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<div class="title">ArpackSelfAdjointEigenSolver.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) 2012 David Harmon <dharmon@gmail.com></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">// Eigen is free software; you can redistribute it and/or</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment">// modify it under the terms of the GNU Lesser General Public</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment">// License as published by the Free Software Foundation; either</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment">// version 3 of the License, or (at your option) any later version.</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment">//</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment">// Alternatively, you can redistribute it and/or</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// modify it under the terms of the GNU General Public License as</span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment">// published by the Free Software Foundation; either version 2 of</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// the License, or (at your option) any later version.</span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment">//</span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment">// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment">// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment">// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment">// GNU General Public License for more details.</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment">//</span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">// You should have received a copy of the GNU Lesser General Public</span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">// License and a copy of the GNU General Public License along with</span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// Eigen. If not, see <http://www.gnu.org/licenses/>.</span></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#ifndef EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H</span></div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor"></span><span class="preprocessor">#define EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H</span></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include <Eigen/Dense></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">namespace </span>Eigen { </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="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> RealScalar> <span class="keyword">struct </span>arpack_wrapper;</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">template</span><<span class="keyword">typename</span> MatrixSolver, <span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> Scalar, <span class="keywordtype">bool</span> BisSPD> <span class="keyword">struct </span>OP;</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> }</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> </div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">template</span><<span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> MatrixSolver=SimplicialLLT<MatrixType>, <span class="keywordtype">bool</span> BisSPD=false></div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">class </span>ArpackGeneralizedSelfAdjointEigenSolver</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">public</span>:</div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">//typedef typename MatrixSolver::MatrixType MatrixType;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> MatrixType::Scalar Scalar;</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> MatrixType::Index Index;</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> NumTraits<Scalar>::Real RealScalar;</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  ArpackGeneralizedSelfAdjointEigenSolver()</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  : m_eivec(),</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  m_eivalues(),</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  m_isInitialized(false),</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  m_eigenvectorsOk(false),</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  m_nbrConverged(0),</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  m_nbrIterations(0)</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  { }</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  ArpackGeneralizedSelfAdjointEigenSolver(<span class="keyword">const</span> MatrixType& A, <span class="keyword">const</span> MatrixType& B,</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  Index nbrEigenvalues, std::string eigs_sigma=<span class="stringliteral">"LM"</span>,</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordtype">int</span> options=ComputeEigenvectors, RealScalar tol=0.0)</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  : m_eivec(),</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  m_eivalues(),</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  m_isInitialized(false),</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  m_eigenvectorsOk(false),</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_nbrConverged(0),</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  m_nbrIterations(0)</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>  compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);</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="l00136"></a><span class="lineno"> 136</span>  ArpackGeneralizedSelfAdjointEigenSolver(<span class="keyword">const</span> MatrixType& A,</div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  Index nbrEigenvalues, std::string eigs_sigma=<span class="stringliteral">"LM"</span>,</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordtype">int</span> options=ComputeEigenvectors, RealScalar tol=0.0)</div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  : m_eivec(),</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  m_eivalues(),</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  m_isInitialized(false),</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  m_eigenvectorsOk(false),</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  m_nbrConverged(0),</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  m_nbrIterations(0)</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>  compute(A, nbrEigenvalues, eigs_sigma, options, tol);</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> </div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  ArpackGeneralizedSelfAdjointEigenSolver& compute(<span class="keyword">const</span> MatrixType& A, <span class="keyword">const</span> MatrixType& B,</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  Index nbrEigenvalues, std::string eigs_sigma=<span class="stringliteral">"LM"</span>,</div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordtype">int</span> options=ComputeEigenvectors, RealScalar tol=0.0);</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  </div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  ArpackGeneralizedSelfAdjointEigenSolver& compute(<span class="keyword">const</span> MatrixType& A,</div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  Index nbrEigenvalues, std::string eigs_sigma=<span class="stringliteral">"LM"</span>,</div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordtype">int</span> options=ComputeEigenvectors, RealScalar tol=0.0);</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> </div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">const</span> Matrix<Scalar, Dynamic, Dynamic>& eigenvectors()<span class="keyword"> const</span></div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  eigen_assert(m_isInitialized && <span class="stringliteral">"ArpackGeneralizedSelfAdjointEigenSolver is not initialized."