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<div class="title">ConservativeSparseSparseProduct.h</div> </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">// This file is part of Eigen, a lightweight C++ template library</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// for linear algebra.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">//</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr></span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment">//</span></div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment">// This Source Code Form is subject to the terms of the Mozilla</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment">// Public License v. 2.0. If a copy of the MPL was not distributed</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment">// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"></span><span class="preprocessor">#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">namespace </span>Eigen { </div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">static</span> <span class="keywordtype">void</span> conservative_sparse_sparse_product_impl(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> remove_all<Lhs>::type::Scalar Scalar;</div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> remove_all<Lhs>::type::Index Index;</div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="comment">// make sure to call innerSize/outerSize since we fake the storage order.</span></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  Index rows = lhs.innerSize();</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  Index cols = rhs.outerSize();</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  eigen_assert(lhs.outerSize() == rhs.innerSize());</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  std::vector<bool> mask(rows,<span class="keyword">false</span>);</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  Matrix<Scalar,Dynamic,1> values(rows);</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  Matrix<Index,Dynamic,1> indices(rows);</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// estimate the number of non zero entries</span></div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="comment">// given a rhs column containing Y non zeros, we assume that the respective Y columns</span></div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="comment">// of the lhs differs in average of one non zeros, thus the number of non zeros for</span></div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// the product of a rhs column with the lhs is X+Y where X is the average number of non zero</span></div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// per column of the lhs.</span></div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="comment">// Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)</span></div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros();</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  res.setZero();</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  res.reserve(Index(estimated_nnz_prod));</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="comment">// we compute each column of the result, one after the other</span></div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordflow">for</span> (Index j=0; j<cols; ++j)</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  {</div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  res.startVec(j);</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  Index nnz = 0;</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">for</span> (<span class="keyword">typename</span> Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  {</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  Scalar y = rhsIt.value();</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  Index k = rhsIt.index();</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">for</span> (<span class="keyword">typename</span> Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  Index i = lhsIt.index();</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  Scalar x = lhsIt.value();</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span>(!mask[i])</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  mask[i] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  values[i] = x * y;</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  indices[nnz] = i;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  ++nnz;</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  values[i] += x * y;</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// unordered insertion</span></div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k=0; k<nnz; ++k)</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  {</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordtype">int</span> i = indices[k];</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  res.insertBackByOuterInnerUnordered(j,i) = values[i];</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  mask[i] = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="preprocessor">#if 0</span></div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="preprocessor"></span> <span class="comment">// alternative ordered insertion code:</span></div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">int</span> t200 = rows/(log2(200)*1.39);</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">int</span> t = (rows*100)/139;</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// FIXME reserve nnz non zeros</span></div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// FIXME implement fast sort algorithms for very small nnz</span></div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// if the result is sparse enough => use a quick sort</span></div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="comment">// otherwise => loop through the entire vector</span></div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// In order to avoid to perform an expensive log2 when the</span></div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="comment">// result is clearly very sparse we use a linear bound up to 200.</span></div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">//if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)</span></div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">//res.startVec(j);</span></div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">if</span>(<span class="keyword">true</span>)</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  {</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">if</span>(nnz>1) std::sort(indices.data(),indices.data()+nnz);</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k=0; k<nnz; ++k)</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  {</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">int</span> i = indices[k];</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  res.insertBackByOuterInner(j,i) = values[i];</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  mask[i] = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  }</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// dense path</span></div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0; i<rows; ++i)</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">if</span>(mask[i])</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  mask[i] = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  res.insertBackByOuterInner(j,i) = values[i];</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  }</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  }</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  res.finalize();</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span> }</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span> } <span class="comment">// end namespace internal</span></div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType,</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordtype">int</span> LhsStorageOrder = (traits<Lhs>::Flags&<a