250 points by optimization_whiz 1 year ago flag hide 18 comments
johnappleseed 1 year ago next
Fascinating approach, can't wait to test it on my latest large-scale problem. Kudos to the team!
codewizz 1 year ago next
Absolutely agree with you, JohnAppleseed. How does it compare to gradient descent in terms of speed and accuracy?
johnappleseed 1 year ago next
@codewizz I would say the new approach is a bit slower than gradient descent but typically generates more accurate results, especially in high dimensional spaces.
algoexpert 1 year ago prev next
We've tested it alongside other popular optimization algorithms and the results showcased a performance edge for high-dimensional problems.
johnappleseed 1 year ago next
@algoexpert Impressive! Have you tried combining it with other methods for a potentially more significant boost?
algoexpert 1 year ago next
@JohnAppleseed We've attempted to mix this one with several other approaches like simulated annealing and it indeed yielded further improvements.
mathbeast 1 year ago prev next
The article doesn't mention the complexity class. Could the authors elaborate on it in the next iteration?
cqfc 1 year ago next
I assume you refer to the time and space complexity. From what I observed, this method operates in O(n^3) time and O(n^2) space, which might be a bottleneck for extremely large problems.
codeheart 1 year ago next
I wonder what adjustments can be made to decrease the time and space requirements and increase applicability across more domains?
johnappleseed 1 year ago next
@codeheart Definitely worth thinking about. Let's see if some bright minds find a way to optimize the algorithm further and extend it to other domains.
mathman 1 year ago prev next
Any thoughts on potential parallelism to overcome complexity hurdles? Perhaps GPU acceleration?
mathbeast 1 year ago next
@mathman Research is underway to leverage parallel computation and GPU technology, with promising preliminary inventions in the pipeline.
goku 1 year ago prev next
Before we dive deep, is this method able to circumvent local minima trapping issues of traditional methods?
vectorqueen 1 year ago next
@goku The team made their constant-factor improvements based on gradient-based solvers, shrinking the chances of converging to a local minimum.
goku 1 year ago next
@vectorqueen That's reassuring. I'm curious to see empirical evidence in larger optimization test-cases and more varied datasets.
vectorqueen 1 year ago next
@goku The approach has already showcased its potential with many real-world cases. I'm optimistic about its future impact.
haskellwager 1 year ago prev next
A novel technique involves injecting randomness to escape local minima. We'll see more about this in future publications, I reckon.
atsuyaku 1 year ago next
I'm also looking forward to the upcoming developments regarding exploiting randomness for optimization performance.