</span>);</div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  eigen_assert(m_eigenvectorsOk && <span class="stringliteral">"The eigenvectors have not been computed together with the eigenvalues."</span>);</div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">return</span> m_eivec;</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> </div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keyword">const</span> Matrix<Scalar, Dynamic, 1>& eigenvalues()<span class="keyword"> const</span></div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  eigen_assert(m_isInitialized && <span class="stringliteral">"ArpackGeneralizedSelfAdjointEigenSolver is not initialized."</span>);</div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">return</span> m_eivalues;</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="l00269"></a><span class="lineno"> 269</span>  Matrix<Scalar, Dynamic, Dynamic> operatorSqrt()<span class="keyword"> const</span></div>
<div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  eigen_assert(m_isInitialized && <span class="stringliteral">"SelfAdjointEigenSolver is not initialized."</span>);</div>
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  eigen_assert(m_eigenvectorsOk && <span class="stringliteral">"The eigenvectors have not been computed together with the eigenvalues."</span>);</div>
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">return</span> m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();</div>
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div>
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div>
<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  Matrix<Scalar, Dynamic, Dynamic> operatorInverseSqrt()<span class="keyword"> const</span></div>
<div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  eigen_assert(m_isInitialized && <span class="stringliteral">"SelfAdjointEigenSolver is not initialized."</span>);</div>
<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  eigen_assert(m_eigenvectorsOk && <span class="stringliteral">"The eigenvectors have not been computed together with the eigenvalues."</span>);</div>
<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">return</span> m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();</div>
<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  }</div>
<div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div>
<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  ComputationInfo info()<span class="keyword"> const</span></div>
<div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  eigen_assert(m_isInitialized && <span class="stringliteral">"ArpackGeneralizedSelfAdjointEigenSolver is not initialized."</span>);</div>
<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">return</span> m_info;</div>
<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</div>
<div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div>
<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordtype">size_t</span> getNbrConvergedEigenValues()<span class="keyword"> const</span></div>
<div class="line"><a name="l00312"></a><span class="lineno"> 312</span> <span class="keyword"> </span>{ <span class="keywordflow">return</span> m_nbrConverged; }</div>
<div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div>
<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordtype">size_t</span> getNbrIterations()<span class="keyword"> const</span></div>
<div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="keyword"> </span>{ <span class="keywordflow">return</span> m_nbrIterations; }</div>
<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div>
<div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  Matrix<Scalar, Dynamic, Dynamic> m_eivec;</div>
<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  Matrix<Scalar, Dynamic, 1> m_eivalues;</div>
<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  ComputationInfo m_info;</div>
<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordtype">bool</span> m_isInitialized;</div>
<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keywordtype">bool</span> m_eigenvectorsOk;</div>
<div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div>
<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordtype">size_t</span> m_nbrConverged;</div>
<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordtype">size_t</span> m_nbrIterations;</div>
<div class="line"><a name="l00326"></a><span class="lineno"> 326</span> };</div>
<div class="line"><a name="l00327"></a><span class="lineno"> 327</span> </div>
<div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div>
<div class="line"><a name="l00329"></a><span class="lineno"> 329</span> </div>
<div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div>
<div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div>
<div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="keyword">template</span><<span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> MatrixSolver, <span class="keywordtype">bool</span> BisSPD></div>
<div class="line"><a name="l00333"></a><span class="lineno"> 333</span> ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>&</div>
<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD></div>
<div class="line"><a name="l00335"></a><span class="lineno"> 335</span> ::compute(<span class="keyword">const</span> MatrixType& A, Index nbrEigenvalues,</div>
<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  std::string eigs_sigma, <span class="keywordtype">int</span> options, RealScalar tol)</div>
<div class="line"><a name="l00337"></a><span class="lineno"> 337</span> {</div>
<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  MatrixType B(0,0);</div>
<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);</div>
<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  </div>
<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a name="l00342"></a><span class="lineno"> 342</span> }</div>
<div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div>
<div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div>
<div class="line"><a name="l00345"></a><span class="lineno"> 345</span> <span class="keyword">template</span><<span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> MatrixSolver, <span class="keywordtype">bool</span> BisSPD></div>
<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>&</div>
<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD></div>
<div class="line"><a name="l00348"></a><span class="lineno"> 348</span> ::compute(<span class="keyword">const</span> MatrixType& A, <span class="keyword">const</span> MatrixType& B, Index nbrEigenvalues,</div>
<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::string eigs_sigma, <span class="keywordtype">int</span> options, RealScalar tol)</div>
<div class="line"><a name="l00350"></a><span class="lineno"> 350</span> {</div>
<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  eigen_assert(A.cols() == A.rows());</div>
<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  eigen_assert(B.cols() == B.rows());</div>
<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  eigen_assert(B.rows() == 0 || A.cols() == B.rows());</div>
<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  eigen_assert((options &~ (EigVecMask | GenEigMask)) == 0</div>
