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10 posts tagged with computerscience *and* math.

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## Time with class! Let's Count!

I want to demonstrate how amazing combinatorial explosion is! Please don't stop me. An animation about numbers that get large. It has a happy ending and possibly even a moral. [more inside]

## It's just a jump to the ... well, in any legal direction really

The Peg Solitaire Army is a problem spun off from a classic recreation, and yet another example of the golden ratio turning up where you least expect it. If you want to look at the game more deeply, George Bell's solitaire pages are the ne plus ultra: There's more about the solitaire army (and variants), ... [more inside]

## I Can Tell By The Pixels

Visualizing Algorithms 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."

"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."

## Letâ€™s not complicate things unnecessarily.

## Breaking the Coppersmith-Winograd barrier

*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^2.37*[more inside]

**3**) steps. [via]## Eleven Equations True Computer Science Geeks Should (at Least Pretend to) Know

## Never tell me the odds.

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's blog 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 and statistics.

## The On-Line Encyclopedia of Integer Sequences

Ever wondered what comes next, and why? The On-Line Encyclopedia of Integer Sequences has the answers. (Previously.)

## Big book of algorithms

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.

## Who can name the bigger number?

Who can name the bigger number? I guarantee you will lose to the Busy Beavers. (No, infinity is not allowed, the bigger infinity is a different game.) The author also debunks in very simple terms the recent story that quantum computers perform calculations without being turned on. My first post and disclaimer: I know the author from our mutual field of quantum information.

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