# 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!

# 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.

# A square grid path problem

Last November I have solved Problem 15 of Project Euler (a counting problem involving paths in square grids), and, although the problem admits a simple solution, some of the solutions presented in their forums are very complicated. Thus, I thought it would be a good idea to present my solution, as I consider it very simple.

## Problem statement

Starting in the top left corner of a $2{\times}2$ grid, there are 6 routes (without backtracking) to the bottom right corner.

How many routes are there through a $20{\times}20$ grid?

## My solution

In order to make the problem more interesting, let us investigate the more general problem of counting the number of routes in an $n{\times}n$ grid. Our argument is based on three observations:

1. all the paths have size $2{\times}n$ (the reason is obvious: you have to go right $n$ positions and down another $n$ positions);
2. since we can only go right or down, we can identify every path by a string of Rs and Ds, where a R means going right and a D means going down; as an example, the paths illustrated in the problem statement are (from left to right and from top to bottom): RRDD, RDRD, RDDR, DRRD, DRDR and DDRR;
3. the strings mentioned above must contain the same number of Rs and Ds.

From these three observations, we can transform the problem to the following:

How many different strings of size $2{\times}n$, consisting of $n$ Rs and n Ds, are there?

The solution is now very simple, because the positioning of $n$ Ds (or Rs) determines the positioning of the other $n$ Rs (or Ds). Hence, the number we are interested in is the number of ways in which we can choose $n$ positions from $2{\times}n$ available positions. The answer, using the traditional notation for the binomial coefficient, is:

${2n \choose n} = \frac{(2n)!}{n! \times n!}~~~~.$

Instantiating n with 20, we get the answer to the initial problem of the $20{\times}20$ grid.

## Generalization to $m{\times}n$ grids

The generalization to an $m{\times}n$ grid is also simple. The only difference is that the strings have length $m+n$. Using the same reasoning as above, the number of paths through an ${m{\times}n}$ grid is:

${m+n \choose n} = \frac{(m+n)!}{m!\times n!}~~~~.$

Final note: If you want to access the forum of the problem, you have to solve it.

# How to be more confident about your own programs: an example using Perl

Programming is one of the most difficult branches of applied mathematics; the poorer mathematicians had better remain pure mathematicians.

It was Edsger W. Dijkstra, the famous computer scientist, who wrote these words in his note named “How do we tell truths that might hurt?“. I am sure that many people didn’t like to read them, and didn’t understand what he meant.

Although it is not my intention to discuss his words, I want to present a simple example that demonstrates how mathematics can be used to program better and to make you more confident about your own programs.

This post includes a fair amount of mathematical definitions and concepts, but they should not be difficult to understand. After the mathematical discussion, I present an example using the Perl programming language (it also applies to languages like C or Java).

## The Problem

The problem I am going to deal with, involves the ceiling and floor functions. If you don’t remember, the floor of a real number $x$ is (usually) written as $\lfloor{}x\rfloor$ and it is defined as the greatest integer that is at most $x$. Similarly, the ceiling of a real number $x$ is written as $\lceil{}x\rceil$ and it is defined as the smallest integer that is at least $x$.

The goal is to implement the ceiling function supposing that our programming language only provides the floor function to round numbers. Formally, given a real $x$, we want to calculate an expression $e$ such that:

$\lceil{}x\rceil = \lfloor{}e\rfloor ~~.$