## What’s New in MATLAB R2017a?

MATLAB R2017a was released last week. Many of the changes reported in the release notes are evolutionary, building on and extending major new features introduced previously. For example, the Live Editor continues to gain expanded capabilities. In this post I pick out a few new features that caught my eye. This is very much a personal selection. For full details of what’s new, see the release notes, and see also my previous post What’s New in MATLAB R2016b if you are not familiar with R2016b.

## Parula

The parula color map has been modified slightly. The difference is subtle, but as the following example illustrates, the R2017a parula is a bit more vibrant and has a bit less cyan and yellow in the blues and greens.

I note, however, that the difference between the old and the new parula is smaller when the images are converted to the CMYK color space, as they must be for printing.

## Heatmap

The new heatmap function plots a heatmap of tabular data. Although it is intended mainly for use with the table data type, I think heatmap will be useful for getting insight into the structure of matrices, as illustrated by the following examples.

## String Arrays

String arrays, introduced in MATLAB R2016b, can now be formed using double quotes:

>> s = string('This is a string') % R2016b
s =
"This is a string"
>> t = "This is a string"         % R2017a
t =
"This is a string"
>> isequal(s,t)
ans =
logical
1
>> whos
Name      Size            Bytes  Class     Attributes

s         1x1               166  string
t         1x1               166  string


However, there is one major caveat. Many MATLAB functions that take a char as input argument have not yet been adapted to accept strings. Hence

>> A = gallery('moler',3)
A =
1    -1    -1
-1     2     0
-1     0     3
>> A = gallery("moler",3)
Error using nargin
Argument must be either a character vector or a function handle.
Error in gallery (line 191)
nargs = nargin(matname);


I expect that such functions will be updated in future releases.

## Missing

A new function missing creates missing values appropriate to the data type in question.

>> A = ones(2); A(2,2) = missing
A =
1     1
1   NaN

> d = datetime('2014-05-26')
d =
datetime
26-May-2014
>> d(2) = missing
d =
1×2 datetime array
26-May-2014   NaT


Until converted to the target datatype, a missing value has class missing:

>> m = missing
m =
missing

>> class(m)
ans =
'missing'


## Performance Improvements

The release notes report performance improvements under a variety of headings, including execution engine, scripts, and mathematics functions. These are very welcome, as the user automatically benefits from them. One comment that caught my eye is “The backslash command A\B is faster when operating on negative definite matrices”. I think this means that MATLAB checks whether the matrix, A, is symmetric with all negative diagonal elements and, if it is, attempts a Cholesky factorization of -A.

## Tracing the Early History of MATLAB Through SIAM News

A recent blog post by Ned Gulley points out that the new mathematics gallery (“Mathematics: The Winton Gallery”) at the Science Museum, London, contains a copy of the disk and manual for MATLAB 1.3, from 1985, sitting next to a trial assembly of Charles Babbage’s analytical engine.

This set me thinking about the early history of MATLAB and The MathWorks. Items such as manuals and disks must be quite rare nowadays. What other traces are there of early MATLAB history? I have recently been looking through some back issues of SIAM News and spotted a number of adverts for MATLAB over the period 1985–1991. Let’s see what historical insight these early adverts give.

## 1985

The first advert I can find is from the March 1985 SIAM News, and it is for PC-MATLAB, priced at \$695, running on an IBM-PC or compatible computer. The advert features the now famous MathWorks logo, which represents an eigenfunction of the L-shaped membrane. It quotes a time of 10.1 seconds for a 50-by-50 real matrix multiplication on a machine with an Intel 8087 coprocessor. This floating-point coprocessor was a useful add-on to the IBM-PC, which used the Intel 8086 chip.

MATLAB benefited from being launched at a time when the IBM PC with 8087 coprocessor was just starting to become popular. The 8086-8087 combination made it possible to carry out computations on a desktop PC that had previously required a minicomputer—and they could now be done with the interactive MATLAB interface.

