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Article overview
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Universality in halting time and its applications in optimization | | Levent Sagun
; Thomas Trogdon
; Yann LeCun
; | | Date: |
20 Nov 2015 | | Abstract: | The authors present empirically universal distributions for the halting time
(measured by the number of iterations to reach a given accuracy) of
optimization algorithms applied to at least two systems: spin glasses and deep
learning. Given an algorithm, the fluctuations of the halting time follow a
universal distribution when the system is well tuned. This universality of the
distribution is demonstrated by our computations: it is independent of input
data and dimension. When the system is not well tuned this type of universality
is destroyed. What makes this observation practically relevant is that the
halting time fluctuations do not follow the universal distribution because
either time is wasted for no gain in accuracy or the algorithm stops
prematurely giving inaccurate results. This is consistent with the observations
of Deift et al. (2015) in an analysis of the conjugate gradient algorithm. | | Source: | arXiv, 1511.6444 | | Services: | Forum | Review | PDF | Favorites | |
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