## Joan E. Walsh (1932–2017)

By Len Freeman and Nick Higham

Joan Eileen Walsh was born on 7 October 1932 and passed away on 30 December 2017 at the age of 85.

Joan obtained a First Class B.A. honours degree in Mathematics from the University of Oxford in 1954. She then spent three years working as an Assistant Mistress at Howell’s School in Denbigh, North Wales. In 1957 Joan left teaching and enrolled at the University of Cambridge to study for a Diploma in Numerical Analysis. This qualification was awarded, with Distinction, in 1958. At this point, Joan returned to the University of Oxford Computing Laboratory to study for a D.Phil. under the supervision of Professor Leslie Fox. She was Fox’s first doctoral student. Her D.Phil. was awarded in 1961.

Between October 1960 and March 1963, Joan worked as a Mathematical Programmer for the CEGB (Central Electricity Generating Board) Computing Department in London. In April 1963, she was appointed to a Lectureship in the Department of Mathematics at the University of Manchester. She progressed through the positions of Senior Lecturer (1966) and Reader (1971) before being appointed as Professor of Numerical Analysis at the University of Manchester in October 1974. For the academic year 1967-1968 Joan had leave of absence at the SRC Atlas Computer Laboratory—a joint appointment with St Hilda’s College, Oxford.

Joan led the Numerical Analysis group at the University of Manchester until 1985, after which Christopher Baker took over. This was a period of expansion both for the Numerical Analysis group at Manchester and, more generally, for numerical analysis in Britain. This expansion of British numerical analysis was supported by special grants from the SRC (Science Research Council) to provide additional funding for the subject at the Universities of Dundee, Manchester and Oxford, from 1973 until 1976. This funding supported one Senior Research Fellow and two Research Fellows at each Institution. Joan helped establish the Manchester group as one of the leading Numerical Analysis research centres in the United Kingdom (with eight permanent staff by 1987)—a position that is maintained to the present day.

Joan was Head of the Department of Mathematics between 1986 and 1989, and subsequently became Pro-Vice Chancellor of the University of Manchester in 1990. She held the latter role for four years, and was responsible for undergraduate affairs across the University. Joan’s tenure as Pro-Vice Chancellor coincided with substantial, and sometimes controversial, changes in undergraduate teaching—for example, the introduction of semesterisation and of credit-based degree programmes; Joan managed these major changes across the University with her customary tact, energy and determination. Joan was an efficient and effective administrator at a time when relatively few women occupied senior management roles in universities.

After 35 years’ service, Joan retired from the University in 1998 and was appointed Professor Emeritus.

In retirement, Joan returned to her studies; between 2000 and 2003 she studied for an MA in “Contemporary Theology in the Catholic Tradition” at Heythrop College of the University of London.

Over the years, and particularly during her tenure as Pro-Vice Chancellor, Joan sat on, and chaired, numerous University committees, far too many to list. She had a very long relationship with Allen Hall (a University Hall of Residence) where she was on the Hall Advisory Committee from 1975 until her retirement in 1998.

Joan served leadership roles nationally, as well as in the University. She was Vice President of the IMA (1992-1993) and a member of the Council of the IMA (1990-1991 and 1994-1995). She was elected Fellow of the Institute of Mathematics and its Applications (IMA) in 1984. She was a member of the Computer Board for Universities and Research Councils for several years from the late 1970s. She encouraged the creation of its Software Provision Committee, formally constituted in 1980 with Joan as its first Chairman, which she led until 1985. She was also President of the National Conference of University Professors (1993–1994). Further, she was a member of the Board of Governors at Withington Girls’ School, a leading independent school, for six years between 1993 and 1999.

Nowadays, all computational scientists take for granted the existence of software libraries such as the NAG Library. It is unimaginable to undertake major computational tasks without them. In 1970, Joan was one of a group of four academics who founded the Nottingham Algorithms Group with the aim of developing a comprehensive mathematical software library for use by the group of universities that were running ICL 1906A mainframe computers. Subsequently, the Nottingham Algorithms Group moved from the University of Nottingham to the University of Oxford and the project was incorporated as the Numerical Algorithms Group (NAG) Ltd. Joan became the Founding Chairman of NAG Ltd. in 1976, a position she held for the next ten years. She was subsequently a member of the Council of NAG Ltd. from 1992 until 1996. In recognition of her contribution to the NAG project Joan was elected as a Founding Member of the NAG Life Service Recognition Award in 2011.

