Location: Maxwell Dworkin G115, 33 Oxford Street, Cambridge MA 02138

Speakers: Brian Hayes, IACS Associate

Time: Lunch 12:30pm; talk 1:00pm

 

Title:  Orderly Randomness: Quasirandom Numbers and Quasi–Monte Carlo

 

Abstract: Modern computing has an insatiable appetite for randomness. Cryptography and other kinds of adversarial computation demand “true” random numbers, which have three key properties: They are unpredictable, uncorrelated, and unbiased. Most other applications rely on pseudorandom numbers, which give up unpredictability but are still uncorrelated and unbiased. A third kind of randomness is even weaker. Quasirandom numbers are neither unpredictable nor uncorrelated; they claim only to be unbiased. They don’t even “look” random. Nevertheless, in some circumstances quasirandom numbers seem to be superior to pseudorandom ones. For example, they allow faster convergence or better error bounds in certain Monte Carlo simulations. Although quasirandom numbers have been known since the 1950s, some of their useful properties have been recognized only in the past few years, and they are not yet fully understood.  Free and open to the public. No registration required.

Speaker Bio: Brian Hayes is Senior Writer and columnist for American Scientist magazine and an Associate of SEAS. He writes mainly on mathematical and computational themes both for American Scientist and for his weblog bit-player.org. In the 1970s and 80s he was an editor of Scientific American, and later he edited American Scientist. He has been a visitor at the Mathematical Sciences Research Institute in Berkeley and at the International Centre for Theoretical Physics in Trieste. A collection of Hayes’s columns, titled Group Theory in the Bedroom, and Other Mathematical Diversions, was published by Hill and Wang in 2008. He is also the author of Infrastructure: A Guide to the Industrial Landscape (W. W. Norton, 2005; second edition 2014).

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UPCOMING SEMINARS

 

2/13    Ray Jones (IACS Lecturer) on "Connectomics: extracting neural connectivity from very large data sets"

 

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