Just an update on what I 've done with the project over the weekend. I watched some videos on youtube about how to decide whether a solution is a saddle point in a multivariable function...but it would take some hard work to generalize that method to nonlinear case. I've gone over the codebase of methods/mvu and optimizer over the weekend. I find the template initialization a bit confusing with lots of <<<>>> but the other parts are mostly fine. I will need to spend some time matching the algorithm suggested in the paper with the actual code we have but this shouldn't be hard. And currently I don't think any further constraints to the SDP class are needed. Best Regards Daniel Li ________________________________ From: Ryan Curtin <r...@ratml.org> Sent: 04 April 2018 20:53:52 To: LI Xuran Cc: email@example.com Subject: Re: [mlpack] MVU Bug Fix GSOC On Wed, Apr 04, 2018 at 10:01:11AM +0000, LI Xuran wrote: > Hey Ryan, > > Sorry for the ambiguity. Basically what I did was just a shortlist of > the major past topics related to LRSDP on Github. I didn't realise > that the sparse and dense constraint is already implemented, which is > great. > > By "detail constraints" I basically want to say is that it would be > helpful to have some dataset that is guaranteed to not have any saddle > point, i.e. having only local and global minima so that we can check > the convergence of the LRSDP on problems not involving saddle point. > Maybe it shouldn't be implemented as a constraint but just as > something to keep in mind during the generation of the data. > > since sparse and dense constraints have already been implemented in > MUV SDP then yeah I think a variadic template wouldn't be quite of > help in this case. I haven't give much thought to this over the past > few days as I was caught up with coursework and hackathon, but I will > look further into the code base and give a detailed suggestion on > whether it is possible to further constraint the question or pass on > some useful information to the SDP class this week. Hi Daniel, I hope the hackathon was enjoyable. Thanks for the clarifications here, I think I understand better now. I think it may be difficult to devise an LRSDP problem that has no saddle points (and prove that it has no saddle points). Thanks, Ryan -- Ryan Curtin | "Bye-bye, goofy woman. I enjoyed repeatedly r...@ratml.org | throwing you to the ground." - Ben Jabituya
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