Joan’s research interests focused on the numerical solution of ordinary differential equation boundary value problems and the numerical solution of partial differential equations. She conducted much of her research in collaboration with PhD students, supervising the following PhD students at the University of Manchester, who obtained their degrees in the years shown:

• Thomas Sag, 1966;
• Les Graney, 1973;
• David Sayers, 1973;
• Geoffrey McKeown, 1977;
• Roderick Cook, 1978;
• Patricia Hanson, 1979;
• Guy Lonsdale, 1985;
• Chan Basaruddin, 1990;
• Fathalla Rihan (supervised jointly with C. T. H. Baker), 2000.

Joan was an important figure in the development of Numerical Analysis and Scientific Computing at the University of Manchester and in the UK more generally. Her essay Numerical Analysis at the Victoria University of Manchester, 1957-1979 gives an interesting perspective on early developments at Manchester.

Brian Ford OBE, Founder Director of NAG, writes:

Joan had a brilliant career in Mathematics (particularly areas of Numerical Mathematics, Ordinary and Partial Differential Equations), Computing, University Education and Teaching, and was an excellent researcher, teacher, administrator, doctoral supervisor and colleague. But she was so much more than that!

Joan was invariably kind and thoughtful, intellectually gifted and generous with advice and guidance. Her profound Christian faith illuminated every aspect of her life. Joan’s deep reading and wide intellectual interests coupled with her prudence and clear thinking gave her profound knowledge and command. She was excellent company –amusing, modest, never belittling nor intimidating- and enjoyed fine wine and food in good company. She held firm beliefs, gently and persuasively seeking what she saw as the right way. Many people turned to her for help, advice and references and were grateful for her readily-offered help and support.

Joan was a private, even guarded, person. A devout Catholic, on her retirement she completed an MA in “Contemporary Theology in the Catholic Tradition” at Heythorp College, University of London. Fluent in Latin and reading regularly at services, she loved the traditional Tridentine Mass of the Church. Along with her local bishop in Salford and other like-minded Catholics, she therefore worked actively for the restitution of the Tridentine Mass to the liturgy of the world-wide Church (sidelined after Vatican II in favour of local languages), an involvement which culminated her joining high-level discussions at the Vatican. This bore fruit, the Tridentine Latin Mass being officially declared the extraordinary form of the Roman Rite of Mass a few years later: Joan was thrilled. Such was Joan’s commitment to things she believed in and her endless thought and work for others.

Joan was an excellent contributor to the NAG Library, believing strongly in collaboration and sharing, with high quality standards for all aspects of our work and thorough checking and testing. She was an excellent first Chairman of NAG and invaluable colleague and advisor. We thoroughly enjoyed working together, invariably in an excellent spirit. We achieved much.

Posted in people | 2 Comments

## Conference in Honour of Walter Gautschi

Last week I had the pleasure of attending and speaking at the Conference on Scientific Computing and Approximation (March 30-31, 2018) at Purdue University, held in honour of Walter Gautschi (Professor Emeritus of Computer Science and Mathematics at Purdue University) on the occasion of his 90th birthday.

The conference was expertly organized by Alex Pothen and Jie Shen. The attendees, numbering around 70, included many of Walter’s friends and colleagues.

The speakers made many references to Walter’s research contributions, particularly in the area of orthogonal polynomials. In my talk, Matrix Functions and their Sensitivity, I emphasized Walter’s work on conditioning of Vandermonde matrices.

A Vandermonde matrix $V_n$ is an $n\times n$ matrix depending on parameters $x_1,x_2,\ldots,x_n$ that has $j$ th column $[1, x_j, \ldots, x_j^{n-1}]^T$. It is nonsingular when the $x_i$ are distinct. This is a notoriously ill conditioned class of matrices. Walter said that he first experienced the ill conditioning when he computed Gaussian quadrature formulas from moments of a weight function.

Walter has written numerous papers on Vandermonde matrices that give much insight into their conditioning. Here is a very a brief selection of Walter’s results. For more, see my chapter Numerical Conditioning in Walter’s collected works.

