Title: **The Computer as Crucible**

Authors: **Jonathan Borwein and Keith Devlin**

True pp.: **154**

Publisher: **A K Peters Ltd**

Published on: **November 2008**

ISBN-13: **978-1568813431**

Rating: **8/10**

Jonathan Borwein and Keith Devlin are well-known mathematicians who have a strong appreciation of, and expertise in, experimental mathematics. In this book they provide us with a concise, inviting introduction to the field.

The first chapter tries to succinctly explain what experimental mathematics is and why it’s a fundamental tool for the modern mathematician. The following is their definition:

Experimental mathematics is the use of a computer to run computations—sometimes no more than trial-and-error tests—to look for patterns, to identify particular numbers and sequences, to gather evidence in support of specific mathematical assertions that may themselves arise by computational means, including search. Like contemporary chemists—and before them the alchemists of old—who mix various substances together in a crucible and heat them to a high temperature to see what happens, today’s experimental mathematician puts a hopefully potent mix of numbers, formulas, and algorithms into a computer in the hope that something of interest emerges.

They immediately address some of the possible objections and illustrate how an approach that doesn’t focus on formal proof, but rather on exploration and experimentation, ultimately leads to hypotheses which can then be, in many cases, proved analytically. The authors argue that in this sense, thanks to the aid of advanced computers, mathematics is becoming more and more similar to other natural sciences.

They also make a case for how great mathematicians like Euler, Gauss, and Reimann were doing experimental mathematics well before calculators where available. Their calculations on paper were far more limited than what computers afford us these days, yet they served them well when it came to sharpening and verifying their intuitions.

The rest of the book is a continuous series of examples that show the advantages of this approach in practice. The examples are highly interesting (some of them stunning) and tend to focus on calculus, analysis and analytical number theory.

Each chapter is accompanied by a section called “Explorations”. I found this section to be particularly valuable. Within it you’ll find exercises, and further examples and considerations. The answers/solutions to the actual problems are provided in the second to last chapter, just before the brief epilogue.

Chapter 2 discusses how to calculate an arbitrary digit for irrational numbers like [tex]\pi[/tex], in certain bases. They illustrate how the so called BBP Formula (Bailey-Borwein-Plouffe formula, co-discovered by Jonathan Borwein’s brother) came to be.

[tex]\displaystyle \pi = \sum_{k=0}^\infty\frac{1}{16^k}\left (\frac{4}{8k+1}-\frac{2}{8k+4}-\frac{1}{8k+5}-\frac{1}{8k+6}\right )[/tex]

The use of a program which implements the PSQL integer relation algorithm in high-precision, floating-point arithmetic was key to its discovery. The BBP Formula in turn allowed the calculation of the quadrillionth binary digit of [tex]\pi[/tex] back in 2000.

Chapter 3 focuses on identifying numbers, digits patterns, and sequences once you obtain a numeric result through your calculations and experimentation. They introduce the subject with relatively obvious values like the approximations of [tex]e-2[/tex] or [tex]\pi +e /2[/tex], but the chapter quickly escalates to an example where a closed form for a seemingly random sequence needs to be found.

Chapter 4 analyzes the Reimann Zeta function from the eyes of an experimental mathematician, and shows us what kind of insight we can gain from this unique perspective.

In chapter 5 we learn how by numerically evaluating definite integrals, it is sometimes possible to identify the resulting value which will help us to analytically resolve those particular integrals. The examples presented in this chapter originate for the most part from physics and are very challenging if attempted without the aid of experimental methods. To better grasp the kind of integrals discussed in this chapter, here is an example:

[tex]\displaystyle C = \int_{0}^{\infty} \int_{y}^{\infty}\frac{(x-2)^2\log{((x+y)/(x-y))}}{x y sinh(x+y)} {\mathrm{d} x}{\mathrm{d} y}[/tex]

The explorations section provides a few more interesting integrals, including some for which a closed form is not known. The authors even include an integral that intentionally stumps Mathematica 6 and Maple 11.

Chapter 6 is dedicated to serendipitous discoveries (“proof by serendipity”) with a few interesting examples of how “luck” met preparation, ultimately enriching the body of mathematical knowledge almost by chance.

In chapter 7 the authors go back to talk about [tex]\pi[/tex], this time in base 10, to calculate its digits with efficient, fast converging formulas and methods. The chapter wraps up with a discussion about the normality of [tex]\pi[/tex], which hasn’t been proved of course, but appears to be empirically supported by the statistical analysis of the first trillion digits. In the explorations section there is a nice discussion about the implementation of fast arithmetic through the Karatsuba multiplication, and the subject of Montecarlo simulations (a very inefficient method of calculating [tex]\pi[/tex], but a great way to show the idea behind Montecarlo simulations).