class="code" href="group__flags.html#ga7bd49e7b260e869e10fb9dc4fd081a85">RowMajorBit</a>) ? <a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a> : <a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcdac86184b0e3be936fbfd20249a057a0bf">ColMajor</a>,</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordtype">int</span> RhsStorageOrder = (traits<Rhs>::Flags&<a class="code" href="group__flags.html#ga7bd49e7b260e869e10fb9dc4fd081a85">RowMajorBit</a>) ? <a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a> : <a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcdac86184b0e3be936fbfd20249a057a0bf">ColMajor</a>,</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordtype">int</span> ResStorageOrder = (traits<ResultType>::Flags&<a class="code" href="group__flags.html#ga7bd49e7b260e869e10fb9dc4fd081a85">RowMajorBit</a>) ? <a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a> : ColMajor></div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="keyword">struct</span> conservative_sparse_sparse_product_selector;</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor></div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span> {</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> remove_all<Lhs>::type LhsCleaned;</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> LhsCleaned::Scalar Scalar;</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;</div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  ColMajorMatrix resCol(lhs.rows(),rhs.cols());</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// sort the non zeros:</span></div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  RowMajorMatrix resRow(resCol);</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  res = resRow;</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span> };</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor></div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span> {</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  {</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  RowMajorMatrix rhsRow = rhs;</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  RowMajorMatrix resRow(lhs.rows(), rhs.cols());</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  internal::conservative_sparse_sparse_product_impl<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  res = resRow;</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  }</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> };</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor></div>
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> {</div>
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  {</div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;</div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  RowMajorMatrix lhsRow = lhs;</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  RowMajorMatrix resRow(lhs.rows(), rhs.cols());</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  res = resRow;</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  }</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span> };</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor></div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span> {</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;</div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  RowMajorMatrix resRow(lhs.rows(), rhs.cols());</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  res = resRow;</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span> };</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor></div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span> {</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits<typename remove_all<Lhs>::type>::Scalar Scalar;</div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div>
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  ColMajorMatrix resCol(lhs.rows(), rhs.cols());</div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);</div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  res = resCol;</div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  }</div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> };</div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>,ColMajor,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>></div>
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> {</div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  {</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;</div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  ColMajorMatrix lhsCol = lhs;</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  ColMajorMatrix resCol(lhs.rows(), rhs.cols());</div>
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  internal::conservative_sparse_sparse_product_impl<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);</div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  res = resCol;</div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  }</div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span> };</div>
<div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>></div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span> {</div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  {</div>
<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;</div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  ColMajorMatrix rhsCol = rhs;</div>
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  ColMajorMatrix resCol(lhs.rows(), rhs.cols());</div>
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);</div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  res = resCol;</div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  }</div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> };</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keyword">typename</span> ResultType></div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="keyword">struct </span>conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>,<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a>></div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> {</div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs, ResultType& res)</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  {</div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;</div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">typedef</span> SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;</div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  RowMajorMatrix resRow(lhs.rows(),rhs.cols());</div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);</div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="comment">// sort the non zeros:</span></div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  ColMajorMatrix resCol(resRow);</div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  res = resCol;</div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  }</div>
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> };</div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span> } <span class="comment">// end namespace internal</span></div>
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span> </div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span> } <span class="comment">// end namespace Eigen</span></div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="preprocessor">#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H</span></div>
<div class="ttc" id="group__enums_html_gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044"><div class="ttname"><a href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">Eigen::RowMajor</a></div><div class="ttdef"><b>Definition:</b> Constants.h:266</div></div>
<div class="ttc" id="group__enums_html_gga0c5bde183ecefe103f70b49ad9740bcdac86184b0e3be936fbfd20249a057a0bf"><div class="ttname"><a href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcdac86184b0e3be936fbfd20249a057a0bf">Eigen::ColMajor</a></div><div class="ttdef"><b>Definition:</b> Constants.h:264</div></div>
<div class="ttc" id="group__flags_html_ga7bd49e7b260e869e10fb9dc4fd081a85"><div class="ttname"><a href="group__flags.html#ga7bd49e7b260e869e10fb9dc4fd081a85">Eigen::RowMajorBit</a></div><div class="ttdeci">const unsigned int RowMajorBit</div><div class="ttdef"><b>Definition:</b> Constants.h:53</div></div>
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