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Department of Computer Science and Technology

Undergraduate

Course pages 2022–23 (working draft)

Computation Theory

Principal lecturer: Prof Andrew Pitts
Taken by: Part IB CST
Hours: 12
Format: In-person lectures
Suggested hours of supervisions: 3
Prerequisites: Discrete Mathematics
This course is a prerequisite for: Complexity Theory, Quantum Computing, Types
Past exam questions

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.

Recommended reading

* Hopcroft, J.E., Motwani, R. and Ullman, J.D. (2001). Introduction to automata theory, languages, and computation. Addison-Wesley (2nd ed.).
* Hindley, J.R. and Seldin, J.P. (2008). Lambda-calculus and combinators, an introduction. Cambridge University Press (2nd ed.).
Cutland, N.J. (1980). Computability: an introduction to recursive function theory. Cambridge University Press.
Davis, M.D., Sigal, R. and Weyuker, E.J. (1994). Computability, complexity and languages. Academic Press (2nd ed.).
Sudkamp, T.A. (2005). Languages and machines. Addison-Wesley (3rd ed.).