The advert mentions “mainframe MATLAB”, which it says is written in Fortran, runs on “larger computers”, and is in use in several hundred organizations worldwide.

PC-MATLAB had been rewritten in C, and it supported graphics, IEEE arithmetic (as implemented in the 8087), and “user-defined functions” (M-files).

Note that at this time MathWorks was located at 124 Foxwood Road, Portola Valley, California. In his article The Growth of MATLAB and The MathWorks Over Two Decades, Cleve Moler explains that this first mailing address was “a rented A-frame cabin where Jack [Little] lived in the hills above Stanford University in Portola Valley, California”.

The September 1985 issue of SIAM News contains a rather different advert that now refers to “the original mainframe version of MATLAB” and emphasizes the ease of use of MATLAB.

## 1987

The next advert is from the January 1987 SIAM News. MATLAB is now also available as Pro-MATLAB running on a Sun workstation or a VAX computer. M-files are now mentioned by that name and LINPACK benchmark figures are stated, the largest figure being 98 Kflops on the MicroVAX II.

The MathWorks has now moved to Massachusetts.

## 1990

The advert in the May 1990 SIAM News gives a version number, MATLAB 3.5, and it announces the Signal Processing Toolbox. It boasts that MATLAB has over 400 built-in functions and supports an enlarged range of computers, which include Cray supercomputers. The benchmark for a 50-by-50 real matrix multiplication is now down to 0.71 seconds on a 20 Mhz 386-based PC: a reduction by a factor of 14 from the figure quoted in 1985.

A strange feature of the advert is that the toolbox is only explicitly mentioned in the box at top left and the meaning of “toolbox” is not stated.

The MathWorks address has changed again, to 21 Eliot St., South Natick, Massachusetts. In the article mentioned above, Cleve explains, “When the company reached about a dozen employees, we moved several miles east to take over the second floor of a lovely building in South Natick, Massachusetts”.

## 1991

The advert in the May 1991 issue of SIAM News focuses on two new toolboxes: the Spline Toolbox and the Optimization Toolbox. The gray box defines toolboxes as “sets of routines written in the MATLAB programming language for specialized applications”.

MathWorks is now located at Cochituate Place on Prime Park Way in Natick. The street was so-named because it had been the home of the Prime Computer Corporation, which produced minicomputers from 1972 to 1992.

These adverts give some insight into the development of MATLAB in its early years. They also show how the rapid growth of MathWorks necessitated frequent relocation of the company. Indeed, in the article mentioned above Cleve Moler notes that the number of employees roughly doubled every year for the first seven years.

## Parallel Numerical Linear Algebra for Extreme Scale Systems

A minisymposium Parallel Numerical Linear Algebra for Extreme Scale Systems was held at the SIAM Conference on Computational Science and Engineering, Atlanta on February 28, 2017.

Today’s most powerful supercomputers are composed of hundreds of thousands of computing cores (CPUs and accelerators) connected in high speed networks that make up a massively parallel high performance computing (HPC) system. These systems are placing new demands on effective scalable numerical algorithms and software libraries, which will only increase in the future as we move towards increasingly heterogeneous systems with millions of compute cores. This minisymposium, which I organized jointly with Bo Kågström (Umeå University, Sweden), focused on addressing these challenges in the context of linear algebra problems through developing novel parallel algorithms, exploring advanced scheduling strategies and runtime systems, carrying out offline and online autotuning, and avoiding communication and synchronization bottlenecks.

The speakers were all members of the NLAFET (Parallel Numerical Linear Algebra for Future Extreme-Scale Systems) project, which is one of the high-profile extreme-scale computing research projects funded by the European Commission within the Future and Emerging Technologies (FET) program under Horizon 2020. Much of the work described in the minisymposium was carried out within NLAFET.

Around 75 people attended and there was standing room only. Here are the talks, with links to the slides. The names of the speakers are italicized.

