# Computer Laboratory

Course pages 2012–13

Subsections

## Computation Theory

Lecturer: Professor A.M. Pitts

No. of lectures: 12

Suggested hours of supervisions: 3

Prerequisite course: Discrete Mathematics

This course is a prerequisite for Complexity Theory (Part IB), Quantum Computing (Part II).

### Aims

The aim of this course is to introduce several apparently different formalisations of the informal notion of algorithm; to show that they are equivalent; and to use them to demonstrate that there are uncomputable functions and algorithmically undecidable problems.

### Lectures

• Introduction: algorithmically undecidable problems. Decision problems. The informal notion of algorithm, or effective procedure. Examples of algorithmically undecidable problems. [1 lecture]

• Register machines. Definition and examples; graphical notation. Register machine computable functions. Doing arithmetic with register machines. [1 lecture]

• Universal register machine. Natural number encoding of pairs and lists. Coding register machine programs as numbers. Specification and implementation of a universal register machine. [2 lectures]

• Undecidability of the halting problem. Statement and proof. Example of an uncomputable partial function. Decidable sets of numbers; examples of undecidable sets of numbers. [1 lecture]

• Turing machines. Informal description. Definition and examples. Turing computable functions. Equivalence of register machine computability and Turing computability. The Church-Turing Thesis. [2 lectures]

• Primitive and partial recursive functions. Definition and examples. Existence of a recursive, but not primitive recursive function. A partial function is partial recursive if and only if it is computable. [2 lectures]

• lambda-Calculus. Alpha and beta conversion. Normalization. Encoding data. Writing recursive functions in the lambda-calculus. The relationship between computable functions and lambda-definable functions. [3 lectures]

### Objectives

At the end of the course students should

• be familiar with the register machine, Turing machine and lambda-calculus models of computability;

• understand the notion of coding programs as data, and of a universal machine;

• be able to use diagonalisation to prove the undecidability of the Halting Problem;

• understand the mathematical notion of partial recursive function and its relationship to computability.