In a 1962 paper he showed that

$\displaystyle\|V_n^{-1}\|_{\infty} \le \max_i \prod_{j\ne i}\frac{ 1+|x_j| }{ |x_i-x_j| }.$

In 1978 he obtained

$\displaystyle\|V_n^{-1}\|_{\infty} \ge \max_i \prod_{j\ne i} \frac{ \max(1,|x_j|) }{ |x_i-x_j| },$

which differs from the upper bound by at most a factor $2^{n-1}$. A 1975 result is that for $x_i$ equispaced on $[0,1]$,

$\displaystyle\kappa(V_n)_{\infty} \sim \frac{1}{\pi} e^{-\frac{\pi}{4}} (3.1)^n.$

A 1988 paper returns to lower bounds, showing that for $x_i \ge 0$ and $n\ge 2$,

$\displaystyle\kappa(V_n)_{\infty} > 2^{n-1}.$

When some of the $x_i$ coincide a confluent Vandermonde matrix can be defined, in which columns are “repeatedly differentiated”. Walter has obtained bounds for the confluent case, too.

These results quantify the extreme ill conditioning. I should note, though, that appropriate algorithms that exploit structure can nevertheless obtain accurate solutions to Vandermonde problems, as described in Chapter 22 of Accuracy and Stability of Numerical Algorithms.

Posted in conferences | Tagged | 1 Comment

## Palomino Blackwing Pencil Tribute to Ada Lovelace

Despite the deep penetration of digital tools into our lives, a lot of mathematics is still written by hand in pencil, and so it is appropriate that the Palomino Blackwing Volumes 16.2 pencil is a tribute to Ada Lovelace, the 19th century mathematician who worked on Charles Babbage’s proposed Analytical Engine.

The Palomino Blackwing, from California Cedar Products Company, is a modern version of the Blackwing pencil produced up until 1998 by the Eberhard Faber Pencil Company. The Blackwing was a favorite of luminaries such as John Steinbeck and Leonard Bernstein, and was much missed until CalCedar acquired the brand and started production of its own version of the pencil in 2011. Blackwing Volumes are limited editions “celebrating the people, places and events that have defined our creative culture”.

The 16.2 in the volume name refers to the Analytical Engine’s storage capacity of 16.2 kB (enough to hold one thousand 40 decimal digit numbers). The matt white finish and matt black ferrule are “inspired by the simple styling of early personal computers”. The rear of the pencil contains a pattern that represents in binary the initials AAL that Lovelace used to sign her work.

Blackwing pencils are available with four different graphite hardnesses, of which the 16.2 is the second firmest, roughly equivalent to a B, and the same as for the regular Blackwing 602. The following test compares the 16.2 with the Blackwing (no number, and the softest), the Dixon Ticonderoga HB, and the Staedtler Noris HB. The paper is Clairefontaine and the shaded area shows a smear test where I rubbed my thumb over the shaded rectangle.

The pencils come in packs of 12 and are available at, for example Bureau Direct (UK), pencils.com (USA), and JetPens (USA). If you’re in New York City, pop into Caroline Weaver’s wonderful CW Pencil Enterprise store.

One review has suggested that a harder graphite (as in certain other limited editions) would be better for writing mathematics. For me the 16.2 core is fine, but I also enjoy using the softer Blackwing cores. For a mathematician, as for any writer, having to pause to sharpen a pencil is not necessarily a bad thing, especially as the shavings give off a wonderful odor of the California incense cedar from which the barrels are made.

Posted in writing | Tagged | 3 Comments

## Photo Highlights of 2017

Here are some of my favourite photos taken at events that I attended in 2017.

## Atlanta (January)

This was the first time I have attended the Joint Mathematics Meetings, which were held in Atlanta, January 4-7, 2017. It was a huge conference with over 6000 attendees. A highlight for me was the launch of the third edition of MATLAB Guide on the SIAM booth, with the help of The MathWorks: Elizabeth Greenspan and Bruce Bailey looked after the SIAM stand: If you are interested in writing a book or SIAM, Elizabeth would love to hear from you!

The conference was held in the Marriott Marquis Hotel and the Hyatt Regency Hotel, both of which have impressive atriums. This photo is taken taken with a fish-eye lens, looking up into the Marriott Marquis Hotel’s atrium (For more photos, see Fuji Fisheye Photography: XT-2 and Samyang 8mm).