Related to this minisymposium was the two-day Workshop on Batched, Reproducible, and Reduced Precision BLAS, held a couple of days beforehand at Georgia Tech. The workshop included presentations from both academia and industry and the program contains links to the speakers’ slides.

## Writing Mathematics in Pencil, and Why Analogue is Not Dead

It’s an old joke that mathematicians need just a pencil, paper, and a bin, while philosophers are even more frugal because they don’t need the bin. Yet nowadays more and more of the time of mathematicians, indeed all scientists, is spent at the computer. Whereas twenty years ago I would handwrite a draft of a paper before typing it in, I now do almost all the drafting directly in $\LaTeX$ at the keyboard.

But in response to computers dominating our lives, and in a move away from the (mythical?) paperless office, people are increasingly reverting to analogue tools, encouraged by the pleasure of handling stationery and, for those of us who were brought up in an analogue world, nostalgia.

This is a good time to employ retro tools in a digital world because we can now buy online an increasingly wide variety of stationery from all around the world.

What might you gain by writing mathematics with a pencil and paper as opposed to typing it at the computer? Sitting at a desk with a pencil in hand you are free from the distractions of the windows on your computer screen. The analogue process, with its delays of turning a page, sharpening the pencil, and rubbing out mistakes, has the benefit of slowing you down and thereby promoting your flow of thought and creativity. And the touch of the paper and the smell of cedar as you sharpen the pencil refresh the senses.

Donald Knuth has another reason for writing with a pencil, as explained in this 2008 interview.

I love keyboards, but my manuscripts are always handwritten. The reason is that I type faster than I think. There’s a synchronization problem. I can think of ideas at about the rate I can write them down with a pencil. But with typing I’m going faster, so I have to sync, and my thoughts have to start up and stop again in a way that involves more of my brain.

Yet more reasons for using pencils are given in the video Why Use Pencils? by T. J. Cosgrove. One of T. J.’s points is that the graphite produced by a pencil does not fade, unlike inks.

It is also worth noting that in some recently published research psychologists found evidence that students who take notes with pencil (or pen) and paper outperform those who take notes on a laptop.

So there are some good reasons for writing with a pencil. How should you choose one from among the many different types available?

I don’t know of a good source of advice on pencils for mathematicians (maybe I will write something in due course), but this blog post on pencils for musicians is largely applicable if you replace “music” by “mathematics”. The post is by Caitlin Elgin, from the wonderful Manhattan pencil shop pictured in the photo above, which I took when I visited it last year.

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## PCAM Authors Speaking About Their Work at SAMSI

The Statistical and Applied Mathematical Sciences Institute (SAMSI) has just run a Workshop on the Interface of Statistics and Optimization. Among the speakers were four authors of articles in The Princeton Companion to Applied Mathematics (PCAM).

In an earlier post I provided links to videos of PCAM authors giving talks related to the topics of their PCAM articles. To add to those, here are the four PCAM author talks from the SAMSI workshop.

I have also included the talk by Margaret Wright, because it provides insight into a number of important topics covered in PCAM in a very lucid way.

• John Burns, Parameter Identification for Dynamical Systems with Structured Uncertainty (author of PCAM article Optimal Sensor Location in the Control of Energy-Efficient Buildings)

• Jack Dongarra, The Road to Exascale and Legacy Software for Dense Linear Algebra (author of PCAM article High-Performance Computing)

• Yonina Eldar, Phase Retrieval and Analog to Digital Compression (author of PCAM article Compressed Sensing)

• Stephen Wright, Randomness in Coordinate Descent (author of PCAM article Continuous Optimization (Nonlinear and Linear Programming)

• Margaret Wright, Old, New, Borrowed, and Blue in the Marriage of Statistics and Optimization

## Preparing CMYK Figures for Book Printing

All printing is done in CMYK, the color space based on the four colors cyan, magenta, yellow, and black. Figures that we generate in MATLAB and other systems are invariably saved in RGB format (red, green, blue). When we send a paper for publication we submit RGB figures and they are transformed to CMYK somewhere in the production process, usually without us realizing that it has been done.

