# 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!

# 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.

# In Defense Of Computer Science

So why study computer science? The job prospects at the end are usually pretty good – because, if nothing else, you can become a pretty good software developer fairly quickly – but they are unknown. My argument is that the study of computer science is enough of an incentive enough to make it worthwhile.

# The programmers of tomorrow

A recent article written by Dr. Robert B.K. Dewar and Dr. Edmond Schonberg (both from AdaCore Inc.) is generating some discussion on the state of Computer Science (CS) education in the United States. In “Computer Science Education: Where Are the Software Engineers of Tomorrow?“, Dewar and Schonberg claim that U.S. universities are training unqualified and easily replaceable programmers.

“It is our view that Computer Science (CS) education is neglecting basic skills, in particular in the areas of programming and formal methods. We consider that the general adoption of Java as a first programming language is in part responsible for this decline. We examine briefly the set of programming skills that should be part of every software professional’s repertoire.”

The comment about Java’s adoption annoyed some Java aficionados, but in a recent interview, Robert Dewar adds that the problem goes far beyond the choice of Java as the first programming language. The real problem is that CS programs are being dumbed down, so that they become more accessible and popular. In result, they “are not rigorous enough and don’t promote in-depth thinking and problem solving”.

“A lot of it is, ‘Let’s make this all more fun.’ You know, ‘Math is not fun, let’s reduce math requirements. Algorithms are not fun, let’s get rid of them. Ewww – graphic libraries, they’re fun. Let’s have people mess with libraries. And [forget] all this business about ‘command line’ – we’ll have people use nice visual interfaces where they can point and click and do fancy graphic stuff and have fun.”

Although the paper is concerned with the American reality, I believe we have the same problem in Europe — at least, and as far as I know, in the UK and in Portugal. However, in my opinion, the problem starts before university. The maths’s programs in secondary schools are also being simplified (or dumbed down, if you prefer) and many important concepts, like logic and proofs, are being ignored.

In result, first-year students usually have a poor background on maths and problem solving. In fact, most of them have never seen a proof and don’t even understand the importance of mathematical reasoning. With poor reasoning abilities, they become intellectually less curious, accepting things as they are presented, and they have tremendous difficulties creating new algorithms, or convincing someone that their own algorithms are correct.

Moreover, once they are in the university, one of two things happens:

1. they are not taught explicitly how to solve problems or how to derive algorithms from their formal specifications (this is the most common case);
2. or they are taught the above skills but their poor background doesn’t allow them to fully appreciate these subjects.