How Does MIT's New Algorithm Revolutionize Optimization Speeds?

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Discussion Overview

The discussion centers on a new cutting-plane algorithm developed by MIT graduate students, which is claimed to improve optimization speeds for various engineering applications. Participants explore the implications of this algorithm for optimization problems in fields such as control systems and machine learning.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • Some participants highlight the significance of optimization problems in engineering, noting their relevance in design tradeoffs, scheduling, and machine learning.
  • One participant mentions that the new algorithm is a general-purpose solution that reportedly improves on the running time of its predecessor.
  • Another participant humorously references the variety of algorithms in existence, suggesting a broader context of algorithm development.
  • A later reply clarifies that the focus is specifically on an algorithm designed for optimization rather than optimizing existing algorithms.
  • Participants note that the algorithm may offer order-of-magnitude speedups for certain problems, potentially enhancing solution-finding processes.

Areas of Agreement / Disagreement

Participants express interest in the advancements presented, but there appears to be some confusion regarding the specific focus of the algorithm, indicating a lack of consensus on the details of its application.

Contextual Notes

Some statements reflect uncertainty about the algorithm's theoretical limits and its practical implications, as well as the distinction between optimizing algorithms and algorithms for optimization.

Who May Find This Useful

Readers interested in optimization techniques, algorithm development, and applications in engineering and machine learning may find this discussion relevant.

Greg Bernhardt
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Optimization problems are everywhere in engineering: Balancing design tradeoffs is an optimization problem, as are scheduling and logistical planning. The theory — and sometimes the implementation — of control systems relies heavily on optimization, and so does machine learning, which has been the basis of most recent advances in artificial intelligence.

This week, at the IEEE Symposium on Foundations of Computer Science, a trio of present and past MIT graduate students won a best-student-paper award for a new “cutting-plane” algorithm, a general-purpose algorithm for solving optimization problems. The algorithm improves on the running time of its most efficient predecessor, and the researchers offer some reason to think that they may have reached the theoretical limit.

http://news.mit.edu/2015/faster-optimization-algorithm-1023

PDF: http://arxiv.org/pdf/1508.04874v1.pdf
 
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LOL!
scnr
Edit: was a reference to a post that got deleted.

Good to see more progress on optimization. There are so many crazy algorithms, e. g. for multiplication.
 
Last edited:
No mfb, this isn't about optimising algorithms, it's about an algorithm for optimisation!
 
New general-purpose optimization algorithm promises order-of-magnitude speedups on some problems.
(fromt the link below the quote)
=> it can be used to speed up finding solutions to problems.
 

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