<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  && (options & EigVecMask) != EigVecMask</div>
<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  && <span class="stringliteral">"invalid option parameter"</span>);</div>
<div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div>
<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordtype">bool</span> isBempty = (B.rows() == 0) || (B.cols() == 0);</div>
<div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div>
<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="comment">// For clarity, all parameters match their ARPACK name</span></div>
<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="comment">// Always 0 on the first call</span></div>
<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordtype">int</span> ido = 0;</div>
<div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div>
<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordtype">int</span> n = (int)A.cols();</div>
<div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div>
<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="comment">// User options: "LA", "SA", "SM", "LM", "BE"</span></div>
<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordtype">char</span> whch[3] = <span class="stringliteral">"LM"</span>;</div>
<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  </div>
<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// Specifies the shift if iparam[6] = { 3, 4, 5 }, not used if iparam[6] = { 1, 2 }</span></div>
<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  RealScalar sigma = 0.0;</div>
<div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div>
<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">if</span> (eigs_sigma.length() >= 2 && isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1]))</div>
<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  {</div>
<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  eigs_sigma[0] = toupper(eigs_sigma[0]);</div>
<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  eigs_sigma[1] = toupper(eigs_sigma[1]);</div>
<div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div>
<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="comment">// In the following special case we're going to invert the problem, since solving</span></div>
<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="comment">// for larger magnitude is much much faster</span></div>
<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">// i.e., if 'SM' is specified, we're going to really use 'LM', the default</span></div>
<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">if</span> (eigs_sigma.substr(0,2) != <span class="stringliteral">"SM"</span>)</div>
<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  {</div>
<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  whch[0] = eigs_sigma[0];</div>
<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  whch[1] = eigs_sigma[1];</div>
<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  }</div>
<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div>
<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {</div>
<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  eigen_assert(<span class="keyword">false</span> && <span class="stringliteral">"Specifying clustered eigenvalues is not yet supported!"</span>);</div>
<div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div>
<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="comment">// If it's not scalar values, then the user may be explicitly</span></div>
<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="comment">// specifying the sigma value to cluster the evs around</span></div>
<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  sigma = atof(eigs_sigma.c_str());</div>
<div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div>
<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="comment">// If atof fails, it returns 0.0, which is a fine default</span></div>
<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div>
<div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div>
<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="comment">// "I" means normal eigenvalue problem, "G" means generalized</span></div>
<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordtype">char</span> bmat[2] = <span class="stringliteral">"I"</span>;</div>
<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">if</span> (eigs_sigma.substr(0,2) == <span class="stringliteral">"SM"</span> || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])) || (!isBempty && !BisSPD))</div>
<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  bmat[0] = <span class="charliteral">'G'</span>;</div>
<div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div>
<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="comment">// Now we determine the mode to use</span></div>
<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordtype">int</span> mode = (bmat[0] == <span class="charliteral">'G'</span>) + 1;</div>
<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keywordflow">if</span> (eigs_sigma.substr(0,2) == <span class="stringliteral">"SM"</span> || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])))</div>
<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  {</div>
<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="comment">// We're going to use shift-and-invert mode, and basically find</span></div>
<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="comment">// the largest eigenvalues of the inverse operator</span></div>
<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  mode = 3;</div>
<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  }</div>
<div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div>
<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="comment">// The user-specified number of eigenvalues/vectors to compute</span></div>
<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordtype">int</span> nev = (int)nbrEigenvalues;</div>
<div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div>
<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="comment">// Allocate space for ARPACK to store the residual</span></div>
<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  Scalar *resid = <span class="keyword">new</span> Scalar[n];</div>
<div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div>
<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="comment">// Number of Lanczos vectors, must satisfy nev < ncv <= n</span></div>
<div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="comment">// Note that this indicates that nev != n, and we cannot compute</span></div>
<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="comment">// all eigenvalues of a mtrix</span></div>
<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordtype">int</span> ncv = std::min(std::max(2*nev, 20), n);</div>
<div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div>
<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="comment">// The working n x ncv matrix, also store the final eigenvectors (if computed)</span></div>
<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  Scalar *v = <span class="keyword">new</span> Scalar[n*ncv];</div>
<div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordtype">int</span> ldv = n;</div>
<div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div>
<div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="comment">// Working space</span></div>
<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  Scalar *workd = <span class="keyword">new</span> Scalar[3*n];</div>
