150 points by optimus_prime 5 months ago flag hide 18 comments
optimizer1 5 months ago next
Fascinating approach! I've been working on these types of problems for years and finally, something refreshing. Hoping to find an open-source implementation!
optimizer2 5 months ago next
@optimizer1 I totally agree! I'm surprised at how easily the authors parallelized this problem. Gonna read the paper to learn more.
datascientist123 5 months ago prev next
Definitely intriguing. Is there any formal analysis of the method in the paper? Trying to understand its convergence guarantees.
author1 5 months ago next
Hi @datascientist123, thank you for your questions. Yes, the paper includes formal analysis showing convergence guarantees under mild conditions. We Appendix A has information for weaker decompositions. Fingers crossed for an open-source release soonest.
mathgenius 5 months ago next
@author1 The approach is quite amazing, and I'm excited to apply it to the variety of problems I'm working on. Thank you for your contribution to the field!
newuser67 5 months ago prev next
I heard of a similar concept for smaller scale optimization problems. But this is really cool to see it for large-scale problems.
optimizer1 5 months ago next
@newuser67 The original concept you're thinking of might have inspired this. We've seen similar trends, but this takes it to another level. Awesome stuff!
ai_expert 5 months ago prev next
I worked on a project last year that faced a similar challenge. We solved it differently but could definitely have used this method. Kudos!
newkid001 5 months ago prev next
This reminds me of a method I once read in a blog post. But I failed to replicate it. Can someone shed light on the implementation here?
optimizer3 5 months ago next
@newkid001 I too have felt the same. We noticed discrepancies as well. Hopefully, someone in the community can help us connect the dots.
optimizer4 5 months ago next
I suspect opportunities to iterate and improve this method will continue to emerge as we delve deeper into its details. Exciting! @newkid001
algoqueen 5 months ago prev next
A very enlightening article indeed. It will be interesting to see how this affects other ML algorithms and applications beyond the bounded issues mentioned.
profgary 5 months ago prev next
This can pose a considerable improvement for the computational complexity of solving large-scale combinatorial optimization issues.
bigdatabob 5 months ago next
Absolutely professor! Even with von Neumann's minimax, there's potential for better strategies, which can help solve more complex problems. I'm optimistic!
resourcesguru 5 months ago prev next
Wonderful read. Bookmarking this. I'll create an educational article based on this post for those who are still learning the ropes. Thanks, community!
codewizard 5 months ago prev next
I'd like to see this tested against other popular solvers like Gurobi, CPLEX, and Mosek. Could the authors make the solver accessible for the public to try out?
author1 5 months ago next
Hi @codewizard, we appreciate your input. We plan to release an open-source implementation soon, so you can try it for yourself and compare solutions. Stay tuned!
neuronnetworks 5 months ago prev next
Scalability is essential to keep up with the growing demands in deep learning. Great advancement in this aspect.