Multiples in the Fibonacci series

I found the following problem on K. Rustan M. Leino’s puzzles page:

[Carroll Morgan told me this puzzle.]

Prove that for any positive K, every Kth number in the Fibonacci sequence is a multiple of the Kth number in the Fibonacci sequence.

More formally, for any natural number n, let F(n) denote Fibonacci number n. That is, F(0) = 0, F(1) = 1, and F(n+2) = F(n+1) + F(n). Prove that for any positive K and natural n, F(n*K) is a multiple of F(K).

This problem caught my attention, because it looks like a good example for using a result that I have derived last year. My result gives a reasonable sufficient condition for showing that a function distributes over the greatest common divisor and shows that the Fibonacci function satisfies the condition.

In fact, using the property that the Fibonacci function distributes over the greatest common divisor, we can solve this problem very easily. Using $\fapp{fib}{n}$ to denote the Fibonacci number $n$, $m{\nabla}n$ to denote the greatest common divisor of $m$ and $n$, and $\setminus$ to denote the division relation, a possible proof is:

\[
\beginproof
\pexp{\text{$\fapp{fib}{(n{\times}k)}$ is a multiple of $\fapp{fib}{k}$}}
\hint{=}{definition}
\pexp{\fapp{fib}{k} \setminus \fapp{fib}{(n{\times}k)}}
\hint{=}{rewrite in terms of $\nabla$}
\pexp{\fapp{fib}{k} ~\nabla~ \fapp{fib}{(n{\times}k)} ~=~ \fapp{fib}{k}}
\hint{=}{$fib$ distributes over $\nabla$}
\pexp{\fapp{fib}{(k{\nabla}(n{\times}k))} = \fapp{fib}{k}}
\hint{=}{$k{\nabla}(n{\times}k) = k$}
\pexp{\fapp{fib}{k} = \fapp{fib}{k}}
\hint{=}{reflexivity}
\pexp{true~~.}
\endproof
\]

The crucial step is clearly the one where we apply the distributivity property. Distributivity properties are very important, because they allow us to rewrite expressions in a way that prioritizes the function that has the most relevant properties. In the example above we could not simplify $\fapp{fib}{k}$ nor $\fapp{fib}{(n{\times}k)}$, but applying the distributivity property prioritised the $\nabla$ operator — and we know how to simplify $k{\nabla}(n{\times}k)$. Furthermore, in practice, distributivity properties reduce to simple syntactic manipulations, thus reducing the introduction of error and simplifying the verification of our arguments.

(Now that I think about it, perhaps it would be a good idea to write a note on distributivity properties, summarizing their importance and their relation with symbol dynamics.)

If you have any corrections, questions, or alternative proofs, please leave a comment!

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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.

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On Programming and Mathematical Methodology — Part II

In the last post I have presented some historical context about programming and mathematical methodology. If you read it, then you should have an idea when and why programmers started to investigate on mathematical methodology. However, I haven’t mentioned any aspects of mathematical methodology that can help us to improve our programming or mathematical skills.

In this post, I’ll talk about mathematical proofs. And what’s the relevance of this topic to programmers? Well, computer programs are mathematical formulae, with a precise formal meaning and embodying constructive theorems about the systems they implement (as well-written in “Mathematics and Programming – A Revolution in the Art of Effective Reasoning”, by Roland Backhouse). The difference between theorems embodied by computer programs and the ones usually studied in mathematics is that they are applied by an unforgiving machine, with the effect that the smallest error can cause a huge damage. This means that computer programmers must create trustworth designs, i.e., the constructive theorems embodied by their programs must be programmed correctly.

Mathematical Proofs

Mathematicians job is to do mathematics, i.e., to design and present theorems, arguments, algorithms and in some cases whole theories. However, the traditional mathematical curriculum is more concerned with teaching mathematical facts — existing theories and concepts — than with the doing of mathematics. And even when design and presentation get some attention, they are treated separately: design of solutions is viewed as a psychological issue, while presentation is viewed as a matter of personal style (words from this Dijkstra’s note on Mathematical Methodology).
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On Programming and Mathematical Methodology

First of all, welcome to my new blog. Being this my first post, I will present myself, give you some background on what I am doing and explain what are my intentions about this blog. My name is João Fernando Ferreira and I am a research student at the Foundations of Programming research group at the University of Nottingham (visit the About page to find out more).

My research is on algorithmic problem solving and its main goal is to develop calculational problem-solving techniques resulting in educational material supporting the use of a calculational approach to algorithmic problem solving. The focus of the project will be on the dynamics of problem solving – the processes of mathematical modelling and effective calculation in the formulation of concise and precise algorithmic methods.

As any other researcher, I spend most of my time reading and writing. That is one of the reasons I have created this blog: to organise my written notes. Using a blog system brings me some advantages like:

  • I can tag my notes and use the system search capabilities to find them;
  • I can access and change them from any place in the world, as long as I have Internet access;
  • I can add new content from any place in the world, as well;
  • I can share my notes and get feedback from my colleagues, supervisors, friends or anyone who is just interested in the same topics.

Other important reason to share my notes and thoughts is that I think they may be of interest for programmers. Since this is my first post, and (probably) most of you don’t really understand what I am doing, I will start by presenting some historical facts (mixed with personal opinions) and motivations. It is my hope that these facts will help you understand the relation and importance of mathematical methodology to programming. Please note that to read the entire post, you may need to click the “Read more” link.

Historical context

In the 1960s, programmers started recognising that there were serious problems in the programming field and that it was necessary to prove the correctness of programs. At the time, software engineering was facing a software crisis and programming was not very well understood. Many software projects ran over budget and schedule and some of them even caused property damage and loss of life (see the RISKS-FORUM Digest for some examples).

To solve these problems, computer scientists focused on programming methodology and on ways to build programs in a systematic way. A common consensus was that programs should be proved correct, and in the late 1960s, some important articles had an important impact on the field. In 1968, Edsger W. Dijkstra published an article on the harmfulness of the Go To statement, where he claims that its use makes it impossible to determine the progress of a program. Also, one year later, Tony Hoare published a seminal article where he introduces the Hoare triples and an axiomatic approach to language definition.
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