<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordtype">int</span> lworkl = ncv*ncv+8*ncv; <span class="comment">// Must be at least this length</span></div>
<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  Scalar *workl = <span class="keyword">new</span> Scalar[lworkl];</div>
<div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div>
<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordtype">int</span> *iparam= <span class="keyword">new</span> <span class="keywordtype">int</span>[11];</div>
<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  iparam[0] = 1; <span class="comment">// 1 means we let ARPACK perform the shifts, 0 means we'd have to do it</span></div>
<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  iparam[2] = std::max(300, (<span class="keywordtype">int</span>)std::ceil(2*n/std::max(ncv,1)));</div>
<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  iparam[6] = mode; <span class="comment">// The mode, 1 is standard ev problem, 2 for generalized ev, 3 for shift-and-invert</span></div>
<div class="line"><a name="l00450"></a><span class="lineno"> 450</span> </div>
<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="comment">// Used during reverse communicate to notify where arrays start</span></div>
<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordtype">int</span> *ipntr = <span class="keyword">new</span> <span class="keywordtype">int</span>[11]; </div>
<div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div>
<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="comment">// Error codes are returned in here, initial value of 0 indicates a random initial</span></div>
<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="comment">// residual vector is used, any other values means resid contains the initial residual</span></div>
<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="comment">// vector, possibly from a previous run</span></div>
<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keywordtype">int</span> info = 0;</div>
<div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div>
<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  Scalar scale = 1.0;</div>
<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">//if (!isBempty)</span></div>
<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">//{</span></div>
<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">//Scalar scale = B.norm() / std::sqrt(n);</span></div>
<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">//scale = std::pow(2, std::floor(std::log(scale+1)));</span></div>
<div class="line"><a name="l00467"></a><span class="lineno"> 467</span> <span class="comment"></span> <span class="comment">//for (size_t i=0; i<(size_t)B.outerSize(); i++)</span></div>
<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="comment">// for (typename MatrixType::InnerIterator it(B, i); it; ++it)</span></div>
<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="comment">// it.valueRef() /= scale;</span></div>
<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="comment">//}</span></div>
<div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div>
<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  MatrixSolver OP;</div>
<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordflow">if</span> (mode == 1 || mode == 2)</div>
<div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  {</div>
<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">if</span> (!isBempty)</div>
<div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  OP.compute(B);</div>
<div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div>
<div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (mode == 3)</div>
<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  {</div>
<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keywordflow">if</span> (sigma == 0.0)</div>
<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  {</div>
<div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  OP.compute(A);</div>
<div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  }</div>
<div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  {</div>
<div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// Note: We will never enter here because sigma must be 0.0</span></div>
<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">if</span> (isBempty)</div>
<div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  {</div>
<div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  MatrixType AminusSigmaB(A);</div>
<div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">for</span> (Index i=0; i<A.rows(); ++i)</div>
<div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  AminusSigmaB.coeffRef(i,i) -= sigma;</div>
<div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  </div>
<div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  OP.compute(AminusSigmaB);</div>
<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  }</div>
<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  {</div>
<div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  MatrixType AminusSigmaB = A - sigma * B;</div>
<div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  OP.compute(AminusSigmaB);</div>
<div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  }</div>
<div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  }</div>
<div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  }</div>
<div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  </div>
<div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keywordflow">if</span> (!(mode == 1 && isBempty) && !(mode == 2 && isBempty) && OP.info() != Success)</div>
<div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  std::cout << <span class="stringliteral">"Error factoring matrix"</span> << std::endl;</div>
<div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div>
<div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keywordflow">do</span></div>
<div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  {</div>
<div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  internal::arpack_wrapper<Scalar, RealScalar>::saupd(&ido, bmat, &n, whch, &nev, &tol, resid, </div>
<div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  &ncv, v, &ldv, iparam, ipntr, workd, workl,</div>
<div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  &lworkl, &info);</div>
<div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div>
<div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keywordflow">if</span> (ido == -1 || ido == 1)</div>
<div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  {</div>
<div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  Scalar *in = workd + ipntr[0] - 1;</div>
<div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  Scalar *out = workd + ipntr[1] - 1;</div>
<div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div>
<div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <span class="keywordflow">if</span> (ido == 1 && mode != 2)</div>
<div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  {</div>
<div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  Scalar *out2 = workd + ipntr[2] - 1;</div>
<div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keywordflow">if</span> (isBempty || mode == 1)</div>
<div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  Matrix<Scalar, Dynamic, 1>::Map(out2, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);</div>
<div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  Matrix<Scalar, Dynamic, 1>::Map(out2, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);</div>
