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<div class="title">SelfadjointMatrixVector.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) 2008-2009 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_SELFADJOINT_MATRIX_VECTOR_H</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"></span><span class="preprocessor">#define EIGEN_SELFADJOINT_MATRIX_VECTOR_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="comment">/* Optimized selfadjoint matrix * vector product:</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * This algorithm processes 2 columns at onces that allows to both reduce</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * the number of load/stores of the result by a factor 2 and to reduce</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * the instruction dependency.</span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> */</span></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="keyword">template</span><<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> Index, <span class="keywordtype">int</span> StorageOrder, <span class="keywordtype">int</span> UpLo, <span class="keywordtype">bool</span> ConjugateLhs, <span class="keywordtype">bool</span> ConjugateRhs, <span class="keywordtype">int</span> Version=Specialized></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">struct </span>selfadjoint_matrix_vector_product;</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">template</span><<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> Index, <span class="keywordtype">int</span> StorageOrder, <span class="keywordtype">int</span> UpLo, <span class="keywordtype">bool</span> ConjugateLhs, <span class="keywordtype">bool</span> ConjugateRhs, <span class="keywordtype">int</span> Version></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">struct </span>selfadjoint_matrix_vector_product</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> {</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">static</span> EIGEN_DONT_INLINE <span class="keywordtype">void</span> run(</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  Index size,</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">const</span> Scalar* lhs, Index lhsStride,</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">const</span> Scalar* _rhs, Index rhsIncr,</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  Scalar* res,</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  Scalar alpha);</div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> };</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">template</span><<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> Index, <span class="keywordtype">int</span> StorageOrder, <span class="keywordtype">int</span> UpLo, <span class="keywordtype">bool</span> ConjugateLhs, <span class="keywordtype">bool</span> ConjugateRhs, <span class="keywordtype">int</span> Version></div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> EIGEN_DONT_INLINE <span class="keywordtype">void</span> selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run(</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  Index size,</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> Scalar* lhs, Index lhsStride,</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> Scalar* _rhs, Index rhsIncr,</div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  Scalar* res,</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  Scalar alpha)</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>  <span class="keyword">typedef</span> <span class="keyword">typename</span> packet_traits<Scalar>::type Packet;</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> Index PacketSize = <span class="keyword">sizeof</span>(Packet)/<span class="keyword">sizeof</span>(Scalar);</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  IsRowMajor = StorageOrder==<a class="code" href="group__enums.html#gga0c5bde183ecefe103f70b49ad9740bcda1e16fa1b92ed7a058cd4ce7a9a0db044">RowMajor</a> ? 1 : 0,</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  IsLower = UpLo == <a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910baf886b397626076218462d53d50eb96bc">Lower</a> ? 1 : 0,</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  FirstTriangular = IsRowMajor == IsLower</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> </div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0;</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1;</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex, ConjugateRhs> cjd;</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0;</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// FIXME this copy is now handled outside product_selfadjoint_vector, so it could probably be removed.</span></div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">// if the rhs is not sequentially stored in memory we copy it to a temporary buffer,</span></div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// this is because we need to extract packets</span></div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  ei_declare_aligned_stack_constructed_variable(Scalar,rhs,size,rhsIncr==1 ? const_cast<Scalar*>(_rhs) : 0); </div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">if</span> (rhsIncr!=1)</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> Scalar* it = _rhs;</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">for</span> (Index i=0; i<size; ++i, it+=rhsIncr)</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  rhs[i] = *it;</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</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>  Index bound = (std::max)(Index(0),size-8) & 0xfffffffe;</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> (FirstTriangular)</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  bound = size - bound;</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="keywordflow">for</span> (Index j=FirstTriangular ? bound : 0;</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  j<(FirstTriangular ? size : bound);j+=2)</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="keyword">register</span> <span class="keyword">const</span> Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">register</span> <span class="keyword">const</span> Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride;</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  Scalar t0 = cjAlpha * rhs[j];</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  Packet ptmp0 = pset1<Packet>(t0);</div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  Scalar t1 = cjAlpha * rhs[j+1];</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  Packet ptmp1 = pset1<Packet>(t1);</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  Scalar t2(0);</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  Packet ptmp2 = pset1<Packet>(t2);</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  Scalar t3(0);</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  Packet ptmp3 = pset1<Packet>(t3);</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">size_t</span> starti = FirstTriangular ? 0 : j+2;</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">size_t</span> endi = FirstTriangular ? j : size;</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">size_t</span> alignedStart = (starti) + internal::first_aligned(&res[starti], endi-starti);</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordtype">size_t</span> alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);</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="comment">// TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  res[j] += cjd.pmul(numext::real(A0[j]), t0);</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1);</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">if</span>(FirstTriangular)</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>  res[j] += cj0.pmul(A1[j], t1);</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  t3 += cj1.pmul(A1[j], rhs[j]);</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  }</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">else</span></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>  res[j+1] += cj0.pmul(A0[j+1],t0);</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  t2 += cj1.pmul(A0[j+1], rhs[j+1]);</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=starti; i<alignedStart; ++i)</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  res[i] += t0 * A0[i] + t1 * A1[i];</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  t2 += numext::conj(A0[i]) * rhs[i];</div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  t3 += numext::conj(A1[i]) * rhs[i];</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)</span></div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// gcc 4.2 does this optimization automatically.