This article was ported from my old Wordpress blog here, If you see any issues with the rendering or layout, please send me an email. The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It’s used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this ser
Some of the first Rust code I wrote was a struct with a &str field. As you might imagine, the borrow checker didn’t let me do a lot of things, and the API ergonomics were limited. This article aims to demonstrate the issues with having raw &str references in structs, introduce some intermediate APIs that alleviate the ergonomics but aren’t necessarily efficient, and end with an implementation that
The J programming language is like Forth. Both are old-school and obscure. Both have quirky syntax thanks to a dead simple parser and unorthodox choices. Both encourage point-free style. Both insist on terminology that evokes comparisons with natural languages, e.g. Forth calls functions words, while J calls them verbs. However, several features make a J interpreter more challenging to write than
Comonads are the category-theoretic dual of monads. While monads have been found to be a very useful design pattern for structuring programs in functional languages, such as Haskell, comonads have so far failed to gain much traction. In this talk we will look at what comonads are and how they are defined in Haskell, comparing them with the Haskell definition of monads. We will then look at some sp
We present a formalisation in Agda of the theory of concurrent transitions, residuation, and causal equivalence of traces for the pi-calculus. Our formalisation employs de Bruijn indices and dependently-typed syntax, and aligns the "proved transitions" proposed by Boudol and Castellani in the context of CCS with the proof terms naturally present in Agda's representation of the labelled transition
Python is somewhat poorly suited for game development. However it is well suited to data-science. A lot of our tasks, working with large voxels sets, strongly resemble the kind of problems you encounter in data science. The big advantage comes from limiting "real-time" state. By not relying on anything having to happen now, we can do more complicated computation. At the low-end, that means we can
Futhark is a small programming language designed to be compiled to efficient parallel code. It is a statically typed, data-parallel, and purely functional array language in the ML family, and comes with a heavily optimising ahead-of-time compiler that presently generates either GPU code via CUDA and OpenCL, or multi-threaded CPU code. As a simple example, this function computes the average of an a
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