<div dir="ltr"><div><div><div><div><br></div>Hi Chivalry,<br><br></div>convex + non-smooth problems often originate from minimizing the maximum of multiple smooth convex problems.<br></div><br>If this is the case in your instance, you can reformulate the problem by introducing additional variables containing the individual objective function values, and a set of inequality constraints.<br></div><div>The resulting instance is a smooth convex problem with convex constraints and as such amenable using IpOpt.<br></div><div><br></div>Sebastian<br></div><div class="gmail_extra"><br><div class="gmail_quote">On Fri, May 8, 2015 at 3:45 PM, <a href="mailto:key01023@gmail.com">key01023@gmail.com</a> <span dir="ltr"><<a href="mailto:key01023@gmail.com" target="_blank">key01023@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word"><div>Dear all,</div><div>I have a large class of non smooth convex optimization problems with about 8000 dimensions and I am looking for c++ solvers to solve that. I tried level method (extremely slow due to built up of sub-gradients), optimal gradient method due to Nesterov (very slow convergence), limited memory bundle method (LMBM) (no compilable solver available, failed to compile LMBM). I want to ask whether ipopt can be applied to solve this problem, because it uses limited memory BFGS which i guess somewhat resembles the LMBM methods. If so, is there some c++ file examples showing a case where we don’t need to provide the Hessian. Thank you.</div><br><div>
<div style="color:rgb(0,0,0);letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;word-wrap:break-word">With regards,<br></div><div style="color:rgb(0,0,0);letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;word-wrap:break-word">Chivalry</div></div></div><br>_______________________________________________<br>
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