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\begin{document}
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{\Large המחלקה למתמטיקה, בן-גוריון}

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{\Huge קולוקוויום}\\[0.2\baselineskip]

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\textbf{ב}\emph{יום שלישי, 10 בינואר, 2017}
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\textbf{בשעה} \emph{14:30 -- 15:30}
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\textbf{ב}\emph{Math -101}

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ההרצאה

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{\Large\bfseries Universality in numerical computations with random data. Case studies\par}
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תינתן על-ידי
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{\large\scshape Percy Deift  
  %
  (NYU)
}
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\textbf{תקציר:}
  This is joint work with Govind Menon, Sheehan Olver and
Thomas Trogdon. The speaker will  present evidence for universality in
numerical computations with random data. Given a (possibly stochastic)
numerical algorithm

with random input data, the time (or number of iterations) to
convergence (within a given tolerance) is a random variable, called the
halting time. Two-component universality is observed for the
fluctuations of the halting time, i.e., the

histogram for the halting times, centered by the sample average and
scaled by the sample variance, collapses to a universal curve,
independent of the input data distribution, as the dimension increases.
Thus, up to two components,

the sample average and the sample variance, the statistics for the
halting time are universally prescribed. The case studies include six
standard numerical algorithms, as well as a model of neural computation
and decision making.
  


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