15d876c88d5f9">&#9670;&nbsp;</a></span>bt_band_to_tridiagonal() <span class="overload">[1/2]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;Backend B, Device D, class T &gt; </div>
<table class="memname">
<tr>
<td class="memname">void dlaf::eigensolver::internal::bt_band_to_tridiagonal </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classdlaf_1_1comm_1_1_communicator_grid.html">comm::CommunicatorGrid</a>&#160;</td>
<td class="paramname"><em>grid</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const SizeType&#160;</td>
<td class="paramname"><em>band_size</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classdlaf_1_1matrix_1_1_matrix.html">matrix::Matrix</a>&lt; T, D &gt; &amp;&#160;</td>
<td class="paramname"><em>mat_e</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classdlaf_1_1matrix_1_1_matrix.html">matrix::Matrix</a>&lt; const T, Device::CPU &gt; &amp;&#160;</td>
<td class="paramname"><em>mat_hh</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<p>Eigenvalue back-transformation implementation, which applies the inverse of the transformation used to get a tridiagonal matrix from a band one.</p>
<p>It computes E -= V T V* E, applying to a general matrix E the inverse of the transformation described by the reflectors in V (block-wise, so T represents the T factor which embeds the information about taus), which are the ones used to transform a band matrix to a tridiagonal matrix.</p>
<p>In particular, V and T are obtained using data about reflectors and taus passed via <code>mat_hh</code> where they are stored using following compact representation</p>
<p>compact extended AT BT CT DT 1 0 0 0 A1 B1 C1 D1 A1 1 0 0 A2 B2 C2 D2 A2 B1 1 0 A3 B3 C3 D3 A3 B2 C1 1 0 B3 C2 D1 0 0 C3 D2 0 0 0 D3</p>
<p>where A, B, C and D refers to distinct reflectors, with their components numbered and their taus identified by the letter T.</p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">mat_hh</td><td>matrix containing reflectors together with taus (compact form see representation above) </td></tr>
</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd><code>mat_hh</code> is distributed according to <code>grid</code> </dd>
<dd>
<code>mat_hh</code> has size (N x N) </dd>
<dd>
<code>mat_hh</code> has blocksize (NB x NB) </dd>
<dd>
<code>mat_hh</code> has tilesize (NB x NB)</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">mat_e</td><td>matrix to which the inverse transformation is applied to </td></tr>
</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd><code>mat_e</code> is distributed according to <code>grid</code> </dd>
<dd>
<code>mat_e</code> has size (N x M) </dd>
<dd>
<code>mat_e</code> has blocksize (NB x MB) </dd>
<dd>
<code>mat_e</code> has tilesize (NB x MB)</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">band_size</td><td>size of the reflectors (normal one, not constrained by any matrix size limit) </td></tr>
</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd><code>band_size</code> is a divisor of <code>mat_hh.blockSize().cols()</code> </dd></dl>

</div>
</div>
<a id="ac93781339b28227cf04add9d24d77340"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac93781339b28227cf04add9d24d77340">&#9670;&nbsp;</a></span>bt_band_to_tridiagonal() <span class="overload">[2/2]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;Backend B, Device D, class T &gt; </div>
<table class="memname">
<tr>
<td class="memname">void dlaf::eigensolver::internal::bt_band_to_tridiagonal </td>
<td>(</td>
<td class="paramtype">const SizeType&#160;</td>
<td class="paramname"><em>band_size</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classdlaf_1_1matrix_1_1_matrix.html">matrix::Matrix</a>&lt; T, D &gt; &amp;&#160;</td>
<td class="paramname"><em>mat_e</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classdlaf_1_1matrix_1_1_matrix.html">matrix::Matrix</a>&lt; const T, Device::CPU &gt; &amp;&#160;</td>
<td class="paramname"><em>mat_hh</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<p>Eigenvalue back-transformation implementation on local memory, which applies the inverse of the transformation used to get a tridiagonal matrix from a band one.</p>
<p>It computes E -= V T V* E, applying to a general matrix E the inverse of the transformation described by the reflectors in V (block-wise, so T represents the T factor which embeds the information about taus), which are the ones used to transform a band matrix to a tridiagonal matrix.</p>
<p>In particular, V and T are obtained using data about reflectors and taus passed via <code>mat_hh</code> where they are stored using following compact representation</p>
<p>compact extended AT BT CT DT 1 0 0 0 A1 B1 C1 D1 A1 1 0 0 A2 B2 C2 D2 A2 B1 1 0 A3 B3 C3 D3 A3 B2 C1 1 0 B3 C2 D1 0 0 C3 D2 0 0 0 D3</p>
<p>where A, B, C and D refers to distinct reflectors, with their components numbered and their taus identified by the letter T.</p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">mat_hh</td><td>matrix containing reflectors together with taus (compact form see representation above) </td></tr>
</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd><code>mat_hh</code> is not distributed </dd>
<dd>
<code>mat_hh</code> has size (N x N) </dd>
<dd>
<code>mat_hh</code> has blocksize (NB x NB) </dd>
<dd>
<code>mat_hh</code> has tilesize (NB x NB)</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">mat_e</td><td>matrix to which the inverse transformation is applied to </td></tr>
</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd><code>mat_e</code> is not distributed </dd>
<dd>
<code>mat_e</code> has size (N x M) </dd>
<dd>
<code>mat_e</code> has blocksize (NB x MB) </dd>
<dd>
<code>mat_e</code> has tilesize (NB x MB)</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">band_size</td><td>size of the reflectors (normal one, not constrained by any matrix size limit) </td></tr>
</table>
</dd>
</dl>
<dl class="section pre"><dt>Precondition</dt><dd><code>band_size</code> is a divisor of <code>mat_hh.blockSize().cols()</code> </dd></dl>

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