## Atlanta (March)

I was back in Atlanta for the SIAM Conference on Computational Science and Engineering, February 27-March 3, 2017. A highlight was a 70th birthday dinner celebration for Iain Duff, pictured here speaking at the Parallel Numerical Linear Algebra for Extreme Scale Systems minisymposium: Here is Sarah Knepper of Intel speaking in the Batched Linear Algebra on Multi/Many-Core Architectures symposium (a report on which is given in the blog post by Sam Relton) Torrential rain one night forced me to take shelter on the way back from dinner, allowing a moment to capture this image of Peach Tree Street.

## Washington (April)

The National Math Festival was held at the Walter E. Washington Convention Center in Washington DC on April 22, 2017: I caught the March for Science on the same day:

## Pittsburgh (July)

The SIAM Annual Meeting, held July 10-14, 2017 at the David Lawrence Convention Center in Pittsburgh, was very busy for me as SIAM president. Here is conference co-chair Des Higham speaking in the minisymposium “Advances in Mathematics of Large-Scale and Higher-Order Networks”: Emily Shuckburgh gave the I.E. Block Community Lecture “From Flatland to Our Land: A Mathematician’s Journey through Our Changing Planet”: The Princeton Companion to Applied Mathematics was on display on the Princeton University Press stand: Here are Des and I on the Roberto Clemente bridge over the Allegheny River, the evening before the conference started:

## Numerical Linear Algebra Group 2017

The Manchester Numerical Linear Algebra Group (many of whom are in the October 2017 photo below) was involved in a variety of activities this year. This post summarizes what we got up to. Publications are not included here, but many of them can be found on MIMS EPrints under the category Numerical Analysis.

## Software

Together with Jack Dongarra’s team at the University of Tennessee the group is one of the two partners involved in the development of PLASMA: Parallel Linear Algebra Software for Multicore Architectures.

PhD students Weijian Zhang, Steven Elsworth and Jonathan Deakin released Etymo—a search engine for machine learning research and development.

We continue to make our research codes available, which is increasingly done on GitHub; see the repositories of Fasi, Higham, Relton, Sego, Tisseur, Zhang. We also put MATLAB software on MATLAB Central File Exchange and on our own web sites, e.g., the Rational Krylov Toolbox (RKToolbox).

## PhD Students

New PhD students Gian Maria Negri Porzio and Thomas McSweeney joined the group in September 2017.

## Postdoctoral Research Associates (PDRAs)

Sam Relton, who was working on the Parallel Numerical Linear Algebra for Extreme Scale Systems (NLAFET) project, left in June 2017 to take up a position at the University of Leeds. Negin Bagherpour joined NLAFET in March 2017, leaving in November 2017.

Srikara Pranesh joined the project in November 2017. Pierre Blanchard joined us in October 2017 to work jointly on the ICONIC project (which started in June 2017) and NLAFET.

## Presentations at Conferences and Workshops

UK and Republic of Ireland Section of SIAM Annual Meeting, University of Strathclyde, January 12, 2017: Fasi, Gwynne, Higham, Zemaityte, Zhang.

2017 Joint Mathematics Meetings, Atlanta, January 4-7, 2017: Higham.

Workshop on Matrix Equations and Tensor Techniques, Pisa, Italy, February 13-14 2017: Fasi

Due Giorni di Algebra Lineare Numerica, Como, Italy, February 16-17, 2017: Fasi

International Conference on Domain Decomposition Methods DD24, Svalbard, Norway, February 6-10, 2017: Sistek.

Workshop on Batched, Reproducible, and Reduced Precision BLAS, Atlanta, February 23-25, 2017: Hammarling, Relton.

SIAM Conference on Computational Science and Engineering, Atlanta, February 27-March 3, 2017: Relton, Zounon. See the blog posts about the meeting by Nick Higham and Sam Relton.

High Performance Computing in Science and Engineering (HPCSE) 2017, Hotel Solan, Czech Republic, May 22-25, 2017: Sistek

Advances in Data Science, Manchester, May 15-16, 2017: Zhang.

27th Biennial Conference on Numerical Analysis, Glasgow, June 27-30, 2017: Tisseur.

Householder Symposium XX on Numerical Linear Algebra, The Inn at Virginia Tech, June 18-23, 2017: Tisseur.

SIAM Annual Meeting, Pittsburgh, July 10-14, 2017: Zhang (see this SIAM News article about Weijian’s presentation). A Storify of the conference is available in PDF form.

ILAS 2017 Conference, Iowa State University, USA, July 24-28, 2017: Güttel

24th IEEE Symposium on Computer Arithmetic, London, July 24-26, 2017: Higham (see this blog post by George Constantinides).