If you are one of those people who writes books and prefers to generate the final PDF yourself then you will need to convert any color figures to CMYK. The generation and use of CMYK files is something of a dark art. Here I report what I found out about it when producing the third edition (2017) of MATLAB Guide, co-authored with Des Higham. This is the first edition of the book to use color.

CMYK produces a different range of colors than RGB. Since it is a subtractive color space, designed for inks, it cannot produce some of the brilliant colors that RGB can, especially in the blues. In other words, some RGB colors are out of gamut in CMYK. (Less importantly, the converse is true: some CMYK colors cannot be produced in RGB.) This is something we are used to, but usually do not notice. Whenever we print a document on a laser printer we view a CMYK representation of the colors. In many cases, a figure will look very similar in print and on screen, but there are plenty of exceptions.

The following image shows an RGB image on the left, the result of converting that image to CMYK and then back to RGB in the middle, and a scan of a laser printer’s reproduction of the RGB image on the right. The differences between the RGB version and the other two versions may be shocking! Fortunately, when the CMYK version is viewed on a printed page in isolation from the RGB version (necessarily displayed on a monitor) it does not look so bad. [Of course, if you are reading a printed version of this post then the first two images will look essentially the same.]

After some experimentation, I settled on the following procedure, which I describe for a PDF workflow. For a PostScript workflow, PDF files need to be replaced by EPS files.

• Print the whole document from an RGB file.
• Find figures that look very different in print than on screen and select from those any where the difference is not acceptable. In most cases an unacceptable difference will be one where contrast between colors, or saturation of colors, has been reduced.
• Edit the selected figures in Photoshop, or some other image manipulation package that can save in CMYK form, in order to produce a better looking CMYK file. In Photoshop, Image-Mode-CMYK Color converts an image to CMYK form, but before using this command you should set the correct CMYK working space under Edit-Color Settings (see the Color Space section below). You can edit the RGB image (making use of View-Proof Color to see how the image will look in CMYK, and View-Gamut Warning to see colors that will be out of gamut in CMYK) and then convert to CMYK, or you can convert to CMYK and then edit the file. Save the CMYK image as a PDF file.
• Generate the PDF file of the book using the edited CMYK figures and the RGB forms of all the other figures, that is, those that did not need special treatment.
• Load the PDF file into Adobe Acrobat Pro and issue the command
Tools - Print Production - Convert Colors


This converts all objects in the file to CMYK.

Of the well over 100 figures in MATLAB Guide, only two needed special treatment.

However, there was one problem with this procedure. A handful of images are screen dumps that show a MATLAB window, and these are necessarily low resolution, at 72dpi. When converted to CMYK in Adobe Acrobat these images degraded badly. The solution was to resample the images to 150dpi in Photoshop via Image-Image Size and save to PDF; conversion in Acrobat then worked fine. (Such resampling is probably good practice anyway.)

This is not quite the full story. Here are some more gory details, which are worth reading if you are actually producing a book.

## Color Spaces

There is no unique CMYK or RGB color space: these spaces are device dependent. RGB spaces can have different white points and CMYK spaces are designed for particular combinations of paper and ink. Adobe Acrobat Pro offers “U.S. Web Coated (SWOP) v2”, “Uncoated FOGRA29 (ISO 12647-2:2004)” and many other inscrutably named CMYK profiles. So “convert to CMYK” is an ill-defined instruction unless the precise profile is specified. The conversion settings I used are shown in this screen dump of the dialog box, and were provided by the company who printed the book. Your settings might need to be different. There are a couple of things to note. First, Adobe Acrobat does not remember the settings in the dialog box, so they need to be re-entered every time. Second, since “Embed profile” is not selected with the settings shown, there is no way to tell which profile has been used once the conversion has been done.

