1 point by opti_queen 1 year ago flag hide 18 comments
opt_master 1 year ago next
[HN Top Story] Check out this revolutionary approach to solving large scale optimization problems. It's a game changer for sure! https://example.com/opt-solution
mysterious_hacker 1 year ago next
Great find! Can't wait to share this with my team. Thanks for posting!
quant_queen 1 year ago next
We might also see improvements in the energy efficiency sector where large-scale optimization is key. It's exciting to see how technology progresses!
green_geek 1 year ago next
Indeed! More efficient algorithms lead to less power consumption and quicker processing speeds, supporting a greener tech industry.
algorithm_wiz 1 year ago prev next
Solving large scale optimization problems is tricky. I've seen a lot of failed attempts. I'm curious to read the article to learn more.
data_junkie 1 year ago next
Here's an overview of the article: https://example.com/opt-solution-summary. Unlike previous approaches, this method breaks down the problem into smaller sub-problems that can be solved in parallel, resulting in faster, efficient, and optimal solutions.
efficient_hacker 1 year ago next
That's interesting! I've used a similar technique for smaller problems and it yielded good results. I'm wondering how it scales for even larger problems.
code_monk 1 year ago prev next
I've heard promising reviews about this technique from some dev friends. May the optimization become easier for us all!
num_master 1 year ago prev next
How would you compare the timing complexity of this method with existing ones? Has it been studied rigorously?
opt_builder 1 year ago next
The solution uses a breakthrough implementation of *Smart Partition Algorithms* and an adaptive strategy for re-combining the sub-problems. It reduces the space complexity, which comes with significant speed improvements.
paral_pro 1 year ago next
I'm currently exploring parallel computation in my project. Can you explain a bit more about the adaptive strategy for re-combining the sub-problems?
opt_builder 1 year ago next
*Dynamic Local Re-alignment* adapts to significant changes from an initial solution, leveraging parallelism for both local and global optimizations. Clutched from an academic study on HPC, this method is a real innovation for large scale optimizations.
paral_pro 1 year ago next
That sounds fascinating! I'd be interested in learning about any resources or papers that reviewed this academic work. I'm researching on HPC for my thesis.
math_fascinator 1 year ago prev next
Seeing new, prominent optimization techniques always excites me. I'll be checking out this solution, as it appears to be a seamless blend of mathematical innovation and cutting edge technology.
innovation_promoter 1 year ago next
@math_fascinator @opt_master Great minds think alike. This post reminds me of some interesting applications for solving graph-based problems and resource allocation that involve linear and integer programming optimization algorithms from some peers.
math_fascinator 1 year ago next
The open-source optimization frameworks like Coopr, Dlib, Pyomo, and Bonmin can be great starting points for the curiosity buds wanting to try their hands on such new techniques.
efficient_hacker 1 year ago next
Be sure also to check out newer tools such as TensorFlow Optimizer (tf.keras.optimizers), PyTorch's Adam, and Numba if you're looking for computational power with efficiency.
math_fascinator 1 year ago next
And for metaheuristics and NSGA-III, R and Python are always go-to languages.