</span></div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">const</span> Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart;</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart;</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart;</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  Scalar* EIGEN_RESTRICT resIt = res + alignedStart;</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=alignedStart; i<alignedEnd; i+=PacketSize)</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  Packet A0i = ploadu<Packet>(a0It); a0It += PacketSize;</div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  Packet A1i = ploadu<Packet>(a1It); a1It += PacketSize;</div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  Packet Bi = ploadu<Packet>(rhsIt); rhsIt += PacketSize; <span class="comment">// FIXME should be aligned in most cases</span></div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  Packet Xi = pload <Packet>(resIt);</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  Xi = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi));</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2);</div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3);</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  pstore(resIt,Xi); resIt += PacketSize;</div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  }</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=alignedEnd; i<endi; i++)</div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  t2 += cj1.pmul(A0[i], rhs[i]);</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  t3 += cj1.pmul(A1[i], rhs[i]);</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</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>  res[j] += alpha * (t2 + predux(ptmp2));</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  res[j+1] += alpha * (t3 + predux(ptmp3));</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  }</div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">for</span> (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++)</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">register</span> <span class="keyword">const</span> Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;</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>  Scalar t1 = cjAlpha * rhs[j];</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  Scalar t2(0);</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed</span></div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  res[j] += cjd.pmul(numext::real(A0[j]), t1);</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">for</span> (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++)</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>  res[i] += cj0.pmul(A0[i], t1);</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  t2 += cj1.pmul(A0[i], rhs[i]);</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  }</div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  res[j] += alpha * t2;</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> }</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="comment">// end namespace internal </span></div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="comment">/***************************************************************************</span></div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="comment">* Wrapper to product_selfadjoint_vector</span></div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="comment">***************************************************************************/</span></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> <span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keywordtype">int</span> LhsMode, <span class="keyword">typename</span> Rhs></div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="keyword">struct </span>traits<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true> ></div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  : traits<ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs> ></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> }</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">template</span><<span class="keyword">typename</span> Lhs, <span class="keywordtype">int</span> LhsMode, <span class="keyword">typename</span> Rhs></div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="keyword">struct </span>SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true></div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  : <span class="keyword">public</span> ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs ></div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span> {</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix)</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>  enum {</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  LhsUpLo = LhsMode&(<a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910bae70afef0d3ff7aca74e17e85ff6c9f2e">Upper</a>|<a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910baf886b397626076218462d53d50eb96bc">Lower</a>)</div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  };</div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  SelfadjointProductMatrix(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs) : Base(lhs,rhs) {}</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Dest> <span class="keywordtype">void</span> scaleAndAddTo(Dest& dest, <span class="keyword">const</span> Scalar& alpha)<span class="keyword"> const</span></div>
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> Dest::Scalar ResScalar;</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> Base::RhsScalar RhsScalar;</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">typedef</span> Map<Matrix<ResScalar,Dynamic,1>, <a class="code" href="group__enums.html#gga456ac33d49271d3e2c371351cd1d6371ad5380ca00f3d74b38593adf8a0d06d3e">Aligned</a>> MappedDest;</div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  </div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  eigen_assert(dest.rows()==m_lhs.rows() && dest.cols()==m_rhs.cols());</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>  <span class="keyword">typename</span> internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs);</div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">typename</span> internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs);</div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs)</div>
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  * RhsBlasTraits::extractScalarFactor(m_rhs);</div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  EvalToDest = (Dest::InnerStrideAtCompileTime==1),</div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  UseRhs = (_ActualRhsType::InnerStrideAtCompileTime==1)</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  };</div>