<div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  </div>
<div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  in = workd + ipntr[2] - 1;</div>
<div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  }</div>
<div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div>
<div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <span class="keywordflow">if</span> (mode == 1)</div>
<div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  {</div>
<div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">if</span> (isBempty)</div>
<div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  {</div>
<div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="comment">// OP = A</span></div>
<div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);</div>
<div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  }</div>
<div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div>
<div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="comment">// OP = L^{-1}AL^{-T}</span></div>
<div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::applyOP(OP, A, n, in, out);</div>
<div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  }</div>
<div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  }</div>
<div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (mode == 2)</div>
<div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  {</div>
<div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordflow">if</span> (ido == 1)</div>
<div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  Matrix<Scalar, Dynamic, 1>::Map(in, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);</div>
<div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  </div>
<div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="comment">// OP = B^{-1} A</span></div>
<div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));</div>
<div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  }</div>
<div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (mode == 3)</div>
<div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  {</div>
<div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="comment">// OP = (A-\sigmaB)B (\sigma could be 0, and B could be I)</span></div>
<div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="comment">// The B * in is already computed and stored at in if ido == 1</span></div>
<div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">if</span> (ido == 1 || isBempty)</div>
<div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));</div>
<div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(B * Matrix<Scalar, Dynamic, 1>::Map(in, n));</div>
<div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  }</div>
<div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div>
<div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (ido == 2)</div>
<div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  {</div>
<div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  Scalar *in = workd + ipntr[0] - 1;</div>
<div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  Scalar *out = workd + ipntr[1] - 1;</div>
<div class="line"><a name="l00568"></a><span class="lineno"> 568</span> </div>
<div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordflow">if</span> (isBempty || mode == 1)</div>
<div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);</div>
<div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);</div>
<div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  }</div>
<div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  } <span class="keywordflow">while</span> (ido != 99);</div>
<div class="line"><a name="l00575"></a><span class="lineno"> 575</span> </div>
<div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keywordflow">if</span> (info == 1)</div>
<div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  m_info = NoConvergence;</div>
<div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (info == 3)</div>
<div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  m_info = NumericalIssue;</div>
<div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (info < 0)</div>
<div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  m_info = InvalidInput;</div>
<div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (info != 0)</div>
<div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  eigen_assert(<span class="keyword">false</span> && <span class="stringliteral">"Unknown ARPACK return value!"</span>);</div>
<div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  {</div>
<div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="comment">// Do we compute eigenvectors or not?</span></div>
<div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keywordtype">int</span> rvec = (options & ComputeEigenvectors) == ComputeEigenvectors;</div>
<div class="line"><a name="l00589"></a><span class="lineno"> 589</span> </div>
<div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="comment">// "A" means "All", use "S" to choose specific eigenvalues (not yet supported in ARPACK))</span></div>
<div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="keywordtype">char</span> howmny[2] = <span class="stringliteral">"A"</span>; </div>
<div class="line"><a name="l00593"></a><span class="lineno"> 593</span> </div>
<div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="comment">// if howmny == "S", specifies the eigenvalues to compute (not implemented in ARPACK)</span></div>
<div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keywordtype">int</span> *select = <span class="keyword">new</span> <span class="keywordtype">int</span>[ncv];</div>
<div class="line"><a name="l00597"></a><span class="lineno"> 597</span> </div>
<div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="comment">// Final eigenvalues</span></div>
<div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  m_eivalues.resize(nev, 1);</div>
<div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div>
<div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  internal::arpack_wrapper<Scalar, RealScalar>::seupd(&rvec, howmny, select, m_eivalues.data(), v, &ldv,</div>
<div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  &sigma, bmat, &n, whch, &nev, &tol, resid, &ncv,</div>
<div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  v, &ldv, iparam, ipntr, workd, workl, &lworkl, &info);</div>
<div class="line"><a name="l00605"></a><span class="lineno"> 605</span> </div>
<div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keywordflow">if</span> (info == -14)</div>
<div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  m_info = NoConvergence;</div>
<div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (info != 0)</div>
<div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  m_info = InvalidInput;</div>
<div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  {</div>
<div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="keywordflow">if</span> (rvec)</div>
<div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  {</div>
<div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  m_eivec.resize(A.rows(), nev);</div>
<div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0; i<nev; i++)</div>
<div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j=0; j<n; j++)</div>
<div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  m_eivec(j,i) = v[i*n+j] / scale;</div>
<div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  </div>
<div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keywordflow">if</span> (mode == 1 && !isBempty && BisSPD)</div>
