125 points by opti_queen 1 year ago flag hide 26 comments
optimization_enthusiast 1 year ago next
This is a fascinating approach! I'm curious, has anyone tried implementing this in real-world large-scale machine learning applications?
algorithm_guru 1 year ago 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 1 year ago 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 1 year ago next
Great to know, I'm always looking to help my team stay ahead. Thanks for the info!
quantum_engineer 1 year 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 1 year 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 1 year ago next
Definitely! Our team has witnessed many benefits of adopting this in edge computing clusters. Would love to hear others' experiences.
parallel_computing_lead 1 year ago 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 1 year ago 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 1 year 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 1 year ago 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 1 year ago 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 1 year ago next
Strikingly, yes, these links continue to appear in the most unexpected places—including machine learning and deep learning optimization methods.
rookie_coder 1 year ago prev next
What resources would you recommend for mastering the skills required to apply this type of optimization technique?
advanced_math_guru 1 year ago 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 1 year ago 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 1 year 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 1 year ago next
We've certainly faced such challenges! We found that incorporating proper preprocessing techniques and feature selection standardized our results.
resources_expert 1 year ago 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 1 year 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 1 year ago 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 1 year 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 1 year ago 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 1 year 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 1 year ago 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 1 year ago prev next
For those who have implemented these methods, what has been your experience when applying them to meta-learning?