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< stephentu> naywhayare: you might find this amusing
< stephentu> so this is how you set up a maxcut SDP in mosek, using the *python* api
< stephentu> and here is how you do it in mlpack in C++: https://gist.github.com/stephentu/65b0f971edd8aa2d3124
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< naywhayare> stephentu: wow, that is a lot nicer
< naywhayare> maybe we should turn that into a nice advertisement blog post or something? :)
< stephentu> naywhayare: not quite there yet, mosek is still much faster
< stephentu> haha
< stephentu> turns out writing interior point solvers well is actually hard
< stephentu> hence being able to start companies
< stephentu> based on them
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< naywhayare> stephentu: I was screwing around with L-BFGS, making LineSearch() void (it fails, just like you said)
< naywhayare> do you think that your implementation is robust enough to handle most LRSDP problems? (for some definition of "most"...)
< stephentu> naywhayare: is the question whether or not L-BFGS is robust, or the outer loop augmented lagrangian method is robust?
< stephentu> i think L-BFGS might be ok, but certainly the augmented lagrangian method is not
< stephentu> LR-SDP is really hit or miss, and requires hand holding for each new problem
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< naywhayare> stephentu: I believe L-BFGS is robust, so I was asking more about LRSDP itself
< naywhayare> I'd like to keep the hand-holding out of the L-BFGS class itself if possible, so I'm curious if I might get it to converge by just tweaking some L-BFGS parameters
< naywhayare> if we can make LineSearch() void and remove a bunch of the checks, we can probably accelerate each L-BFGS iteration (maybe not much; not certain)
< naywhayare> but if keeping it in the state it is in your lbfgs branch is what's necessary to make it reliably converge, that's fine
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