# Principles and Applications of Algorithmic Problem Solving

I am currently in Salamanca (Spain), attending the conference Tools for Teaching Logic III. My talk was on teaching logic through algorithmic problem solving and it went quite well, I think. In particular, it seems that the audience enjoyed the examples that I have used and the teaching scenarios that I have shown. As a result, I have promised that I would upload my PhD thesis into this website. Since the thesis can also be useful for other people, I have decided to write a new blog post. I hope you enjoy!

## Abstract

Algorithmic problem solving provides a radically new way of approaching and solving problems in general by using the advances that have been made in the basic principles of correct-by-construction algorithm design. The aim of this thesis is to provide educational material that shows how these advances can be used to support the teaching of mathematics and computing.

We rewrite material on elementary number theory and we show how the focus on the algorithmic content of the theory allows the systematisation of existing proofs and, more importantly, the construction of new knowledge in a practical and elegant way. For example, based on Euclid’s algorithm, we derive a new and efficient algorithm to enumerate the positive rational numbers in two different ways, and we develop a new and constructive proof of the two-squares theorem.

Because the teaching of any subject can only be effective if the teacher has access to abundant and sufficiently varied educational material, we also include a catalogue of teaching scenarios. Teaching scenarios are fully worked out solutions to algorithmic problems together with detailed guidelines on the principles captured by the problem, how the problem is tackled, and how it is solved. Most of the scenarios have a recreational flavour and are designed to promote self-discovery by the students.

Based on the material developed, we are convinced that goal-oriented, calculational algorithmic skills can be used to enrich and reinvigorate the teaching of mathematics and computing.

Principles and Applications of Algorithmic Problem Solving (PhD Thesis, João F. Ferreira, 345 pages)

### Related Posts:

Suppose you write a program and you send the source code to two of your friends, ${\cal A}$ and ${\cal B}$. Your two friends read the code and when they finish, $A$ errors are detected by ${\cal A}$, $B$ errors are detected by ${\cal B}$, and $C$ errors are detected by both. So, in total, $A+B-C$ errors are detected and can now be eliminated. We wish to estimate the number of errors that remain unnoticed and uncorrected.

The original version of this problem concerns manuscripts and proofreaders, instead of source code and programmers. It was posed and solved by George Polya and published in 1976 on The American Mathematical Monthly under the name of Probabilities in Proofreading. Because the problem is interesting and Polya’s solution is short and elegant, I have decided to record and share it. Also, since code sharing and reading is a frequent activity in the software development world, estimating the desired value can be helpful for some readers of this blog.

## Estimating the number of unnoticed errors

Let $E$ be the number of all errors, noticed and unnoticed, in the source code. Our goal is to estimate the value of $E-(A+B-C)$. Let $p$ be the probability that friend ${\cal A}$ notices any given error and $q$ the analogous probability for friend ${\cal B}$. The expected number of errors that may be detected by ${\cal A}$ is $E{\cdot}p$ and by ${\cal B}$ is $E{\cdot}q$. Assuming that these probabilities are independent, the expected number of errors that may be mutually detected by both friends is $E{\cdot}p{\cdot}q$.

Because we are interested in an estimate, we can safely assume that the expected numbers are approximately equal to the number of errors detected, that is, $E{\cdot}p \sim A$, $E{\cdot}q \sim B$, and $E{\cdot}p{\cdot}q \sim C$. (We use the notation $\sim$ to denote that two numbers are approximately equal.)

We now have all the ingredients to conclude the solution. Recall that our goal is to estimate the value of $E-(A+B-C)$. We calculate:

$\beginproof \pexp{E-(A+B-C)} \hint{=}{we rewrite E to prepare the introduction of A, B, and C} \pexp{\frac{E{\cdot}p{\cdot}E{\cdot}q}{E{\cdot}p{\cdot}q} - (A+B-C)} \hint{\sim}{assumption on estimates} \pexp{\frac{A{\cdot}B}{C} - (A+B-C)} \hint{=}{arithmetic} \pexp{\frac{(A-C){\cdot}(B-C)}{C}~~.} \endproof$

This is the desired estimate!