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  </div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest;</div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  internal::gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!UseRhs> static_rhs;</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>  ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),</div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  EvalToDest ? dest.data() : static_dest.data());</div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  </div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(),</div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  UseRhs ? <span class="keyword">const_cast<</span>RhsScalar*<span class="keyword">></span>(rhs.data()) : static_rhs.data());</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="keywordflow">if</span>(!EvalToDest)</div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div>
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="preprocessor"> #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN</span></div>
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span> <span class="preprocessor"></span> <span class="keywordtype">int</span> size = dest.size();</div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  EIGEN_DENSE_STORAGE_CTOR_PLUGIN</div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="preprocessor"> #endif</span></div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="preprocessor"></span> MappedDest(actualDestPtr, dest.size()) = dest;</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>  </div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">if</span>(!UseRhs)</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="preprocessor"> #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN</span></div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="preprocessor"></span> <span class="keywordtype">int</span> size = rhs.size();</div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  EIGEN_DENSE_STORAGE_CTOR_PLUGIN</div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="preprocessor"> #endif</span></div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="preprocessor"></span> Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, rhs.size()) = rhs;</div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  </div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  </div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<_ActualLhsType>::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>, <span class="keywordtype">int</span>(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run</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>  lhs.rows(), <span class="comment">// size</span></div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  &lhs.coeffRef(0,0), lhs.outerStride(), <span class="comment">// lhs info</span></div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  actualRhsPtr, 1, <span class="comment">// rhs info</span></div>
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  actualDestPtr, <span class="comment">// result info</span></div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  actualAlpha <span class="comment">// scale factor</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>  </div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">if</span>(!EvalToDest)</div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  dest = MappedDest(actualDestPtr, dest.size());</div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  }</div>
<div class="line"><a name="l00249"></a><span class="lineno"> 249</span> };</div>
<div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div>
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keywordtype">int</span> RhsMode></div>
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="keyword">struct </span>traits<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false> ></div>
<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  : traits<ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs> ></div>
<div class="line"><a name="l00255"></a><span class="lineno"> 255</span> {};</div>
<div class="line"><a name="l00256"></a><span class="lineno"> 256</span> }</div>
<div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div>
<div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="keyword">template</span><<span class="keyword">typename</span> Lhs, <span class="keyword">typename</span> Rhs, <span class="keywordtype">int</span> RhsMode></div>
<div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="keyword">struct </span>SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false></div>
<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  : <span class="keyword">public</span> ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs ></div>
<div class="line"><a name="l00261"></a><span class="lineno"> 261</span> {</div>
<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix)</div>
<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div>
<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  enum {</div>
<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  RhsUpLo = RhsMode&(<a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910bae70afef0d3ff7aca74e17e85ff6c9f2e">Upper</a>|<a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910baf886b397626076218462d53d50eb96bc">Lower</a>)</div>
<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  };</div>
<div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div>
<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  SelfadjointProductMatrix(<span class="keyword">const</span> Lhs& lhs, <span class="keyword">const</span> Rhs& rhs) : Base(lhs,rhs) {}</div>
<div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div>
<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Dest> <span class="keywordtype">void</span> scaleAndAddTo(Dest& dest, <span class="keyword">const</span> Scalar& alpha)<span class="keyword"> const</span></div>
<div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="comment">// let's simply transpose the product</span></div>
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  Transpose<Dest> destT(dest);</div>
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  SelfadjointProductMatrix<Transpose<const Rhs>, int(RhsUpLo)==<a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910bae70afef0d3ff7aca74e17e85ff6c9f2e">Upper</a> ? <a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910baf886b397626076218462d53d50eb96bc">Lower</a> : <a class="code" href="group__enums.html#ggab59c1bec446b10af208f977a871d910bae70afef0d3ff7aca74e17e85ff6c9f2e">Upper</a>, <span class="keyword">false</span>,</div>
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  Transpose<const Lhs>, 0, <span class="keyword">true</span>>(m_rhs.transpose(), m_lhs.transpose()).scaleAndAddTo(destT, alpha);</div>
<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div>
<div class="line"><a name="l00277"></a><span class="lineno"> 277</span> };</div>
<div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div>
<div class="line"><a name="l00279"></a><span class="lineno"> 279</span> } <span class="comment">// end namespace Eigen</span></div>
<div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div>
<div class="line"><a name="l00281"></a><span class="lineno"> 281</span> <span class="preprocessor">#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_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__enums_html_ggab59c1bec446b10af208f977a871d910bae70afef0d3ff7aca74e17e85ff6c9f2e"><div class="ttname"><a href="group__enums.html#ggab59c1bec446b10af208f977a871d910bae70afef0d3ff7aca74e17e85ff6c9f2e">Eigen::Upper</a></div><div class="ttdef"><b>Definition:</b> Constants.h:169</div></div>
<div class="ttc" id="group__enums_html_ggab59c1bec446b10af208f977a871d910baf886b397626076218462d53d50eb96bc"><div class="ttname"><a href="group__enums.html#ggab59c1bec446b10af208f977a871d910baf886b397626076218462d53d50eb96bc">Eigen::Lower</a></div><div class="ttdef"><b>Definition:</b> Constants.h:167</div></div>
<div class="ttc" id="group__enums_html_gga456ac33d49271d3e2c371351cd1d6371ad5380ca00f3d74b38593adf8a0d06d3e"><div class="ttname"><a href="group__enums.html#gga456ac33d49271d3e2c371351cd1d6371ad5380ca00f3d74b38593adf8a0d06d3e">Eigen::Aligned</a></div><div class="ttdef"><b>Definition:</b> Constants.h:194</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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