<div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::project(OP, n, nev, m_eivec.data());</div>
<div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div>
<div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  m_eigenvectorsOk = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  }</div>
<div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div>
<div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  m_nbrIterations = iparam[2];</div>
<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  m_nbrConverged = iparam[4];</div>
<div class="line"><a name="l00627"></a><span class="lineno"> 627</span> </div>
<div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  m_info = Success;</div>
<div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  }</div>
<div class="line"><a name="l00630"></a><span class="lineno"> 630</span> </div>
<div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keyword">delete</span> select;</div>
<div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  }</div>
<div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div>
<div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keyword">delete</span> v;</div>
<div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keyword">delete</span> iparam;</div>
<div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keyword">delete</span> ipntr;</div>
<div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">delete</span> workd;</div>
<div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">delete</span> workl;</div>
<div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">delete</span> resid;</div>
<div class="line"><a name="l00640"></a><span class="lineno"> 640</span> </div>
<div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  m_isInitialized = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00642"></a><span class="lineno"> 642</span> </div>
<div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a name="l00644"></a><span class="lineno"> 644</span> }</div>
<div class="line"><a name="l00645"></a><span class="lineno"> 645</span> </div>
<div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div>
<div class="line"><a name="l00647"></a><span class="lineno"> 647</span> <span class="comment">// Single precision</span></div>
<div class="line"><a name="l00648"></a><span class="lineno"> 648</span> <span class="comment">//</span></div>
<div class="line"><a name="l00649"></a><span class="lineno"> 649</span> <span class="keyword">extern</span> <span class="stringliteral">"C"</span> <span class="keywordtype">void</span> ssaupd_(<span class="keywordtype">int</span> *ido, <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which,</div>
<div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordtype">int</span> *nev, <span class="keywordtype">float</span> *tol, <span class="keywordtype">float</span> *resid, <span class="keywordtype">int</span> *ncv,</div>
<div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="keywordtype">float</span> *v, <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr,</div>
<div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="keywordtype">float</span> *workd, <span class="keywordtype">float</span> *workl, <span class="keywordtype">int</span> *lworkl,</div>
<div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keywordtype">int</span> *info);</div>
<div class="line"><a name="l00654"></a><span class="lineno"> 654</span> </div>
<div class="line"><a name="l00655"></a><span class="lineno"> 655</span> <span class="keyword">extern</span> <span class="stringliteral">"C"</span> <span class="keywordtype">void</span> sseupd_(<span class="keywordtype">int</span> *rvec, <span class="keywordtype">char</span> *All, <span class="keywordtype">int</span> *select, <span class="keywordtype">float</span> *d,</div>
<div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="keywordtype">float</span> *z, <span class="keywordtype">int</span> *ldz, <span class="keywordtype">float</span> *sigma, </div>
<div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which, <span class="keywordtype">int</span> *nev,</div>
<div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keywordtype">float</span> *tol, <span class="keywordtype">float</span> *resid, <span class="keywordtype">int</span> *ncv, <span class="keywordtype">float</span> *v,</div>
<div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr, <span class="keywordtype">float</span> *workd,</div>
<div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keywordtype">float</span> *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *ierr);</div>
<div class="line"><a name="l00661"></a><span class="lineno"> 661</span> </div>
<div class="line"><a name="l00662"></a><span class="lineno"> 662</span> <span class="comment">// Double precision</span></div>
<div class="line"><a name="l00663"></a><span class="lineno"> 663</span> <span class="comment">//</span></div>
<div class="line"><a name="l00664"></a><span class="lineno"> 664</span> <span class="keyword">extern</span> <span class="stringliteral">"C"</span> <span class="keywordtype">void</span> dsaupd_(<span class="keywordtype">int</span> *ido, <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which,</div>
<div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keywordtype">int</span> *nev, <span class="keywordtype">double</span> *tol, <span class="keywordtype">double</span> *resid, <span class="keywordtype">int</span> *ncv,</div>
<div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordtype">double</span> *v, <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr,</div>
<div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keywordtype">double</span> *workd, <span class="keywordtype">double</span> *workl, <span class="keywordtype">int</span> *lworkl,</div>
<div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keywordtype">int</span> *info);</div>
<div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div>
<div class="line"><a name="l00670"></a><span class="lineno"> 670</span> <span class="keyword">extern</span> <span class="stringliteral">"C"</span> <span class="keywordtype">void</span> dseupd_(<span class="keywordtype">int</span> *rvec, <span class="keywordtype">char</span> *All, <span class="keywordtype">int</span> *select, <span class="keywordtype">double</span> *d,</div>
<div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordtype">double</span> *z, <span class="keywordtype">int</span> *ldz, <span class="keywordtype">double</span> *sigma, </div>
<div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which, <span class="keywordtype">int</span> *nev,</div>
<div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordtype">double</span> *tol, <span class="keywordtype">double</span> *resid, <span class="keywordtype">int</span> *ncv, <span class="keywordtype">double</span> *v,</div>
<div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr, <span class="keywordtype">double</span> *workd,</div>
<div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordtype">double</span> *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *ierr);</div>
<div class="line"><a name="l00676"></a><span class="lineno"> 676</span> </div>
<div class="line"><a name="l00677"></a><span class="lineno"> 677</span> </div>
<div class="line"><a name="l00678"></a><span class="lineno"> 678</span> <span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div>
<div class="line"><a name="l00680"></a><span class="lineno"> 680</span> <span class="keyword">template</span><<span class="keyword">typename</span> Scalar, <span class="keyword">typename</span> RealScalar> <span class="keyword">struct </span>arpack_wrapper</div>
