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Revolutionary Approach to Solving Large Scale Optimization Problems(example.com)

1 point by opti_solvr 1 year ago | flag | hide | 16 comments

  • john_doe 1 year ago | next

    Just saw this on HN, really interesting approach to optimization. Excited to see how this can be applied in real-world scenarios.

    • optimize_fan 1 year ago | next

      Agreed, optimization problems are always interesting. I have been researching in the field for some time and this is a unique approach.

      • optimize_fan 1 year ago | next

        Couldn't agree more, John. I think this has the potential to solve complex problems in a more efficient way. Looking forward to learning more!

    • nerd_alert 1 year ago | prev | next

      I agree with the hype but we need to see actual use cases before getting carried away. Skepticism is healthy for progress.

  • just_curious 1 year ago | prev | next

    Can anyone give a simple explanation of how this new approach claims to be revolutionary? Would love to learn more.

    • algo_guru 1 year ago | next

      Sure - it utilizes a new algorithm that's able to scan millions of potential solutions in just seconds, making it ideal for large-scale optimization problems.

      • jane_123 1 year ago | next

        Impressive. Any insight into what kind of large-scale problems this could solve more effectively and efficiently?

        • john_doe 1 year ago | next

          I think inventory management or transportation logistics could be suitable areas for implementation. Curious to hear what others think about this.

      • dan_optimizer 1 year ago | prev | next

        Indeed, I could also see this being helpful for electrical grid optimization, since it's a highly complex domain with many variables and constant changes.

  • ml_engineer 1 year ago | prev | next

    Neat. Is it possible to combine this with machine learning, and how would one go about doing this?

    • algo_guru 1 year ago | next

      Definitely possible, and I think ML would only enhance the optimization process. You could potentially use machine learning models to predict outcomes and feed them as inputs to this new algorithm.

      • jane_123 1 year ago | next

        Interesting. Do you have any recommended resources or articles for learning more about this specific application?

        • algo_guru 1 year ago | next

          You might want to check out this recent arxiv paper on combining optimization and machine learning. I think this is a great starting point: <https://arxiv.org/abs/xxxx>

  • speed_up 1 year ago | prev | next

    Does anyone know if this algorithm is parallelizable for distributed computing systems? That could exponentially increase the speed of calculations!

    • optimize_fan 1 year ago | next

      It's my understanding that the algorithm can be parallelized, but it would depend on the problem size and the distribution of the data.

    • algo_guru 1 year ago | prev | next

      Optimize_fan is correct. In theory, this algorithm can be parallelized and distributed, but implementation challenges might arise depending on the problem complexity and computing infrastructure. I would recommend studying the paper and its cited sources carefully to determine feasibility.