Chapter 8 has a bold title, “The computer knows more math than you do”. This provocative title is quickly diminished to put it in context though. The authors start by approaching a tough problem posed by Donald Knuth (of TeX and The Art of Computer Programming fame) to the readers of the American Mathematical Monthly:

[tex]\displaystyle S = \sum_{k=1}^{\infty} \left ( \frac{k^k}{k!e^k}-\frac{1}{\sqrt{2\pi k}} \right )[/tex]

In an attempt to solve this the authors invite us to go on a journey involving the Lambert W function, the Pochhammer function, and Abel’s limit theorem. The rest of the chapter illustrates another difficult problem whose solution obtained through the aid of Maple has important implications not only for mathematics, but also for quantum field theory and statistical mechanics.

In chapter 9 a few infinite series are calculated in order to show how CAS systems and experimental methodology can still be useful when dealing with problems that involve infinite sequences, series, and products.

Chapter 10 is dedicated to the limits and the dangers of this approach. Several examples showcase how one can be misled into making assumptions, and how to avoid this from happening. The ad hoc example below is correct to over half a billion digits:

[tex]\displaystyle \sum_{n=1}^{\infty} \frac{\left \lfloor ne^{\pi\sqrt{163}/3} \right \rfloor}{2^n} = 1280640[/tex]

After having calculated a few hundred digits, it would be natural to assume that the series converges to a natural number, when in reality it’s an irrational and transcendental number.

In chapter 11, conscious of the selective focus on analysis and analytical number theory throughout the book, Borwein and Devlin introduce other examples such as a topology problem whose proof was reached thanks to a deeper insight gained through computer visualization of a surface, a knot theory problem, the Four Color Theorem, the Robbins Conjecture, the computation of [tex]E_{8}[/tex], and so on.

In truth, I feel that such a thin book could have used more examples like the ones in chapter 11, in order to make a stronger case for the applicability of experimental mathematics to areas outside of analysis.

The book is well written and the tone is never heavy, despite the advanced mathematical examples within it. The authors include historical background and anecdotes which makes for a more interesting read and provides a human perspective behind the formulas presented. The (at times) funny illustrations and occasional jokes are definitely a pleasant addition.

This book is relatively tool agnostic; Maple and Mathematica are referenced throughout, and so are a few online tools to identify number sequences and known numeric values. Overall though, the emphasis in on the methodology rather than a particular CAS (Computer Algebra System) or programming language. In fact, with the exception of a snippet of Maple code in one of the explorations in the first chapter, the book describe the examples from a mathematical and algorithmic standpoint. You won’t find source code for the examples illustrated.

The ideal target audience for The Computer as Crucible is graduate students and researchers. A bright, motivated high-school student will get the gist of this book, but a more mature mathematical audience will actually be able to follow the steps within the examples and fully appreciate the insight on how an experimental approach can aid their research.

Despite the numerous examples employed to make their case, the authors start the book by explaining that it is not intended to be comprehensive. It’s meant to be thought provoking and to whet your appetite as to what is now possible in mathematical research thanks to computers.

As a computer programmer who’s passionate about mathematics, experimental mathematics fascinates me greatly. As such, I hope to work my way through the actual textbooks that are generally suggested as a follow up to this book. Namely, I’ve already started reading Mathematics by Experiment: Plausible Reasoning in the 21st Century (Second Edition), which is co-authored by Jonathan Borwein himself. Other textbooks referenced in this introduction are Experimental Mathematics in Action and Experimentation in Mathematics: Computational Paths to Discovery.

In conclusion, The Computer as Crucible is a lovely little book which builds a strong case for experimental mathematics. Any practicing mathematician or serious amateur should consider checking out this introduction to a topic that will no doubt transform mathematics.

*Full disclosure: We received this book for free from the publisher, but we’re under no obligation to review or endorse it. We routinely receive a fair number of books from several publishers that never make the cut for an actual review. The links have our Amazon referral id which gives us a tiny percentage if you buy a book. In turn this helps support this site.*

This reminded me of the guy who defined the Omega number (https://plus.maths.org/issue37/features/omega/index.html). He also proposes to take an experimental approach to mathematics, in some cases that means to take as axioms some propositions that haven’t been proved just for the sake of finding out what are the possible implications.