First commercially available Fortran compiler for Apple Silicon Macs now available to download

Machine learning is becoming ever more powerful and prevalent in the modern world, and is being used in all kinds of places from cutting-edge science to computer games, and self-driving cars to food production. However, it is a computationally-intensive process – particularly for the initial training stage of the model, and almost universally requires expensive GPU hardware to complete the training in a reasonable length of time.

Linear systems involving Toeplitz matrices arise in many applications, including differential and integral equations and signal and image processing (see, e.g., this article and the books by Ng, and Chan and Jin). More recently, Toeplitz systems have appeared in discretisations of fractional diffusion problems.

NAG has a new Python package for accessing the Library. It has been a few person years in the making and is a substantial improvement over its predecessors in terms of usability.
This post splits into two sections: the first covers some technical details, history and context about what’s happened behind the scenes with the package; the second jumps into some examples of how to use it.

We delve a little more into algorithmic or automatic differentiation.

The index-tracking problem is the problem of reproducing the performance of a stock market index by considering a portfolio of assets comprised on the index.

In honour of the outstanding contributions of James Hardy Wilkinson to the field of numerical software, Argonne National Laboratory, the National Physical Laboratory, and the Numerical Algorithms Group award the Wilkinson Prize for Numerical Software (US $3000).