# Making ThinkFinger Faster

Some time ago, I have installed and configured ThinkFinger, a driver for the fingerprint-readers found in most IBM/Lenovo Thinkpads, including my Lenovo X61 Tablet. Although it worked well, it was very slow! I had to wait 5 to 10 seconds for the prompt to show, then a bit more before I could swipe my finger or write my password, and then a bit more for the password to be accepted.

To make my world a better place to live, I have downloaded the development version of the driver and tried to figure out why it was being so slow. It turns out that in the library that is used to communicate with the fingerprint-reader, there was a call to the function usb_bulk_read that was taking too long. I have tried to find some official documentation about this function, but all I could find was how not to document a function (seriously, what is the role of the parameter timeout? and is it measured in seconds? milliseconds?).

Anyway, I have changed the value of the USB_TIMEOUT constant from 5000 to 5 and the fingerprint-reader is much, much better! In detail, here is the patch:

Index: libthinkfinger/libthinkfinger.c
===================================================================
--- libthinkfinger/libthinkfinger.c     (revision 118)
+++ libthinkfinger/libthinkfinger.c     (working copy)
@@ -35,7 +35,7 @@

#define USB_VENDOR_ID     0x0483
#define USB_PRODUCT_ID    0x2016
-#define USB_TIMEOUT       5000
+#define USB_TIMEOUT       5
#define USB_WR_EP         0x02
#define USB_RD_EP         0x81
#define DEFAULT_BULK_SIZE 0x40


I am not sure why this works and what are the implications, but all the tests I did were satisfactory. If you know more about this problem, please let me know! I would be more than happy to understand why the USB_TIMEOUT constant seems to be acting as a time-wait delay, rather than as a timeout in the following call (line 324, libthinkfinger/libthinkfinger.c):

usb_retval = usb_bulk_read (tf->usb_dev_handle, USB_RD_EP, bytes,
size, USB_TIMEOUT);


(I think it is strange that I haven’t found anyone complaining about the same problem. Nevertheless, I’ve decided to publish this post anyway, in case someone runs into the same problem.)

Update (2009/07/31): I’ve upload a patched SRC RPM: thinkfinger-0.3-9.jff.src.rpm. Please note that I have added the patch to Fedora’s SRC RPM.

# Sir Tony Hoare on the future of Computing Science

Sir Tony Hoare on the future of Computing Science (CACM, March 2009):

I expect the future to be as wonderful as the past has been. There’s still an enormous amount of interesting work to do. As far as the fundamental science is concerned, we still certainly do not know how to prove programs correct. We need a lot of steady progress in this area, which one can foresee, and a lot of breakthroughs where people suddenly find there’s a simple way to do something that everybody hitherto has thought to be far too difficult.

# A Calculational Proof of the Handshaking Lemma

UPDATE (2011/09/20): This post was superseded by An improved proof of the handshaking lemma.

In graph theory, the degree of a vertex $A$, $\fapp{d}{A}$, is the number of edges incident with the vertex $A$, counting loops twice. So, considering Graph 0 below, we have $\fapp{d}{A}=3$, $\fapp{d}{B}=3$, $\fapp{d}{C}=1$, $\fapp{d}{D}=3$, and $\fapp{d}{E}=2$.

Graph 0: Example of an undirected graph with five nodes

A well-known property is that every undirected graph contains an even number of vertices with odd degree. The result first appeared in Euler’s 1736 paper on the Seven Bridges of Königsberg and is also known as the handshaking lemma (that’s because another way of formulating the property is that the number of people that have shaken hands an odd number of times is even).

As we can easily verify, Graph 0 satisfies this property. There are four vertices with odd degree ($A$,$B$, $C$, and $D$), and 4, of course, is an even number.

Although the proof of this property is simple, I have never seen it proved in a calculational and goal-oriented way. My aim with this post is to show you a development of a goal-oriented proof.