LMS-EPSRC Symposium on Model Order Reduction, Durham, UK, August 8-17, 2017: Güttel

Euro-Par 2017, 23rd International European Conference on Parallel and Distributed Computing, August 28-September 1, 2017: Zounon.

INdAM Meeting Structured Matrices in Numerical Linear Algebra: Analysis, Algorithms and Applications, Cortona, Italy, September 4-8, 2017: Fasi, Tisseur.

2017 Woudschoten Conferences, Zeist, The Netherlands, 4-6 October 2017: Tisseur.

ICERM Workshop on Recent Advances in Seismic Modeling and Inversion, Brown University, USA, November 6-10, 2017: Güttel. A video recording of this talk is available.

## Conference and Workshop Organization

Güttel co-organized the SIAM UKIE Annual Meeting 2017 at the University of Strathclyde January 12, 2017 and the GAMM ANLA Workshop on High-Performance Computing at the University of Cologne, September 7-8, 2017.

The Manchester SIAM Student Chapter organized an Manchester Chapter Auto Trader Industry Problem Solving Event on February 22, 2017 and the 7th Manchester SIAM Student Chapter Conference on May 5, 2017.

The group organized three minisymposia at the SIAM Conference on Computational Science and Engineering, Atlanta, February 27-March 3, 2017:

## Visitors

Franco Zivcovic (Università degli Studi di Trento) visited the group from September 2017-January 2018.

## Knowledge Transfer

The Sabisu KTP project, which ended in December 2016, has been awarded the highest grade of “Outstanding” by the KTP Grading Panel. A new KTP project with Process Integration Ltd. is under way, led by Stefan Güttel.

The MSc project of Thomas McSweeney was sponsored by NAG and produced a code for modified Cholesky factorization that will appear in the NAG Library.

## Recognition and Service

Stefan Güttel continued his terms as Secretary/Treasurer of the SIAM UKIE section and vice-chair of the GAMM Activity Group on Applied and Numerical Linear Algebra.

Nick Higham served the first year of his two-year term as President of SIAM.

Weijian Zhang was awarded a SIAM Student Travel Award to attend the SIAM Annual Meeting 2017 in Pittsburgh.

Massimiliano Fasi and Mante Zemaityte were selected to present posters at the SET for Britain 2017 competition, which took place at the House of Commons, London. Fasi’s poster was “Finding Communities in Large Signed Networks with the Weighted Geometric Mean of Laplacians” and Zemaityte’s was “A Shift-and-Invert Lanczos Algorithm for the Dynamic Analysis of Structures”.

Jakub Sistek served as treasurer of the eu-maths-in.cz Czech Network for Mathematics in Industry.

## The Strange Case of the Determinant of a Matrix of 1s and -1s

By Nick Higham and Alan Edelman (MIT)

In a 2005 talk the second author noted that the MATLAB det function returns an odd integer for a certain 27-by-27 matrix composed of $1$s and $-1$s:

>> A = edelman; % Set up the matrix.
>> format long g, format compact, det(A)
ans =
839466457497601


However, the determinant is, from its definition, a sum of an even number (27 factorial) of odd numbers, so is even. Indeed the correct determinant is 839466457497600.

At first sight, this example is rather troubling, since while MATLAB returns an integer, as expected, it is out by $1$. The determinant is computed as the product of the diagonal entries of the $U$ factor in the LU factorization with partial pivoting of $A$, and these entries are not all integers. Standard rounding error analysis shows that the relative error from forming that product is bounded by $nu/(1-nu)$, with $n=27$, where $u \approx 1.1 \times 10^{-16}$ is the unit roundoff, and this is comfortably larger than the actual relative error (which also includes the errors in computing $U$) of $6 \times 10^{-16}$. Therefore the computed determinant is well within the bounds of roundoff, and if the exact result had not been an integer the incorrect last decimal digit would hardly merit discussion.

