Hi Ryan,
>I took a quick look at your proposal and I think it is relatively clear >and sufficiently detailed. I am not clear on exactly what you mean by >"5. it might also be useful to write an algorithm to pre-process the >dataset to make it smoother and convex"---note that the LRSDP algorithm >breaks the convexity of SDPs and it is a nonconvex optimization. Thanks a lot for the feedback! Yeah, you are right about the LRSDP is a nonconvex optimization, what I intend to say is that during the process of sample generation, it might be helpful if we can have some constraints (say convex and closed? I am not too sure about the detail constraints yet but there is a theorem in the paper of local minima specifying it ) on the data to guarantee that an optimal solution can always be reached on the sample created, both by LRSDP and a dual solver. I 've gone through the rest of papers and Posts about LRSDP on Github during the past few days, as I am interested in what effort have been made to debug it. Do you think the following ideas would be helpful to the debug project: 1. refactor the SDP class to allow detail constraints specified in input. 2. create variable template to specify linear/sparse/dense constraints on input or A(constraint matrix) and support evaluation of Tr(A_i * UU^T) I didn't add these two to my proposal, but if you think implementing those would be useful than I can also look into it and take it as a part of the project. Maybe I can try to approach it during the community bound. Thanks again and Best Regards. Daniel Li ________________________________
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