Archive for algorithms
algorithmic complexity
Posted in Books, Kids, Statistics, University life with tags algorithms, computational complexity, computing time, webcomic, xkcd on July 31, 2024 by xi'an
matrix multiplication [cover]
Posted in Books, pictures, Statistics, University life with tags algorithms, AlphaTensor, cover, deep learning, deep neural network, DeepMind, Google, London, matrix algebra, matrix multiplication, Monte Carlo algorithm, Nature, reinforcement learning, tensor, UK on December 15, 2022 by xi'an
poems that solve puzzles [book review]
Posted in Books, Kids, University life with tags ACM Turing Award, Ada Lovelace, Alan Turing, algorithms, AlphaGo, book cover, book review, CHANCE, Charles Babbage, checkers, deep learning, difference engine, Dublin, Fourier transform, John Tukey, machine learning, Oxford University Press, puzzle, University College Dublin on January 7, 2021 by xi'an
Upon request, I received this book from Oxford University Press for review. Poems that Solve Puzzles is a nice title and its cover is quite to my linking (for once!). The author is Chris Bleakley, Head of the School of Computer Science at UCD.
“This book is for people that know algorithms are important, but have no idea what they are.”
These is the first sentence of the book and hence I am clearly falling outside the intended audience. When I asked OUP for a review copy, I was more thinking in terms of Robert Sedgewick’s Algorithms, whose first edition still sits on my shelves and which I read from first to last page when it appeared [and was part of my wife’s booklist]. This was (and is) indeed a fantastic book to learn how to build and optimise algorithms and I gain a lot from it (despite remaining a poor programmer!).
Back to poems, this one reads much more like an history of computer science for newbies than a deep entry into the “science of algorithms”, with imho too little on the algorithms themselves and their connections with computer languages and too much emphasis on the pomp and circumstances of computer science (like so-and-so got the ACM A.M. Turing Award in 19… and retired in 19…). Beside the antique algorithms for finding primes, approximating π, and computing the (fast) Fourier transform (incl. John Tukey), the story moves quickly to the difference engine of Charles Babbage and Ada Lovelace, then to Turing’s machine, and artificial intelligence with the first checkers codes, which already included some learning aspects. Some sections on the ENIAC, John von Neumann and Stan Ulam, with the invention of Monte Carlo methods (but no word on MCMC). A bit of complexity theory (P versus NP) and then Internet, Amazon, Google, Facebook, Netflix… Finishing with neural networks (then and now), the unavoidable AlphaGo, and the incoming cryptocurrencies and quantum computers. All this makes for pleasant (if unsurprising) reading and could possibly captivate a young reader for whom computers are more than a gaming console or a more senior reader who so far stayed wary and away of computers. But I would have enjoyed much more a low-tech discussion on the construction, validation and optimisation of algorithms, namely a much soft(ware) version, as it would have made it much more distinct from the existing offer on the history of computer science.
[Disclaimer about potential self-plagiarism: this post or an edited version of it will eventually appear in my Books Review section in CHANCE.]
reading pile for X break
Posted in Books, pictures, Statistics, Travel, University life with tags AIQ, algorithms, beauty, book reviews, Cahiers du CNRS, CNRS, computer science, Deborah Mayo, England, mathematics, NIck Polson, reading list, Scotland, severe testing, University of Warwick on December 28, 2018 by xi'an
accelerating MCMC
Posted in Books, Statistics, University life with tags acceleration of MCMC algorithms, algorithms, arXiv, cross validated, MCMC, Monte Carlo Statistical Methods, referee, simulation, Telecom Lille, typology, Université Paris Dauphine, University of Warwick, WIREs on April 11, 2018 by xi'an
As forecasted a rather long while ago (!), I wrote a short and incomplete survey on some approaches to accelerating MCMC. With the massive help of Victor Elvira (Lille), Nick Tawn (Warwick) and Changye Wu (Dauphine). Survey which current version just got arXived and which has now been accepted by WIREs Computational Statistics. The typology (and even the range of methods) adopted here is certainly mostly arbitrary, with suggestions for different divisions made by a very involved and helpful reviewer. While we achieved a quick conclusion to the review process, suggestions and comments are most welcome! Even if we cannot include every possible suggestion, just like those already made on X validated. (WIREs stands for Wiley Interdisciplinary Reviews and its dozen topics cover several fields, from computational stats to biology, to medicine, to engineering.)