Does it really matter what CMYK profile you use? Yes, if you want the best possible results. Consider the following figure. On the left is the result of converting the original RGB image to “U.S. Web Coated (SWOP) v2” and on the right is the result of converting to “Photoshop 5 default CMYK” (and converting back to RGB in both cases, the latter conversion producing no visual difference in Photoshop). There is a significant difference between the two conversions in every color except white! In principle, both conversions will produce the same result when printed with the target ink and paper, but if you choose the profile without knowing the target you have little idea what the printed result will be.

## Photography Books

An important assumption in the process described above is that the accuracy of the color reproduction is not vital. For photography books this assumption clearly does not hold and conversion to CMYK enters a new and frightening realm where images might need to be individually fine-tuned. Indeed high quality photography books typically undergo one or more proof stages using actual galley proofs from the ultimate printing device, sometimes with the author present at the printing press.

## Generating CMYK Files in MATLAB

Most of the figures in MATLAB Guide are (naturally) produced in MATLAB. Figures saved with the print function are by default in RGB mode. CMYK is supported for EPS and PS files only, using the ‘-cmyk’ option. This is not very useful if you are using a PDF workflow, as I do.

Instead of using the print function I experimented with export_fig, which is available on MathWorks File Exchange and can save in CMYK format to PDF, EPS, or TIFF files. export_fig creates a PDF file via an EPS file using Ghostscript.

This sounds straightforward, but I found that many of the PDF files generated by export_fig were not fully in CMYK Mode. A PDF file can contain objects in different color spaces and I had files where a color plot was in CMYK but the colorbar was in RGB! The question arises of how one can check whether a given PDF file has any RGB objects. Unfortunately, there seems to be no simple way to do so in Adobe Acrobat Pro. What one can do is invoke

Tools - Print Production - Preflight - PDF analysis -
List objects using ICCbased CMYK - Analyze


then open up

Overview - color spaces


(I am using Adobe Acrobat Pro XI, 11.0.18; usage could differ in other versions). This should contain “DeviceCMYK color space”, but if it contains “DeviceRGB color space” or “DeviceGray color space” then RGB or Grayscale objects are present. If the latter terms appear in a multi-page PDF file, double clicking on the corresponding icon should take you to the last such object.

You can also open up

Overview - Images


to get a list of pages and object types on those pages. Double clicking on a page icon takes you to that page.

## Good Times in MATLAB: How to Typeset the Multiplication Symbol

The MATLAB output

>> A = rand(2); whos
Name      Size            Bytes  Class     Attributes

A         2x2                32  double


will be familiar to seasoned users. Consider this, however, from MATLAB R2016b:

>> s = string({'One','Two'})

s =
1×2 string array
"One"    "Two"


At first sight, you might not spot anything unusual, other than the new string datatype. But there are two differences. First, MATLAB prints a header giving the type and size of the array. It does so for arrays of type other than double precision and char. Second, the times symbol is no longer an “x” but is now a multiplication symbol: “×”.

The new “times” certainly looks better. There are still remnants of “x”, for example in whos s for the example above, but I presume that all occurrences of “x” will be changed to the new symbol in the next release

However, there is a catch: the “×” symbol is a Unicode character, so it will not print correctly when you include the output in LaTeX (at least with the version provided in TeX Live 2016). Moreover, it may not even save correctly if your editor is not set up for Unicode characters.

Here is how we dealt with the problem in the third edition (published in January 2017) of MATLAB Guide. We put the code

\usepackage[utf8x]{inputenc}
\DeclareUnicodeCharacter{0215}{\ensuremath{\times}}


in the preamble of the master TeX file, do.tex. We also told our editor, Emacs, to use a UTF-8 coding, by putting the following code at the end of each included .tex file (we have one file per chapter):

%%% Local Variables:
%%% coding: utf-8
%%% mode: latex
%%% TeX-master: "do"
%%% End:


With this setup we can cut and paste output including “×” into our .tex files and it appears as expected in the LaTeX output.