However, this matrix has more up its sleeve. Let us compute the determinant using a different implementation of Gaussian elimination with partial pivoting, namely the function gep from the Matrix Computation Toolbox:

>> [Lp,Up,Pp] = gep(A,'p'); det(Pp)*det(Up)
ans =
839466457497600


Now we get the correct answer! To see what is happening, we can directly form the products of the diagonal elements of the $U$ factors:

>> [L,U,P] = lu(A);
>> d = diag(U); dp = diag(Up);
>> rel_diff_U_diags = norm((dp - d)./d,inf)
rel_diff_U_diags =
7.37206353875273e-16
>> [prod(d), prod(dp)]
ans =
-839466457497601          -839466457497600
>> [prod(d(end:-1:1)), prod(dp(end:-1:1))]
ans =
-839466457497600          -839466457497600


We see that even though the diagonals of the two $U$ factors differ by a small multiple of the unit roundoff, the computed products differ in the last decimal digit. If the product of the diagonal elements of $U$ is accumulated in the reverse order then the exact answer is obtained in both cases. Once again, while this behaviour might seem surprising, it is within the error bounds of a rounding error analysis.

The moral of this example is that we should not be misled by the integer nature of a result; in floating-point arithmetic it is relative error that should be judged.

Finally, we note that numerical evaluation of the determinant offers other types of interesting behaviour. Consider the Frank matrix: a matrix of integers that has determinant 1. What goes wrong here in the step from dimension 24 to 25?

>> A = gallery('frank',24); det(A)
ans =
0.999999999999996
>> A = gallery('frank',25); det(A)
ans =
143507521.082525


The Edelman matrix is available in the MATLAB function available in this gist, which is embedded below. A Julia notebook exploring the Edelman matrix is available here.

Posted in research | Tagged , | 2 Comments

## Fun Books for Learning Programming

I learned Fortran from the TV course and book by Jeff Rohl. Some years later I came across A FORTRAN Coloring Book by Roger Emanuel Kaufman (MIT Press, 1978). The text is entirely handwritten (even the copyright page), is illustrated with numerous cartoons, and is full of witty wordplay. Yet it imparts the basics of Fortran very well and I could have happily learned Fortran from it. It even describes some simple numerical methods, such as the bisection method. The book is one continuous text, with no chapters or sections, but it has a good index. I’ve long been a fan of the book and Des Higham, and I include three quotes from it in MATLAB Guide.

Kaufman’s book has attracted attention in cultural studies. In the article Bend Sinister: Monstrosity and Normative Effect in Computational Practice, Simon Yuill describes it as “the first published computing text to use cartoon and comic strip drawings as a pedagogic medium” and goes on to say “and it could be argued, is the archetype to the entire For Dummies series and all its numerous imitators”. I would add that the use of cartoons within magazine articles on computing was prevalent in the 1970s, notably in Creative Computing magazine, though I don’t recall anything comparable with Kaufman’s book.

A page from Illustrating C.

A book in a similar vein and from the same era, is the handwritten Illustrating Basic by Donald Alcock (Cambridge University Press, 1977). It’s a bit like Kaufman without the jokes, and is organized into sections. This was the first in a series of such books, culminating in Illustrating C (1992). Like Kaufman’s book, Alcock’s contain nontrivial examples and are a good way for anyone to learn a programming language.

Thinking Forth by Leo Brodie, about the Forth language, is typeset but contains lots of cartoons and hand-drawn figures. It was originally published in 1984 and is now freely available under a Creative Commons license.

A more recent book with a similarly fun treatment is Land of Lisp by Conrad Barski (No Starch Press, 2011). It teaches Common Lisp, coding various games along the way. It’s typeset but is heavily illustrated with cartoons and finishes with a comic strip.

## Org Mode Syntax Cheat Sheet

I’m a keen user of Emacs and Org mode for a variety of tasks, including

• note taking,
• generating documents for exporting to LaTeX, Word, or html.
• creating blog posts (notably for this blog, using Org2blog).

Although Org mode is usually associated with Emacs, it is a markup language in its own right, and one that is far more powerful and more standardized than the Markdown language.

I recently came across the excellent blog post Org-Mode Is One of the Most Reasonable Markup Language to Use for Text by Org enthusiast Karl Voit. In the post he includes a simple example displaying some of the most important aspects of Org syntax. I was struck by how much information can be conveyed in a short piece of Org code. I have adapted Karl’s example into this longer version:

#+TITLE: Org Mode Syntax Cheat Sheet
#+OPTIONS: toc:nil

# A comment line.  This line will not be exported.

Paragraphs are separated by at least one empty line.

*bold* /italic/ _underlined_ +strikethrough+ =monospaced=

https://nickhigham.wordpress.com/ A link without a description.