<div class="line"><a name="l00681"></a><span class="lineno"> 681</span> {</div>
<div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> saupd(<span class="keywordtype">int</span> *ido, <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which,</div>
<div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keywordtype">int</span> *nev, RealScalar *tol, Scalar *resid, <span class="keywordtype">int</span> *ncv,</div>
<div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  Scalar *v, <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr,</div>
<div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  Scalar *workd, Scalar *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *info)</div>
<div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  { </div>
<div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)</div>
<div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  }</div>
<div class="line"><a name="l00689"></a><span class="lineno"> 689</span> </div>
<div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> seupd(<span class="keywordtype">int</span> *rvec, <span class="keywordtype">char</span> *All, <span class="keywordtype">int</span> *select, Scalar *d,</div>
<div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  Scalar *z, <span class="keywordtype">int</span> *ldz, RealScalar *sigma,</div>
<div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which, <span class="keywordtype">int</span> *nev,</div>
<div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  RealScalar *tol, Scalar *resid, <span class="keywordtype">int</span> *ncv, Scalar *v,</div>
<div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr, Scalar *workd,</div>
<div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  Scalar *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *ierr)</div>
<div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  {</div>
<div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)</div>
<div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  }</div>
<div class="line"><a name="l00699"></a><span class="lineno"> 699</span> };</div>
<div class="line"><a name="l00700"></a><span class="lineno"> 700</span> </div>
<div class="line"><a name="l00701"></a><span class="lineno"> 701</span> <span class="keyword">template</span> <> <span class="keyword">struct </span>arpack_wrapper<float, float></div>
<div class="line"><a name="l00702"></a><span class="lineno"> 702</span> {</div>
<div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> saupd(<span class="keywordtype">int</span> *ido, <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which,</div>
<div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keywordtype">int</span> *nev, <span class="keywordtype">float</span> *tol, <span class="keywordtype">float</span> *resid, <span class="keywordtype">int</span> *ncv,</div>
<div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordtype">float</span> *v, <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr,</div>
<div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordtype">float</span> *workd, <span class="keywordtype">float</span> *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *info)</div>
<div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  {</div>
<div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  ssaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);</div>
<div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  }</div>
<div class="line"><a name="l00710"></a><span class="lineno"> 710</span> </div>
<div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> seupd(<span class="keywordtype">int</span> *rvec, <span class="keywordtype">char</span> *All, <span class="keywordtype">int</span> *select, <span class="keywordtype">float</span> *d,</div>
<div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keywordtype">float</span> *z, <span class="keywordtype">int</span> *ldz, <span class="keywordtype">float</span> *sigma,</div>
<div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which, <span class="keywordtype">int</span> *nev,</div>
<div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordtype">float</span> *tol, <span class="keywordtype">float</span> *resid, <span class="keywordtype">int</span> *ncv, <span class="keywordtype">float</span> *v,</div>
<div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr, <span class="keywordtype">float</span> *workd,</div>
<div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <span class="keywordtype">float</span> *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *ierr)</div>
<div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  {</div>
<div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  sseupd_(rvec, All, select, d, z, ldz, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr,</div>
<div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  workd, workl, lworkl, ierr);</div>
<div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  }</div>
<div class="line"><a name="l00721"></a><span class="lineno"> 721</span> };</div>
<div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div>
<div class="line"><a name="l00723"></a><span class="lineno"> 723</span> <span class="keyword">template</span> <> <span class="keyword">struct </span>arpack_wrapper<double, double></div>
<div class="line"><a name="l00724"></a><span class="lineno"> 724</span> {</div>
<div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> saupd(<span class="keywordtype">int</span> *ido, <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which,</div>
<div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keywordtype">int</span> *nev, <span class="keywordtype">double</span> *tol, <span class="keywordtype">double</span> *resid, <span class="keywordtype">int</span> *ncv,</div>
<div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <span class="keywordtype">double</span> *v, <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr,</div>
<div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <span class="keywordtype">double</span> *workd, <span class="keywordtype">double</span> *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *info)</div>
<div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  {</div>
<div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  dsaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);</div>
<div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  }</div>
<div class="line"><a name="l00732"></a><span class="lineno"> 732</span> </div>
<div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> seupd(<span class="keywordtype">int</span> *rvec, <span class="keywordtype">char</span> *All, <span class="keywordtype">int</span> *select, <span class="keywordtype">double</span> *d,</div>
<div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="keywordtype">double</span> *z, <span class="keywordtype">int</span> *ldz, <span class="keywordtype">double</span> *sigma,</div>
<div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordtype">char</span> *bmat, <span class="keywordtype">int</span> *n, <span class="keywordtype">char</span> *which, <span class="keywordtype">int</span> *nev,</div>
<div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordtype">double</span> *tol, <span class="keywordtype">double</span> *resid, <span class="keywordtype">int</span> *ncv, <span class="keywordtype">double</span> *v,</div>
<div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keywordtype">int</span> *ldv, <span class="keywordtype">int</span> *iparam, <span class="keywordtype">int</span> *ipntr, <span class="keywordtype">double</span> *workd,</div>