## Hyphenation of Compound Words

Compound words are common in mathematical writing and it can be hard to remember how to hyphenate them. Unfortunately, there are no hard and fast rules. In this article I give some guidance and illustrative examples. The principle to keep in mind is that hyphenation should help to avoid ambiguity.

In phrases of the form “adjective noun noun” or “noun adjective/participle noun” a hyphen is usually used: closed-form solution, nineteenth-century mathematics, error-correcting code. But if the adjective follows the noun then no hyphen is needed: solution in closed form, mathematics of the nineteenth century, code that is error correcting. Here are some other examples:

• nearest-neighbor interpolation,
• higher-dimensional discrete Fourier transforms,
• large-scale optimization problem,
• minimum-norm solution but solution of minimum norm,
• first-order differential equation but differential equation of first order,
• the parameter-dependent ODE but the ODE is parameter dependent,
• rank-1 matrix but the matrix has rank 1.

In examples such as finite-difference method and finite-element method it is a matter of convention and taste whether to hyphenate. Some authors do and some don’t. Most authors do not hyphenate singular value decomposition.

Compounds beginning with adverbs ending in ly are not hyphenated, since they are usually unambiguous. Examples: slowly converging sequence, highly oscillatory integrand, continuously differentiable function, numerically oriented examples.

An important special case is compounds beginning with ill, well, little, much, and best, the first two of which are particularly common in mathematical writing. Here, a hyphen is used for a compound of two words used adjectivally, but if the compound itself is modified then no hyphen is used. Examples (these also apply with ill replaced by well):

• This is an ill-conditioned problem.
• This is a very ill conditioned problem.
• The problem is ill conditioned.
• This problem is very ill conditioned.

If the first example were to be written as This is an ill conditioned problem then it could be read as if ill were an adjective modifying the compound conditioned problem. Confusion is unlikely in this instance, but in ill-prepared contestant the hyphen is needed unless we are talking about a contestant who is prepared but not well.

Here are two further examples that are complete sentences.

• MATLAB allows a two-dimensional array to be subscripted as though it were one dimensional.
• This approach is particularly well-suited to high-precision computation.

The hyphen in well-suited in the last example is not essential, but is rather a matter of taste.

I know from personal experience that it is hard to achieve good, consistent hyphenation when you are concentrating on all the other aspects of writing. This is where having the services of a copy editor is extremely valuable. To benefit, you need to publish with a journal or book publisher that takes copy editing seriously (SIAM, PUP, CUP, OUP, …).

I give the final word to an Oxford University Press style manual, as quoted in the Economist Style Guide:

If you take hyphens seriously, you will surely go mad.

I am indebted to Sam Clark of T&T Productions for checking this post (and for saving me from many hyphenation blunders in my last two books).

## MATLAB Guide, Third Edition (2017)

The third edition of MATLAB Guide, which I co-wrote with Des Higham, has just been published by SIAM. It is a major update of the second edition (2005) to reflect the many changes in MATLAB over the last twelve years, and is 25 percent longer. There are new sections and chapters, and almost every page has changed.

The new chapters are

• Object-Oriented Programming: presents an introduction to object-oriented programming in MATLAB through two examples of classes.
• Graphs: describes the new MATLAB classes graph and digraph for representing and manipulating undirected graphs and directed graphs.
• Large Data Sets: describes MATLAB features for handling data sets so large that they do not fit into the memory of the computer.
• The Parallel Computing Toolbox: describes this widely used and increasingly important toolbox.

The chapter The Symbolic Math Toolbox has been revised to reflect the change of the underlying symbolic engine from Maple (at the time of the second edition) to MuPAD.