A DOI (digital object identifier) link:
[[doi:10.1093/comnet/cnv016][Matching Exponential-Based and Resolvent-Based Centrality Measures]]

A horizontal line, fill-width across the page:
-----

- First item in a list.
- Second item.
- Sub-item
1. Numbered item.
2. Another item.
- [ ] Item yet to be done.
- [X] Item that has been done.

LaTeX macros can be included: $x_2 = \alpha + \beta^2 - \gamma$.

**** TODO A todo item.
**** DONE A todo item that has been done.

#+BEGIN_QUOTE
This text will be indented on both the left margin and the right margin.
#+END_QUOTE

: Text to be displayed verbatim (as-is), without markup
: (*bold* does not change font), e.g., for source code.
: Line breaks are respected.

Some MATLAB source code:
#+BEGIN_SRC matlab
>> rand(1,3)
ans =
5.5856e-01   7.5663e-01   9.9548e-01
#+END_SRC

Some arbitrary text to be typeset verbatim in monospace font:
#+BEGIN_SRC text
Apples, oranges,
cucumbers, tomatoes
#+END_SRC

# calculated by hitting C-c C-c in Emacs on the #+TBLFM line.

|----------------+-----------+-----------+-------|
|----------------+-----------+-----------+-------|
| United States  |         7 |       497 |  71.0 |
| Unknown        |         4 |        83 |  20.8 |
| United Kingdom |         3 |        41 |  13.7 |
| Germany        |         3 |        29 |   9.7 |
| Netherlands    |         2 |        21 |  10.5 |
| Japan          |         1 |        18 |  18.0 |
|----------------+-----------+-----------+-------|
#+TBLFM: $4=$3/\$2;%.1f

Include an image:
file:nickhighamwordpress.jpg


I have put the source on GitHub along with the results of exporting the file to txt, LaTeX, PDF (direct link), and html. I include conversions done two ways:

• With Emacs: the recommended way.
• With Pandoc. This is useful if you do not use Emacs or want an easy way to automate the conversions. However, Pandoc does not support all Org syntax and has different defaults, so the conversions are not identical.

For more about Org see my previous writings and videos such as Using Emacs 2 – org and Getting Started With Org Mode.

## What’s New in MATLAB R2017b?

Following my earlier posts What’s New in MATLAB R2016b? and What’s New in MATLAB R2017a? I take a look here at the R2017b release of MATLAB. As before, this is not a comprehensive treatment (for which see the Release Notes), but rather a brief description of the changes in MATLAB (not the toolboxes) that are the most notable from my point of view.

## Decomposition Object

MATLAB now has a way of avoiding unnecessarily repeating the factorization of a matrix. In the past, if we wanted to solve two linear systems Ax = b and Ay = c involving the same square, general nonsingular matrix A, writing

x = A\b;
y = A\c;


was wasteful because A would be LU factorized twice. The way to avoid this unnecessary work was to factorize A explicitly then re-use the factors:

[L,U] = lu(A);
x = U\(L\b);
y = U\(L\c);


This solution lacks elegance. It is also less than ideal from the point of view of code maintenance and re-use. If we want to adapt the same code to handle symmetric positive definite A we have to change all three lines, even though the mathematical concept is the same:

R = chol(A);
x = R\(R'\b);
y = R\(R'\c);


The new decomposition function creates an object that contains the factorization of interest and allows it to be re-used. We can now write

dA = decomposition(A);
x = dA\b;
y = dA\c;


MATLAB automatically chooses the factorization based on the properties of A (as the backslash operator has always done). So for a general square matrix it LU factorizes A, while for a symmetric positive definite matrix it takes the Cholesky factorization. The decomposition object knows to use the factors within it when it encounters a backslash. So this example is functionally equivalent to the first two.

The type of decomposition can be specified as a second input argument, for example:

dA = decomposition(A,'lu');
dA = decomposition(A,'chol');
dA = decomposition(A,'ldl');
dA = decomposition(A,'qr');


These usages make the intention explicit and save a little computation, as MATLAB does not have to determine the matrix type. Currently, 'svd' is not supported as a second input augment.

This is a very welcome addition to the matrix factorization capabilities in MATLAB. Tim Davis proposed this idea in his 2013 article Algorithm 930: FACTORIZE: An Object-Oriented Linear System Solver for MATLAB. In Tim’s approach, all relevant functions such as orth, rank, condest are overloaded to use a decomposition object of the appropriate type. MATLAB currently uses the decomposition object only in backslash, forward slash, negation, and conjugate transpose.

