As Maplesoft’s Chief Evangelist, I get countless opportunities to present the joys of Maple and our other products to people. Often, I’m the model corporate citizen and make sure I stay true to all of the key messaging that our Marketing folks force us to repeat … but if you actually experience one of my live presentations, you’ll notice that I often sneak in a whole slew of personal commentary and anecdotes on my 20 or so years with this technology … often stories that never make it to our official scripts. So as my inaugural blog post, I thought I’d start with what turns my crank when I have to present our products to the world. Here’s my Top 10 list of things that still impresses me to no end …

10. Inverse Laplace transform: this was my very first encounter with Maple's magic. I “discovered” this tool in Maple in the late 80’s as an undergrad and quickly discovered that I could do things in a second that took my friends over 10 minutes to do … fast forward 2 months … I got the same grade but I did manage a few more trips to the pub.

9. Linear transformations tutor. Available through the Tools -> Tutors -> Linear Algebra, it’s an interactive tool that illustrates various aspects of linear transform matrices through pictures, equations, and of course, matrices. Linear transformations are of those things that Mrs. Nathan tried to teach me in high school but I never quite got it … now I see the light …

8. Table data structure: as an engineer, I was familiar with arrays. Everything was an array … even scalars were arrays. Maple has this amazing ability to reference arbitrary indices. So f[2], f[-3.14], f[sin(x)+1] all have meaning in Maple. Powerful? Definitely. Useful? Yes, people tell me that it is and one day, I’ll find a really good application for it …

7. Linear graph theory:  as implemented in the multibody mechanical system tools in MapleSim. IMHO this unique approach to automatic model equation generation embodies the word “ubermagic”. Yes it saves bazillions of weeks in equation derivation but more importantly for me, it’s the first major implementation of linear graph theoretic physical system modeling, another bit of techno-exotica developed at my alma mater … I’ll probably write more on this in a later blog post.

6. Dr. Keith Geddes: One of the principal inventors of Maple. He’s a brilliant Saskatchewan boy who made it big in the exciting world of mathematics and software. He’s still active in his research and within the company as a Board member. His wife Debbie still remains as one of the most charming women I’ve ever met and his text book “Algorithms for Computer Algebra” remains one of the most expensive books that I almost bought.

5. “Infinite precision”: calculate to 10,000 or more decimal places? Better yet, keep it as to enjoy every last decimal place? Being able to control precision in software has always been a major part of the symbolic computation world. Even though most engineers like me would find pretty useless for the most part, and would much prefer 4.8 rounded to 5.0, it’s comforting to know that there’s a bit of arithmetic motherhood always lurking in the system to keep us safe.

4. Maple architecture: Maple has a small compiled kernel that is customized for each platform and a language interpreter which is used to program the vast majority of the 4,000 or so library routines. This meant that we were naturally multiplatform … in fact, in the bad old days (circ. 1990’s), we supported dozens of distinct platforms from hand-held devices to Cray supercomputers. Although the similarity stopped fairly quickly, I always make a point of comparing us to Java.

3. “Math Matters” poster: if you haven’t gotten yours yet, do it soon. I’ve had my finger in pretty much every Maplesoft poster ever done and this still stands as my favorite and the one that I worked the hardest on. It still is the only booth giveaway poster that people prefer over pens and other things that cost us real money.

2. The MapleSim concept: intuitive model schematics? automatic equation generation? symbolic methods that accelerate numerical solvers? multi-domain modeling? Each of these topics would have sucked up several dozen PhD theses five years ago and now they’re actually working dependably in an off the shelf product. I’m so thankful that I finished my PhD well before these tools became mainstream … the next generation of students will have to tackle much more difficult problems now that the tools are available.

1. Eigenvalues: The number one thing that impresses me the most is Maple’s ability to calculate eigenvalues … because I can’t. Amazingly enough, I still don’t get it. I can apply it, recite the etymology of the word, even reference it in polite cocktail party conversation but to this day, the subtleties of eigenvalues still escapes me. So the challenge is out. Build a Maple feature that will make me an eigengod … there’s a free hat and poster for you.