New sections include Empty Matrices, Matrix Properties, Argument Checking and Parsing, Fine Tuning the Display of Arrays, Live Editor, Unit Tests, String Arrays, Categorical Arrays, Tables and Timetables, and Timing Code.

Two other big changes are that figures are now printed in color and there are thirteen “Asides”, highlighted in gray boxes, which contain discussions of MATLAB-related topics, such as anonymous functions, reproducibility, and color maps.

The book was launched with a reception hosted by The Mathworks and SIAM at the SIAM booth at the Joint Mathematics Meetings in Atlanta on January 6, 2017. Jim Rundquist (Senior Education Technical Evangelist) represented MathWorks, and several SIAM staff, including SIAM Publisher David Marshall, were present.

Two delicious cakes, one containing a representation of the cover of the book, were enjoyed by reception attendees. Inspired by MATLAB, the cakes were served using slice, deal, and input, and an occasional reshape or rotate, with a pool of workers consuming them asynchronously.

## Taking Up the SIAM Presidency

I am honored to be taking over the reins from Pam Cook as president of the Society for Industrial and Applied Mathematics (SIAM) for the next two years, starting January 1, 2017. Pam remains as past-president during 2017. I look forward to helping to address the challenges facing SIAM and to working with the excellent SIAM officers and staff.

Eighteen months ago I wrote a “candidate statement” for the fall 2015 SIAM elections. The comments I made then remain valid and so I thought it would be worth reproducing the statement here.

The January/February 2017 issue of SIAM News will contain my first From the SIAM President column, in which I give further thoughts on SIAM’s future.

I am happy to receive comments from SIAM members or potential members, either in the box below or by email.

Candidate Statement: SIAM is the leading international organization for applied mathematics and has been an important part of my professional life since I joined as a PhD student, 31 years ago. SIAM is the first place that many people turn to for publications, conferences, and news about applied mathematics and it represents the profession nationally and internationally.

I have been fortunate to be involved in the leadership for many years, having spent six years on the Council, eight years on the Board, and having recently served two terms as Vice President At Large (2010-2013).

SIAM faces a number of challenges that, if elected as President, I relish helping to address, working with SIAM members, SIAM officers, and the excellent SIAM staff.

SIAM’s publications remain strong, but are vulnerable to changes in the way scholarly journals operate (open access, article processing charges, etc.). SIAM needs to monitor the situation and respond appropriately, while striving to provide an even greater service to authors, referees and editors, for example by better use of web tools.

SIAM’s membership is also healthy, but SIAM must continue to enhance membership benefits and work hard to attract and retain student members, who are the future of the society, and to provide value for its members in industry.

Book sales are declining globally and in academic publishing it is becoming harder to find authors with the time to write a book. Nevertheless, the SIAM book program is in a strong position and the 2015 review of the program that I chaired has produced a list of recommendations that should help it to thrive.

SIAM conferences are a terrific place to learn about the latest developments in the subject, meet SIAM staff, browse SIAM books, and attend a business meeting. Attendances continue to grow (the SIAM CSE meeting in Salt Lake City last March was the largest ever SIAM meeting, with over 1700 attendees), but in any given year, the majority of SIAM’s 14,000 members do not attend a SIAM conference. Audio and slide captures of selected lectures are made available on SIAM Presents, but we need to do more to help members engage in virtual participation.

The SIAM web site has provided sterling service for a number of years, but is in need of a major redesign, which is underway. This is an excellent opportunity to integrate better the many services (conferences, journals, books, membership, activity groups, chapters, sections, etc.) in a responsive design. Beyond the core website, SIAM has a strong social media presence, posts a wide variety of videos on its YouTube channel, hosts SIAM Blogs (which I was involved in setting up in 2013), has recently made SIAM News available online, and has SIAM Connect and SIAM Unwrapped as further outlets. Optimizing the use of all these communication tools will be an ongoing effort.

These are just some of the challenges facing SIAM in the future as it continues to play a global leadership role for applied mathematics.

July 2015

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