The notation dA used in the MathWorks documentation for the factorization object associated with A grates a little with me, since dA (or $\Delta A$) is standard notation for a perturbation of the matrix A and of course the derivative of a function. In my usage I will probably write something more descriptive, such as decompA or factorsA.

## String Conversions

Functions written for versions of MATLAB prior to 2016b might assume that certain inputs are character vectors (the old way of storing strings) and might not work with the new string arrays introduced in MATLAB R2016b. The function convertStringsToChars can be used at the start of a function to convert strings to character vectors or cell arrays of character vectors:

function y = myfun(x,y,z)
[x,y,z] = convertStringToChars(x,y,z);


The pre-2016b function should then work as expected.

More functions now accept string arguments. For example, we can now write (with double quotes as opposed to the older single quote syntax for character vectors)

>> A = gallery("moler",3)
A =
1    -1    -1
-1     2     0
-1     0     3


## Tall Arrays

There is more support in R2017b for tall arrays (arrays that are too large to fit in memory). They can be indexed, with sorted indices in the first dimension; functions including median, plot, and polyfit now accept tall array arguments; and the random number generator used in tall array calculations can be independently controlled.

## Word Clouds

The new wordcloud function produces word clouds. The following code runs the function on a file of words from The Princeton Companion to Applied Mathematics.

words = fileread('pcam_words.txt');
words = string(words);
words = splitlines(words);
words(strlength(words)<5) = [];
words = categorical(words);
figure
wordcloud(words)


I had prepared this file in order to generate a word cloud using the Wordle website. Here is the MATLAB version followed by the Wordle version: Wordle removes certain stop words (common but unimportant words) that wordcloud leaves in. Also, whereas Wordle produces a different layout each time it is called, different layouts can be obtained from wordcloud with the syntax wordcloud(words,'LayoutNum',n) with n a nonnegative integer.

## Minima and Maxima of Discrete Data

New functions islocalmin and islocalmax find local minima and maxima within discrete data. Obviously targeted at data manipulation applications, such as analysis of time series, these functions offer a number of options to define what is meant by a minimum or maximum.

The code

A = randn(10); plot(A,'LineWidth',1.5); hold on
plot(islocalmax(A).*A,'r.','MarkerSize',25);
hold off, axis tight


finds maxima down the columns of a random matrix and produces this plot:

Whereas min and max find the smallest and largest elements of an array, the new functions mink(A,k) and maxk(A,k) find the k smallest and largest elements of A, columnwise if A is a matrix.

## Foundations of Applied Mathematics Book Series

Foundations of Applied Mathematics, Volume 1: Mathematical Analysis was published by SIAM this summer. Written by Jeffrey Humpherys, Tyler J. Jarvis, and Emily J. Evans, all from Brigham Young University, this is the first of a four-book series, aimed at the upper undergraduate or first year graduate level. It lays the analysis and linear algebra foundations that the authors feel are needed for modern applied mathematics.

The next book, Volume 2: Algorithms, Approximation, and Optimization, is scheduled for publication in 2018.

At 689 pages, hardback, beautifully typeset on high quality paper, and with four colours used for the diagrams and boxed text, this is a physically impressive book. Unusually for a SIAM book, it contains a dust jacket. I got a surprise when I took it off: underneath is a front cover very different to the dust jacket, containing a huge number 1. I like it and am leaving my book cover-free.

I always look to see what style decisions authors have made, as I ponder them a lot when writing my own books. One was immediately obvious: the preface uses five Oxford commas in the first six sentences!

I have only had time to dip into a few parts of the book, so this is not a review. But here are some interesting features.

• Chapter 15, Rings and Polynomials, covers a lot of ground under this general framework, including the Euclidean algorithm, the Lagrange and Newton forms of the polynomial interpolant, the Chinese remainder theorem (of which several applications are mentioned), and partial fractions. This chapter has attracted a lot of interest on Reddit.
• The authors say in the preface that one of their biggest innovations is treating spectral theory using the Dunford-Schwartz approach via resolvents.
• There is a wealth of support material available here. More material is available here.

It’s great to see such an ambitious project, especially with SIAM as the publisher. If you are teaching applied mathematics with a computational flavour you should check it out.