rcorless

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3 years, 228 days

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Editor-in-Chief of Maple Transactions (www.mapletransactions.org), longtime Maple user (1st use 1981, before Maple was even released). Most obscure piece of the library that I wrote? Probably `convert/MatrixPolynomialObject` which is called by LinearAlgebra[CompanionMatrix] to compute linearizations of matrix polynomials in several different bases. Do not look at the code. Seriously. Do not look. You have been warned.

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These are Posts that have been published by rcorless

Maple Transactions frequently gets submissions that contain Maple code.  The papers (or videos, or Maple documents, or Jupyter notebooks) that we get are, if the author wants a refereed submission, sent to referees by a fairly usual academic process.  We look for well-written papers on topics of interest to the Maple community.

But we could use some help in reviewing code, for some of the submissions.  Usually the snippets are short, but sometimes the packages involved are more substantial.

If you would be interested in having your name on the list of potential code reviewers, please email me (or Paulina Chin, or Jürgen Gerhard) and we will gratefully add you.  You might not get called on immediately---it depends on what we have in the queue.

Thank you very much, in advance, for sharing your expertise.

Rob

My friend and colleague Nic Fillion and I are writing another book, this one on perturbation methods using backward error analysis (and Maple).  We have decided to make the supporting materials available by means of Jupyter notebooks with a Maple kernel (there are some Maple worksheets and workbooks already, but going forward we will use Jupyter).

The presentation style is meant to aid reproducibility, and to allow others to solve related problems by changing the scripts as needed.

The first one is up at 

https://github.com/rcorless/Perturbation-Methods-in-Maple

Comments very welcome.  This particular method is a bit advanced in theory (but it's very simple in practice, for weakly nonlinear oscillators).  I haven't coded for efficiency and there may be some improvements possible ("may" he says, sheesh).  Comments on that are also welcome.

-r

In the most recent issue of Maple Transactions, I published (with David Jeffrey, and with a student named Johan Joby) a paper that used Jupyter Notebook with a Maple kernel as the main vehicle.  Have a look, and let me know what you think.

Two-cycles in the infinite exponential tower

The Proceedings of the Maple Conference 2022 are up at mapletransactions.org and I hope that you will find the articles interesting.  There is a brief memorial to Eugenio Roanes-Lozano, whom some of you will remember from past meetings. 

The cover image was the "People's Choice" from the Art Gallery, by Paul DeMarco.

This provides a nice excuse to remind you to register at the conference page for the Maple Conference 2023 and in particular to remind you to submit your entries for the Art Gallery.  See you there!  The conference will take place October 26 and 27, and features plenary talks by our own Laurent Bernardin and by Tom Crawford (Oxford, but more widely known as "The Naked Mathematician" for his incredibly popular YouTube videos on mathematical topics). See Tom Rocks Maths for more (or less :)

The deadline for submission to the Proceedings (which will again be published in Maple Transactions) will be Nov 27, one month after the conference ends.  We have put new processes in place to ensure a more timely publication schedule, and we anticipate that the Proceedings will be published in early Spring 2024.

Just installing Maple 2023 on my office machine (a mac); installed it on my travel computer (a Surface Pro running Windows) yesterday.

Configured Jupyter notebooks to use the 2023 Maple Kernel and it all went smoothly.  I was *delighted* to notice that plotting Lambert W in Jupyter with the command

plot( [W(x), W(-1,x)], x=-1..4, view=[-1..4, -3.5..1.5], colour=[red,blue], scaling=constrained, labels=[x,W(x)] );

produced a *better* plot near the branch point.  This is hard to do automatically!  It turns out this is a side effect of the better/faster/more memory efficient adaptive plotting software, which I gather from "What's New" was written for efficiency not for quality.  But the quality is better, too!  Nice!

I am working my. way through the "What's New" and I'm really pleased to learn about the new univariate polynomial rootfinder, *not least because it cites the paper describing the algorithm*.  Lots of other goodies too; the new methods of integration look like serious improvements.  Well done. (One thing there: "parallel Risch" is a term of art, and may lead people to believe that Maple is doing something with parallel computing there.  I don't think so.  Could a reference be supplied?)

The new colour schemes and plotting features in 3d and contour plotting look fabulous.

Direct Python language support from a code edit region is not at all what I expected to see---I wonder if it will work in a Jupyter notebook?  I'm going to have to try it...

I'm quite impressed.  The folks at Maplesoft have been working very hard indeed.  Congratulations on a fine release!

 

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