shows you how computer algorithms can be represented visually, leading to better understanding of how the algorithms work:
"Have you ever implemented an algorithm based on formal description? It can be hard! Being able to see what your code is doing can boost productivity. Visualization does not supplant the need for tests, but tests are useful primarily for detecting failure and not explaining it. Visualization can also discover unexpected behavior in your implementation, even when the output looks correct."
posted by quiet earth
on Jun 26, 2014 -
For twenty years, the fastest known algorithm to multiply two n-by-n matrices, due to Coppersmith and Winograd, took a leisurely O(n^2.376) steps. Last year, though, buried deep in his PhD thesis, Andy Stothers discussed an improvement to O(n^2.374) steps. And today, Virginia Vassilevska Williams of Berkeley and Stanford, released a breakthrough paper [pdf] that improves the matrix-multiplication time to a lightning-fast O(n^2.373) steps. [via] [more inside]
posted by albrecht
on Nov 29, 2011 -
Measure-theoretic probability: Why it should be learnt and how to get started.
The clickable chart of distribution relationships.
Just two of the interesting and informative probability resources I've learned about, along with countless other tidbits of information, from statistician John D. Cook
and his probability fact-of-the-day Twitter feed ProbFact
. John also has daily tip and fact Twitter feeds for Windows keyboard shortcuts
, regular expressions
, TeX and LaTeX
, algebra and number theory
, topology and geometry
, real and complex analysis
, and beginning tomorrow, computer science
posted by grouse
on Dec 5, 2010 -
If you could use a great big free handbook of discrete math and algorithms, Jörg Arndt's fxtbook
wants to be your friend. Plain text table of contents
to whet your appetite.
posted by Wolfdog
on Mar 5, 2008 -