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Hello!<br>
I want to acquire the 3 smallest eigenvalue, and attachment is the log view output. I can see epssolve really cost the major time. But I can not see why it cost so much time. Can you see something from it?<br><br>Thank you !
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<div style="font-family:"微软雅黑"; font-size: 14px; color:#000000">Runfeng Jin</div>
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<div style="font-size:12px;line-height:1.5;word-break:break-all;margin-left:10px;margin-right:10px">On <span class="mail-date">6/4/2022 01:37</span>，<a class="mail-to" style="text-decoration:none;color:#2a83f2;" href="mailto:jroman@dsic.upv.es">Jose E. Roman<jroman@dsic.upv.es></a> wrote： </div>
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Convergence depends on distribution of eigenvalues you want to compute. On the other hand, the cost also depends on the time it takes to build the preconditioner. Use -log_view to see the cost of the different steps of the computation.<br><br>Jose<br><br><br> <blockquote class="mmbqc1">El 3 jun 2022, a las 18:50, jsfaraway <jsfaraway@gmail.com> escribió:<br> <br> hello!<br> <br> I am trying to use epsgd compute matrix's one smallest eigenvalue. And I find a strang thing. There are two matrix A(900000*900000) and B(90000*90000). While solve A use 371 iterations and only 30.83s, solve B use 22 iterations and 38885s! What could be the reason for this? Or what can I do to find the reason?<br> <br> I use" -eps_type gd -eps_ncv 300 -eps_nev 3 -eps_smallest_real ". <br> And there is one difference I can tell is matrix B has many small value, whose absolute value is less than 10-6. Could this be the reason?<br> <br> Thank you!<br> <br> Runfeng Jin <br></blockquote><br></blockquote><!--😀-->
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