<div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="keywordtype">double</span> *workl, <span class="keywordtype">int</span> *lworkl, <span class="keywordtype">int</span> *ierr)</div>
<div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  {</div>
<div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  dseupd_(rvec, All, select, d, v, ldv, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr,</div>
<div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  workd, workl, lworkl, ierr);</div>
<div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  }</div>
<div class="line"><a name="l00743"></a><span class="lineno"> 743</span> };</div>
<div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div>
<div class="line"><a name="l00745"></a><span class="lineno"> 745</span> </div>
<div class="line"><a name="l00746"></a><span class="lineno"> 746</span> <span class="keyword">template</span><<span class="keyword">typename</span> MatrixSolver, <span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> Scalar, <span class="keywordtype">bool</span> BisSPD></div>
<div class="line"><a name="l00747"></a><span class="lineno"> 747</span> <span class="keyword">struct </span>OP</div>
<div class="line"><a name="l00748"></a><span class="lineno"> 748</span> {</div>
<div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> applyOP(MatrixSolver &OP, <span class="keyword">const</span> MatrixType &A, <span class="keywordtype">int</span> n, Scalar *in, Scalar *out);</div>
<div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> project(MatrixSolver &OP, <span class="keywordtype">int</span> n, <span class="keywordtype">int</span> k, Scalar *vecs);</div>
<div class="line"><a name="l00751"></a><span class="lineno"> 751</span> };</div>
<div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div>
<div class="line"><a name="l00753"></a><span class="lineno"> 753</span> <span class="keyword">template</span><<span class="keyword">typename</span> MatrixSolver, <span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> Scalar></div>
<div class="line"><a name="l00754"></a><span class="lineno"> 754</span> <span class="keyword">struct </span>OP<MatrixSolver, MatrixType, Scalar, true></div>
<div class="line"><a name="l00755"></a><span class="lineno"> 755</span> {</div>
<div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> applyOP(MatrixSolver &OP, <span class="keyword">const</span> MatrixType &A, <span class="keywordtype">int</span> n, Scalar *in, Scalar *out)</div>
<div class="line"><a name="l00757"></a><span class="lineno"> 757</span> {</div>
<div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="comment">// OP = L^{-1} A L^{-T} (B = LL^T)</span></div>
<div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <span class="comment">// First solve L^T out = in</span></div>
<div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixU().solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));</div>
<div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationPinv() * Matrix<Scalar, Dynamic, 1>::Map(out, n);</div>
<div class="line"><a name="l00764"></a><span class="lineno"> 764</span> </div>
<div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="comment">// Then compute out = A out</span></div>
<div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(out, n);</div>
<div class="line"><a name="l00768"></a><span class="lineno"> 768</span> </div>
<div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="comment">// Then solve L out = out</span></div>
<div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationP() * Matrix<Scalar, Dynamic, 1>::Map(out, n);</div>
<div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixL().solve(Matrix<Scalar, Dynamic, 1>::Map(out, n));</div>
<div class="line"><a name="l00773"></a><span class="lineno"> 773</span> }</div>
<div class="line"><a name="l00774"></a><span class="lineno"> 774</span> </div>
<div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> project(MatrixSolver &OP, <span class="keywordtype">int</span> n, <span class="keywordtype">int</span> k, Scalar *vecs)</div>
<div class="line"><a name="l00776"></a><span class="lineno"> 776</span> {</div>
<div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <span class="comment">// Solve L^T out = in</span></div>
<div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.matrixU().solve(Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k));</div>
<div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.permutationPinv() * Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k);</div>
<div class="line"><a name="l00781"></a><span class="lineno"> 781</span> }</div>
<div class="line"><a name="l00782"></a><span class="lineno"> 782</span> </div>
<div class="line"><a name="l00783"></a><span class="lineno"> 783</span> };</div>
<div class="line"><a name="l00784"></a><span class="lineno"> 784</span> </div>
<div class="line"><a name="l00785"></a><span class="lineno"> 785</span> <span class="keyword">template</span><<span class="keyword">typename</span> MatrixSolver, <span class="keyword">typename</span> MatrixType, <span class="keyword">typename</span> Scalar></div>
<div class="line"><a name="l00786"></a><span class="lineno"> 786</span> <span class="keyword">struct </span>OP<MatrixSolver, MatrixType, Scalar, false></div>
<div class="line"><a name="l00787"></a><span class="lineno"> 787</span> {</div>
<div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> applyOP(MatrixSolver &OP, <span class="keyword">const</span> MatrixType &A, <span class="keywordtype">int</span> n, Scalar *in, Scalar *out)</div>
<div class="line"><a name="l00789"></a><span class="lineno"> 789</span> {</div>
<div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  eigen_assert(<span class="keyword">false</span> && <span class="stringliteral">"Should never be in here..."</span>);</div>
<div class="line"><a name="l00791"></a><span class="lineno"> 791</span> }</div>
<div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div>
<div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> project(MatrixSolver &OP, <span class="keywordtype">int</span> n, <span class="keywordtype">int</span> k, Scalar *vecs)</div>
<div class="line"><a name="l00794"></a><span class="lineno"> 794</span> {</div>
<div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  eigen_assert(<span class="keyword">false</span> && <span class="stringliteral">"Should never be in here..."</span>);</div>
<div class="line"><a name="l00796"></a><span class="lineno"> 796</span> }</div>
<div class="line"><a name="l00797"></a><span class="lineno"> 797</span> </div>
<div class="line"><a name="l00798"></a><span class="lineno"> 798</span> };</div>
<div class="line"><a name="l00799"></a><span class="lineno"> 799</span> </div>
<div class="line"><a name="l00800"></a><span class="lineno"> 800</span> } <span class="comment">// end namespace internal</span></div>
<div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div>
<div class="line"><a name="l00802"></a><span class="lineno"> 802</span> } <span class="comment">// end namespace Eigen</span></div>
<div class="line"><a name="l00803"></a><span class="lineno"> 803</span> </div>
<div class="line"><a name="l00804"></a><span class="lineno"> 804</span> <span class="preprocessor">#endif // EIGEN_ARPACKSELFADJOINTEIGENSOLVER_H</span></div>
<div class="line"><a name="l00805"></a><span class="lineno"> 805</span> <span class="preprocessor"></span></div>
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