Aims
The course aims to introduce the mathematics of discrete structures, showing it as an essential tool for computer science that can be clever and beautiful.
Lectures
- Proof [5 lectures].
Proofs in practice and mathematical jargon. Mathematical statements: implication, bi-implication, universal quantification, conjunction, existential quantification, disjunction, negation. Logical deduction: proof strategies and patterns, scratch work, logical equivalences. Proof by contradiction. Divisibility and congruences. Fermat’s Little Theorem.
- Numbers [5 lectures].
Number systems: natural numbers, integers, rationals, modular integers. The Division Theorem and Algorithm. Modular arithmetic. Sets: membership and comprehension. The greatest common divisor, and Euclid’s Algorithm and Theorem. The Extended Euclid’s Algorithm and multiplicative inverses in modular arithmetic. The Diffie-Hellman cryptographic method. Mathematical induction: Binomial Theorem, Pascal’s Triangle, Fundamental Theorem of Arithmetic, Euclid’s infinity of primes.
- Sets [9 lectures].
Extensionality Axiom: subsets and supersets. Separation Principle: Russell’s Paradox, the empty set. Powerset Axiom: the powerset Boolean algebra, Venn and Hasse diagrams. Pairing Axiom: singletons, ordered pairs, products. Union axiom: big unions, big intersections, disjoint unions. Relations: composition, matrices, directed graphs, preorders and partial orders. Partial and (total) functions. Bijections: sections and retractions. Equivalence relations and set partitions. Calculus of bijections: characteristic (or indicator) functions. Finite cardinality and counting. Infinity axiom. Surjections. Enumerable and countable sets. Axiom of choice. Injections. Images: direct and inverse images. Replacement Axiom: set-indexed constructions. Set cardinality: Cantor-Schoeder-Bernstein Theorem, unbounded cardinality, diagonalisation, fixed-points. Foundation Axiom.
- Formal languages and automata [5 lectures].
Introduction to inductive definitions using rules and proof by rule induction. Abstract syntax trees. Regular expressions and their algebra. Finite automata and regular languages: Kleene’s theorem and the Pumping Lemma.
Objectives
On completing the course, students should be able to
- prove and disprove mathematical statements using a variety of techniques;
- apply the mathematical principle of induction;
- know the basics of modular arithmetic and appreciate its role in cryptography;
- understand and use the language of set theory in applications to computer science;
- define sets inductively using rules and prove properties about them;
- convert between regular expressions and finite automata;
- use the Pumping Lemma to prove that a language is not regular.
Recommended reading
Biggs, N.L. (2002). Discrete mathematics. Oxford University Press (Second Edition).
Davenport, H. (2008). The higher arithmetic: an introduction to the theory of numbers. Cambridge University Press.
Hammack, R. (2013). Book of proof. Privately published (Second edition). Available at:
http://www.people.vcu.edu/ rhammack/BookOfProof/index.html
Houston, K. (2009). How to think like a mathematician: a companion to undergraduate mathematics. Cambridge University Press.
Kozen, D.C. (1997). Automata and computability. Springer.
Lehman, E.; Leighton, F.T.; Meyer, A.R. (2014). Mathematics for computer science. Available on-line.
Velleman, D.J. (2006). How to prove it: a structured approach. Cambridge University Press (Second Edition).
