Universality in numerical computations with random data

Percy A. Deift, Govind Menon, Sheehan Olver, Thomas Trogdon

Research output: Contribution to journalArticlepeer-review


The authors 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 aswell as a model of neural computation and decision-making. A link to relevant software is provided for readers who would like to do computations of their own.

Original languageEnglish (US)
Pages (from-to)14973-14978
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number42
StatePublished - Oct 21 2014


  • Decision times
  • Numerical analysis
  • Random matrix theory

ASJC Scopus subject areas

  • General


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